Abstract:Body weight is an important indicator for reflecting the health and growth conditions, reproduction and production performance of livestock and poultry. Accurate and rapid assessment and monitoring of livestock and poultry body weight is a critical way to improve the level of breeding management and achieve precision livestock farming. The traditional weighing method is time-consuming and laborious, and easy to cause stress response on animals. Weight assessment based on machine vision technology, which can establish an intelligent assessment model between body weight and body shape characteristics by using visual detection technology, is a hotspot of intelligent technology research in livestock and poultry breeding at present. Firstly, the methods of weight assessment were categorically described. Then, the sensor types, methods and applications of animal and poultry body feature treatment were analyzed in detail. The comparative analysis of the research on body size, physical signs and weight assessment model based on machine learning method were focused on. The application effect and the latest research results of various machine learning algorithms in weight assessment were presented. The development potential of deep learning algorithm in the field of automatic weight assessment of livestock and poultry was discussed and analyzed. Finally, the problems and challenges of weight assessment researches on livestock and poultry and the development trend of the future work were pointed out, which can provide some references for the scholars and engineers in the field of the modern intelligent weight assessment for livestock and poultry.

Abstract:In order to solve problems of poor ridging effect and high traction resistance of existing sweet potato ridging shaping machine, a ploughshare furrowing ridging device of sweet potato ridging shaping machine was designed and its main structure and working principle were explained. According to the sweet potato planting pattern and ridging structure agronomic requirements, the structure parameters and value range of the plow body surface of the furrowing ridging device were determined by the horizontal straight element line method. The EDEM discrete element simulation software was used to establish interaction model of the ploughshare furrowing ridging device and soil. The installation angle, bulldozing angle and element line angle difference of the furrowing plow body surface were selected as the experiment factors, and the evaluation indexes were the soil throwing distance and traction resistance of the plow body, the Box-Behnken central composite design experiment was carried out. The simulation experiment results showed that the optimal combinations of the installation angle, bulldozing angle and element line angle difference were 27.19°, 38.05° and 10.69° when the operating speed of the sweet potato ridging shaping machine was 3.33km/h. Field experiment were carried out based on the optimal combination parameters. The ploughshare furrowing ridging device experiment results showed that the ridge height stability coefficient was 98.53%, the ridge soil firmness was 236 kPa, the fuel consumption of tractor operating was 11.94 L/h, which can meet the sweet potato furrowing ridging agronomic requirements. The operating effect of the ploughshare furrowing ridging device was better than that of the now existing plate type ridging device.

Abstract:Based on the objective reality of the sticky soil and large amount of straw in the rice-oil rotation area of the mid-lower Yangtze River, the traditional rotary tillage method for rapeseed direct seeding often leads to practical problems such as shallow plough layer, low stubble burying rate and low flatness. Combined with the agronomic requirements for the preparation of rapeseed seedbeds, a shovel-type seedbed preparation machine suitable for rapeseed direct seeding that realized the functions of soil plowing, soil crushing, straw burial covering, and leveling was designed, which cut and threw soil through active shovel, and integrated passive trenching, soil crushing, and flat surface. According to the requirements of the shovel entry angle, tillage depth, soil cutting pitch, etc., a kinematic model of the crank connecting rod mechanism was established. Based on the Matlab software analysis, the structure parameters of the crank connecting rod mechanism were obtained. And according to the requirements of shovel tip movement trajectory, cutting pitch, ditch bottom protrusion height, etc., the left and right staggered structure of the shovel and the crank spiral arrangement were determined. And the matching design of the unit operating parameters was carried out, the speed ( vm ) was 0.4~0.5m/s, the crank rotation speed (n) was 240r/min. The analysis of the movement process of the soil after being thrown by the shovel was carried out, the installation parameters of the enclosure were determined. A simulation model for the interaction of tillage components-soil-straw based on the discrete element method was established. The EDEM and ADAMS software were used to analyze the straw burial performance of the machine, and the simulation results showed that the average straw burial rate was 91.64%, which can realize the deep burying of straw and returning it to the field. Field experiments showed that under the condition of high stubble rice straw, the average working depth of the shovel-type seedbed preparation machine was 215.3mm, which was 99.2 mm higher than that of the traditional rotary tillage method; the stubble burying rate was 89.43%, which was an increase of 27.61 percentage points compared with the traditional rotary tillage method. The operation effect of the whole machine met the requirements for the preparation of the rapeseed direct seeding bed in the rice stubble field.

Abstract:Aiming at the problems that there is a lot of uncertain disturbance in the nonlinear vehicle model of agricultural machinery, and the measurement is often with noise, the moving horizon estimation (MHE) method for vehicle motion state was proposed. The state estimation problem was transformed into a fixed time domain optimization problem and the constraint conditions were fully considered. In order to improve the computational efficiency of MHE, taking into account the different sampling frequencies of sensors and the possibility of missing or abnormal measurement values, a multi-threading architecture was designed. The multi-threading architecture also can make MHE more suitable for practical applications. The automatic driving simulation system of the rice seeding machine was established by Matlab. The simulation results showed that MHE can effectively suppress system disturbance and measurement noise. The x and y positions and heading angle estimated by MHE were closer to the truth value than those estimated by extended Kalman filter (EKF). MHE was used to estimate the lateral deviation and heading angle deviation measured during the autonomous driving process of the rice seeding machine. The results showed that when the time domain window N was 3~5, the MHE algorithm had a good effect on suppressing the jump of measurement value, and it can also reflect the real trend of state value. It proved that MHE had excellent performance in suppressing system disturbance and measurement noise.

Abstract:Aiming at the problems of poor seed filling effect of mechanical seed metering device in high-speed operation and low qualification index of seed metering device caused by unstable seed delivery process, based on the design idea of guiding seed filling and shortening seed delivery distance, a side-guided soybean high-speed precision seed metering device was researched and designed, which realized orderly seed filling, stable and reliable seed delivery by using side-shaped hole structure and guide plate. The process of seed filling, seed cleaning and seed delivery was analyzed theoretically, and the filling force model was established. The key parameters affecting the performance of seed metering device were determined as follows: seed filling inclination angle α, opening angle β; the main structural parameters of the shaped hole were determined, and the qualified index and the missed sowing index were used as evaluation indexes. The simulation test of the seed filling inclination angle α and the opening angle β was carried out by using the combination test method of two factors and five levels of center rotation. The results showed that the tangential filling force reached the maximum value and then decreased with the increase of position angle, while the zfilling force was gradually increased to the maximum value. Seed-filling angle α and opening angle βhad significant effects on qualified index and missed sowing index. The optimal combination of structural parameters obtained by optimization was that the seedfilling angle α was 11.50° and the opening angle β was 119.05°. The bench test results showed that at operating speed of 4~14km/h, the qualified index of the sideguided soybean seed metering device was no less than 95.27%, the missed seeding index was no more than 4.73%, and the damage index was lower than 0.53%, which met the requirements of high-speed precision seeding.

Abstract:In view of the disadvantages of complex transmission system and obvious difference in seedling emergence effect of each row when single metering device is used in precision compound seeder for Pakchoi, a positive and negative pressure combination metering device for Pakchoi with two plates which can simultaneously sow eight rows was designed. The working process and principle of the metering device and the main structural parameters of the metering device were described. The structural parameters of seed tray and seed metering port and the seed migration trajectory were determined by theoretical analysis. By using DEM-CFD gas-solid coupling dynamic grid model, the effects of seed metering disc speed, air chamber negative pressure and air chamber positive pressure on seed metering performance were analyzed. The results showed that negative pressure had a significant impact on seed metering device qualified index, multiple index and missing index. When the seed metering disc speed was 30r/min, the negative pressure was -3000Pa, the positive pressure was 300Pa, the average qualified index of each row was 93.12%, the average multiple index was 3.59%, and the average missing seeding index was 3.29%. The bench test was carried out by using JPS-12 seed metering device detection test-bed. The results showed that when the rotation speed of seed metering disc was 30r/min, the negative pressure was -3000Pa, the positive pressure was 300Pa, the average qualified index of each row was 91.32%, the average multiple index was 6.19%, and the missing seeding index was 2.49%. The field experiment was carried out with a better combination of factors. The results showed that the average number of seedlings of Pakchoi was 10 in each meter, the average plant spacing was 100.48mm, and the uniformity coefficient of variation of the number of seedlings in each row was 8.05%, which met the agronomic requirements of Pakchoi planting, and the research result can provide a reference for the optimization of the structure of precision metering device.

Abstract:As one of the important parts in the intelligent construction of the whole rapeseed industrial chain, the unmanned seeding operation of rapeseed can greatly improve the operation accuracy and labor production efficiency, and achieve the effect of reducing costs and improving efficiency. The middle and lower reaches of the Yangtze River are the main producing areas of winter rapeseed in China. In the process of unmanned seeding operation, linear navigation is the main method, and field U-turn and line-feed play a key linking role, and the U-turn path planning and controlling effect directly affect the line-to-ine accuracy. Affected by the land and previous crops in Southern China, especially in the rice-oil rotation area in the middle and lower reaches of the Yangtze River, the land scale is small and the soil is sticky. The unmanned planting operation process is complicated at the head of the field, and the U-turn is easy to slip when making the U-turn, and it is easy to produce deviation after changing the line. The tractor start the next row of seeding operations before it goes on the working line, resulting in poor row alignment accuracy and the phenomenon of replay and missed seeding, which directly affects the quality of rapeseed seeding operations. Aiming at the problems of complex line-feeding and poor accurate line-to-line ability of tractor navigation during unmanned seeding operation, a two-back and three-cut fishtail U-turn model was proposed to find the line. Analyzing the characteristics of unmanned seeding operation and equipment characteristics of the rapeseed direct seeding unit, and taking quantitative analysis in the line-hunting path at the U-turn at the field head, a set of rapeseeds unmanned seeding operation system was constructed, the main parts included CaseTM1404 tractor, Beidou high-precision positioning system, angle sensor, vehicle controller, CAN analyzer, industrial computer, electronically controlled steering wheel and rapeseed precision seeder. The simulation test of three kinds of fishtail turning models was carried out. The results showed that the area of the non operating area of the two-back and three-cut fishtail U-turn model was reduced by 14.62%~22.43% compared with the traditional fishtail U-turn model. The field unmanned seeding operation experiment of the model controller was carried out, and the results showed that the initial offset distance and rising time of the two-back and three-cut fishtail U-turn model was reduced by 7.37~8.08cm and 1.3~2.3s compared with that of the traditional fishtail U-turn model, and the operation alignment accuracy was 1.48cm. This research result had guiding value for the comb-path U-turn operation during unmanned seeding operation of rapeseed.

Abstract:Fertilizer is the key factor for increasing rice yield, and solves accumulation blockage, difficulty in regulating fertilizer amount and poor uniformity of fertilization in the existing fertilization machinery, and a side deep fertilizing device which was easy to adjust the amount of fertilizer and prevent blockage was designed, referring to the agronomic requirements of side deep fertilization in paddy field and structure of transplanter, and the basic structural parameters of the device were determined. Through theoretical analysis, the working process of the device was explored, with simulation software, the effects of fertilizer discharge disc speed and fertilizer tank height on fertilizer filling and discharging were investigated, the optimum operating speed range of fertilizer discharge disc was 10~50r/min. Through bench test, the effects of fertilizer discharge disc speed and fertilizer tank height on fertilization stability and the effects of fertilizer discharge disc speed and transplanter speed on fertilization uniformity were obtained, the fertilization results met the national standards, and the fertilization uniformity was improved to a certain extent compared with the fertilization machinery on the market. With field test operation, the operation effect met the agronomic requirements. The research designed a kind of fertilizing device which was easy to regulate the fertilizer amount, antiblocking device with uniform fertilization, provided a reference for the operation of the side deep fertilizing device for paddy field.

Abstract:Combining the characteristics of Cyperus esculentus-seeds and the requirements of planting in Huang-Huai-Hai Plain, in order to solve the problems of poor fluidity, poor filling-seed performance and three seeds per hole scattered and falling due to the uneven surface and irregular shape of the Cyperus esculentus-seeds, a Cyperus esculentus cell-wheel seed-metering device with low-position seeding and cavitation function and V-shaped groove was designed. Through the design of the diameter and the type hole of the metering wheel and the additional V-shaped groove on its surface, the precision separation, and filling-seed performance was improved; a low-position seeding and cavitation device was added under the metering wheel, which can be used to lower the height of seeding and it can also gather the scattered and falling seeds in the middle, which improved the effect of cavitation. The movement characteristics of the seed metering device were simulated through the EDEM, the influence of different structural parameters on the filling-seed performance was analyzed, and the structural parameters of the metering wheel were determined. The speed of the metering wheel, the height of the seed layer and the width of the type hole were taken as the test factors, and the three factors of the seeding qualified index, missing index, and multiple index were used as test indicators, and the quadratic rotation orthogonal combination test was carried out. The simulation test results showed that the primary and secondary order that affected the qualified index of the number of seeds was the speed of the metering wheel, the width of the type hole, and the height of the seed layer; when the speed of the metering wheel was 22.10r/min, the width of the type hole was 14.23mm and the height of the seed layer was 52.59mm, the qualified index was 92.11%, the missing index was 2.24%, and the multiple index was 5.65%. Finally, a bench test was carried out to verify the simulation results, and it was concluded that the Cyperus esculentus metering device with low-position seeding and cavitation function had better filling seed and cavitation performance, which met the requirements of precise seeding of Cyperus esculentus.

Abstract:Aiming at the problems of high humidity, poor fluidity, and difficulty in applying powder organic fertilizer, a powder organic fertilizer discharging device was designed. The device was mainly composed of a fertilizer box, a fertilizer discharging wheel, an anti-free-flow baffle and a fertilizer discharging shaft. In order to improve the applicability of the fertilizer distributor, the powder organic fertilizer with different moisture content ((28±1)%, (32±1)%, (36±1)%) was used as the research object to design the fertilizer distributor. The mechanical analysis of the organic fertilizer during the driving process of the fertilizer discharging wheel was carried out, and the wheel was designed into a cycloidal shape. In order to prevent the organic fertilizer from passing through the fertilizer outlet directly on the bottom of the fertilizer tank to produce self-flow phenomenon, and break the agglomerated organic fertilizer, an anti-free-flow baffle was designed. Taking the number of fertilizer discharging finger and the width of the fertilizer outlet as the experimental factors, and coefficient of variation of fertilization stability as the performance index, the quadratic orthogonal rotation combination experiment was carried out to establish the discrete element simulation model of the fertilizer distribution. The fertilization stability was the best when the number of fertilizer discharging finger was 6 and the width of the fertilizer outlet was 36.36mm. The performance test of the designed fertilizer distribution was carried out with the cutting down rate, coefficient of variation of fertilization consistency of each row, coefficient of variation of fertilization stability and coefficient of variation of fertilization uniformity as the evaluation index. It was showed that when the designed fertilizer distribution applied the organic fertilizer with different moisture contents at a speed of 5~8km/h, each performance index was within the specified range, and the working performance was stable, which can meet the technical requirements.

Abstract:Aiming at the problems of easy injury to tree roots, low fertilizer utilization rate and uneven fertilization in orchard liquid fertilizer application, an air flow guided orchard fertilizer injection machine was developed to realize the mechanized application of orchard liquid fertilizer. A nested fertilizer injection device was designed for the air guided fertilizer injection method. The influence of the outlet hole parameters of the fertilizer injection device on the ventilation and fertilizer injection effect was studied by using Fluent fluid simulation software. It was determined that the number of vent holes and fertilizer injection holes was 4 and the diameter was 2mm. According to the actual operation of the fertilizer injection machine, the fertilizer injection gas-liquid pipeline and fertilizer injection down pressure device of the fertilizer injection machine were designed and analyzed. It was determined that the volume of the gas tank was 80L, the stroke of hydraulic cylinder was 450mm, the cylinder diameter was 40mm and the rod diameter was 28mm, which met the operation requirements. Through the interaction test of air flow guidance parameters and fertilizer injection parameters, the influence law of air flow guidance pressure and liquid fertilizer injection pressure on liquid fertilizer diffusion was explored. When the ventilation pressure was 0.8MPa and the fertilizer injection pressure was 1.5MPa, it was the optimal parameter combination. Based on virtual prototype technology, the modeling of air guided orchard fertilizer injection machine was completed, and the prototype was trial produced. Field tests were carried out in Yancheng, Jiangsu Province and Pinggu, Beijing. The field test results showed that the fertilizer injection machine had stable operation effect, and there was no liquid fertilizer overflow. The longitudinal diffusion range of liquid fertilizer was 264~320mm, the transverse diffusion range was 250~270mm, the operation efficiency was 0.10~0.12hm2/h, which met the operation requirements of liquid fertilizer in orchards.

Abstract:Precision fertilization is a high-efficiency fertilization method, and the fertilizer ejector is a key link in precision fertilization. In order to realize the precise fertilizer discharge of the fertilizer ejector, the fertilizer discharge performance of the arc-groove double-screw fertilizer discharge device based on the principle of dislocation superposition single screw fertilizer discharge curve was analyzed, the factors affecting its fertilizer discharge performance were obtained, and the factors affecting the fertilizer discharge uniformity of the arc-groove double-spiral fertilizer discharge device were obtained, taking the pitch S, arc-groove radius Rp and center distance a as the test factors, and taking the uniformity variation coefficient and fertilization accuracy as the test indexes, the three-factor and three-level Box-Behnken test was carried out. The optimal parameters were pitch S=35mm, arc-groove radius Rp=17.5mm and center distance a=35mm. The arc-groove double-spiral fertilizer discharge device was made under the optimal parameter combination, and the bench verification test and comparative test were carried out. The test results showed that the uniformity variation coefficient of bench test and the relative error between fertilization accuracy and simulation test were 5.07% and 4.69%, respectively, and there was little difference between them, which verified the correctness of simulation. The uniformity variation coefficient of the optimized arc-groove double-spiral fertilizer discharge device was 7.26 and 15.48 percentage points lower than that of the non optimized arc-groove double-spiral fertilizer discharge device and single spiral fertilizer discharge device respectively. The optimized arc-groove double-spiral fertilizer discharge device had good fertilizer uniformity and effectively solved the problem of uneven fertilizer discharge of the single helix fertilizer ejector, it can provide a reference for the structural improvement and optimization of arc-groove double spiral fertilizer ejector.

Abstract:Aiming at the problem that the existing measuring device for anti-collapse characteristic of corn plants is unstable and not perpendicular to the plant, which results in large error of measuring results, a measuring device for anti-collapse characteristic of corn plants driven by motor was designed. The device can quickly measure the maximum moment that the corn stalk can withstand from erect to different angles of lodging. Based on the analysis of the stress on lodging of maize, the plant mechanical model was established and the index of anti-collapse characteristic was put forward. The test results showed that the deviation between the detection angle of the device and the actual angle was 1°, which met the detection requirements. The stability test of the loading speed of the device was carried out, and the results showed that the measuring angle of the designed device changed smoothly with time, which verified the stability of the loading speed of the device. Taking the maximum pushing moment and maximum pulling force as indexes, the field experiments on pushing and pulling lodging of different maize varieties and planting densities were carried out. The results showed that the correlation coefficients of maximum pushing moment and maximum tensile strength were -0.971 and -0.873, respectively at the density of 75000 plants/hm2. At the density of 105000 plants/hm2, the correlation coefficients of maximum pushing moment and maximum tensile strength were -0.991 and -0.927, respectively. The results showed that the maximum pushing moment was better than the maximum pulling force. Based on the field test, the reliability verification test of the device was further carried out with the maximum push-moment as the test index, and the test results were consistent with the existing research conclusions.

Abstract:Aiming at the problems of low automation, low pesticide utilization and poor adaptability of orchard spraying equipment in northern China, an orchard variable distance spraying equipment based on crown volume estimation was designed. The spraying arm of the equipment could adjust the spraying distance and height in horizontal and vertical directions under the control of the control system, so as to adapt to the characteristics of fruit trees in different orchards. In order to achieve precise spraying, a sensor array was constituted by infrared ranging sensor to detect the crown. The data measured by sensor array was used to build a tree crown volume estimation model by mathematical method, and the adjustment scheme of spray parameters was designed to make the spray distance and spray volume adjusted accordingly. In order to verify the performance of the spraying system, the actual movement time and the response time of the spraying arm when received the movement command and the accuracy of the crown volume estimation model were tested. Two groups of automatic target spraying tests of fixed distance and variable distance were carried out on simulated peach trees. The results showed that the movement of the spraying arm could achieve the instantaneous response and the actual movement time was almost consistent with the theory, and the relative error of the volume estimation model was 11.27%. Compared with the fixed distance target spraying, the variable distance automatic target spraying increased the pesticide attachment rate by 18.66%, and saved 30.25% of the liquid medicine.

Abstract:Flax stems contain a large amount of cellulose and have strong toughness. The discrete element rigid model is challenging to express the physical and kinematic characteristics of flax accurately stems in the process of joint harvesting, resulting in the lack of flexible model and contact parameters in the dynamic process of communal harvesting and difficult to micro study. In order to solve this problem, the root, middle, and neck stems of flax were taken as the research objects. The modeling parameters of flax stems were calculated based on the intrinsic parameters. The bonding modeling method of the discrete element method was used to construct the flexible model of flax stems, and the intrinsic parameters and contact parameters of flax stems were set as high and low levels. Plackett-Burman and Central-Composite tests were used to determine the contact parameters between flax stems and harvesting equipment. 〖JP3〗The flax stem shear test and stacking angle test were used to verify the model's reliability. The results showed that the normal stiffness Kn=1.13×109N/m3, and tangential stiffness Ks=5.6×108N/m3 in the flax flexible model parameters. Normal critical stress σ=6.67MPa, tangential acute stress γ=8.5MPa,bond radius Rj=0.25mm. The optimal values of recovery coefficient, static friction coefficient, and dynamic friction coefficient between flax culm-steel parts were 0.33, 0.28 and 0.14, respectively. The optimal values of recovery coefficient, static friction coefficient, and dynamic friction coefficient between flax stems-flax stems were 0.3, 0.508, and 0.033, respectively. In the shear test, the relative errors between the maximum shear load and the simulation results were 1.67%, 3.09% and 5.44%, respectively. In the stacking angle test, the relative error between the average stacking angle of the stem and the simulation results was 0.31%. The flexible modulus of flax stem was consistent with the contact parameters and the actual situation. The physical properties of flax stem can be characterized. The research result can provide some reference for discrete element simulation of flax stem.

Abstract:In view of the problems of unstable measurement, large packaging damage and low packaging efficiency in the quantitative packaging of potato quantitative packaging machine in China, the principle of S-type tension sensor was used to weigh, and the weighing accuracy and stability were improved. On this basis, the structural design of quantitative packaging device was carried out. The structural parameters were determined by analyzing the key components of the quantitative packaging device, and the key factors affecting the packaging performance and the range of values were clarified. Taking conveyor belt speed, drainage plate angle and bagging height as experimental factors, weighing qualified rate, peeling rate and potato injury rate as experimental evaluation indexes, with the help of software Design-Expert 10.0.3, three factors and three levels experiments were carried out by Design-Behnken, and the experimental results were analyzed by variance analysis. The influence of various interactive factors on the experimental indexes was analyzed by response surface test. The structure and working parameters of the quantitative bagging device were optimized, and the optimal value of each factor was determined according to the actual working conditions. On this basis, five quantitative packaging tests were carried out on the test bench. The verification test showed that when the speed of the conveying line was 0.44m/s, the angle of the drainage plate was 62.3°, and the packaging height was 496.1mm, the qualified rate of weighing was 97.32%, the rate of skin breakage was 1.22%, and the rate of sweet potato injury was 0.94%. Compared with the theoretical value after parameter optimization, the relative errors between the measured value and the theoretical value were 1.23%, 2.40% and 2.17%, respectively. It was shown that the quantitative bagging device improved the qualified rate of bagging, improved the efficiency of bagging and replacement, and reduced the damage of skin and potato.

Abstract:In order to solve the problems of low root cutting rate and high bulb damage rate after cutting device in the process of garlic combined harvesting, a root cutting device was designed. The key parts of the root cutting device on garlic combine harvester mainly included clamping conveying mechanism and cutting mechanism. According to the structural characteristics and operation requirements of garlic combine harvester, the bulb and root could be separated automatically in the process of movement with the counter rotating disc cutter and pressing of shifting wheel. The dynamic and deformation model of shifting wheel pair and the mechanical model of root cutting were established to achieve the separation of bulb and root in this process. Taking sprocket speed, shifting wheel speed and disc cutter speed as test factors, taking garlic damage rate and root cutting rate as test index, the mathematical models of response values were established and regression analysis and response surface analysis were carried out. The test results showed that when the speed of sprocket was 107r/min, the speed of paddle wheel was 52r/min and the speed of disc cutter was 197r/min, the performance of root cutting device of combined garlic harvester was the best, with the garlic damage rate of 0.63% and the root cutting rate of 97.07%. The optimization results were verified by tests, and the verification results were basically consistent with the optimization results. The research results could provide a technical reference for further exploring the technology of garlic combine harvester and bulb-root separator.

Abstract:The leaves of hydroponic lettuce were easy to damage during the process of harvesting in the plant factory, to improve the harvesting quality for hydroponic lettuce, a flexible harvester with low damage was designed and manufactured combined with the agricultural technology of hydroponic lettuce. To realize flexible harvesting with low damage for hydroponic lettuce, image processing was used to detect the outer rectangle of hydroponic lettuce contour, and the plant height and leaf expansion size of lettuce were obtained. The lettuce leaves were grabbed by flexible fingers, and then the lettuce stem was removed by the cutting device. The working process and structure parameters of flexible grabbing device, stem cutting device, and control system were analyzed. The parameters combination of grabbing height ratio, grabbing diametrical ratio, and bending angle for flexible fingers were optimized by using response surface methodology. The optimizing test results showed that the order of significance for harvesting success rate was bending angle, grabbing diametrical ratio, grabbing height ratio, and the single factors had highly significant influence on the leaves damage area. The optimized combination of factors was solved by quadratic fitting function, and the value of grabbing height ratio, grabbing diametrical ratio and bending angle were 0.55, 0.76, 39.7°, respectively. The verification test showed that the leaves damage area was 186mm2, and the harvesting success rate was 96% under the optimized combination, which meant the low damage harvesting for hydroponic lettuce was realized.

Abstract:In view of the low efficiency of manual picking of chrysanthemum and the lack of mechanization, a pneumatic flipping comb-type chrysanthemum picking device was designed based on the idea of reciprocating motion of picking parts. The picking device was mainly composed of picking part, tooth cleaning part, pneumatic throwing device, screw lifting mechanism, walking device and collecting device, etc. The flowers were picked by the combing action of the comb teeth, and the collecting work was completed by the tooth cleaning part and pneumatic throwing device, and the working height of the picking part can be adjusted by the screw lifting mechanism. Based on the analysis of the growth characteristics and picking requirements of chrysanthemums, a pneumatic flipping comb-type chrysanthemum picking device was designed. The working principle of the picking device was analyzed, the force analysis and theoretical calculation were passed and the structure and working parameters of each component were determined. A prototype picking machine was built, and a ternary quadratic regression combination test was conducted with crank speed, comb spacing and machine driving speed as the test factors, and the picking rate, breakage rate and impurity rate as the test indexes, a mathematical model between the factors and indexes was established, and the optimal combination of parameters was determined. The best picking effect was achieved when the crank speed was 47.94 r/min, the comb spacing was 8mm and the machine driving speed was 0.17 m/s. At this time, the picking rate was 92%, the breakage rate was 1.83%, and the impurity rate was 10%. This pneumatic flipping comb-type chrysanthemum picking device had stable operation and good passability, which met the agronomic requirements of chrysanthemum picking.

Abstract:In order to study the effect of blade swept back on the cavitation flow field in tip region and performance of axial flow pump, the swept back 20° and swept back 40° impeller were redesigned based on the scaled model of TJ04-ZL-02 impeller in South to North Water Diversion Project. The whole flow field was calculated based on ANSYS CFX, the changes of pressure, cavitation and streamline distribution in the passage of prototype impeller and swept back impeller were compared and analyzed, and the effects of blade swept back on vortex intensity, vorticity and velocity field in tip region under different cavitation conditions were analyzed. The results showed that the NPSHa of the swept back impeller was decreased, and the cavitation area and cavitation volume fraction of the suction surface of the swept back blade were lower than those of the prototype blade. The backward sweep of the blade reduced the low pressure area of the suction surface and effectively inhibited the development of triangular cavitation cloud in tip region. As the blade swept backward reduced the pressure difference in the tip area, the tip leakage was decreased, and the vortex intensity and vorticity in the TLV core area were decreased with the increase of the swept angle. With the aggravation of cavitation, the vortex intensity and vorticity of TLV was increased, and the flow field in the tip area became more complex and disordered. Axial flow pump had the advantages of simple structure, large flow rate and high efficiency, and it played an important role in flood control and drainage, large-scale water transfer. The blade swept back improved the cavitation performance of the pump and had great potential in improving the cavitation performance of the axial flow pump.

Abstract:In order to explore the potential of estimating the fraction of absorbed photosynthetically active radiation (FPAR) of crops from unmanned aerial vehicle (UAV) multispectral remote sensing images, the vegetation index, texture index and leaf area index (LAI) were extracted from UAV multispectral images, which were used as model input parameters. On the basis of analyzing the correlation between different parameters and FPAR, the vegetation index and texture index were optimized. The FPAR of maize was estimated by unary linear regression (UL), multivariate stepwise regression model (MSR), ridge regression model (RR) and BP neural network model (BPNN). The results showed that the vegetation indexes, texture indices and LAI had a strong correlation relationship with FPAR, and the absolute value of correlation coefficient of vegetation index was the largest. Among different types of FPAR estimation models, BPNN model had the best estimation effect. The determination coefficient (R2) and root mean square error (RMSE) of FPAR estimation model were 0.857 and 0.173, respectively. The R2 and RMSE of validation model were 0.868 and 0.186, respectively. The relative error (RE) between model estimation value and field measured value was 8.71%. In different combinations of model parameters, the FPAR model fused with vegetation index, texture index and LAI had the best estimation and verification effect, which indicated that the fusion of multi feature parameters can effectively improve the estimation effect of FPAR. These results provided a scientific basis for precision estimation of maize FPAR and production potential based on UAV multispectral remote sensing data.

Abstract:Obtaining chlorophyll content of crops rapidly is of great significance for timely diagnosing the health status of crops and guiding field management. In recent years, the development of unmanned aerial vehicle (UAV) has made it possible to quickly and accurately obtain information at the farm scale. The purpose was to estimate SPAD of summer maize based on UAV multispectral images, especially focusing on whether the hierarchical linear model with meteorological data had high accuracy. SPAD in the jointing stage, the tasseling stage and the filling stage were measured by SPAD-502Plus chlorophyll meter, and the multispectral images were captured by RedEdge mounted on DJI M600 Pro. Firstly, the vegetation indices of 21 experimental plots were extracted by band math and establishing the region of interest. Then, the correlation between the vegetation indices and SPAD was analyzed, and the vegetation indices with high correlation coefficient were selected as the input variables of SPAD estimation model. At last, the SPAD estimation models for the jointing stage, the tasseling stage, the filling stage and the whole growth stage were constructed by using partial least squares (PLS), random forest regression (RF) and hierarchical linear model (HLM), respectively. The results were compared to select the best model, which could provide support for SPAD estimation. It was found that except NRI, other vegetation indices (NDVI, OSAVI, GNDVI, RVI, MCARI, MSR, CIre) were significantly correlated with SPAD, furthermore, OSAVI and NDVI had strong and stable correlation with SPAD. The best model for each growth period was established by RF. For the jointing stage, the tasseling stage, the filling stage and the whole growth period, the R2 of the test set was 0.81, 0.81, 0.73 and 0.61, and the RMSE was 1.24, 2.32, 3.13 and 3.20, respectively. The HLM model, which coupled rainfall and maximum temperature with vegetation index, could improve the accuracy of linear model for estimating SPAD, but its accuracy was lower than that of RF. Therefore, the RF model based on UAV multispectral images could realize the estimation of SPAD of summer maize timely and accurately.

Abstract:Aiming at the problem of accurate management of orchard under the background of complex environment such as bare soil, shelter, fruit tree shadow and weeds, the image data of apple orchard was obtained by UAV equipped with multispectral camera, and then the fruit tree pixels were extracted and the global fruit tree row navigation line was extracted. The obtained multispectral image data were preprocessed to obtain digital orthophoto map (DOM) and digital surface model (DSM) image. The normalized difference greenness index (NDGI) and ratio vegetation index (RVI) distribution maps that were easy to distinguish apple trees from weeds were selected and calculated, and the NDGI and RVI images were fused with DSM image; the excess green (EXG) index and normalized difference canopy shadow index (NDCSI) were comprehensively used to eliminate the pixels such as soil, shelter and shadow in the fusion image by threshold segmentation method, so as to reduce the interference of non-vegetation mixed pixels on the classification and recognition of fruit trees. Support vector machine (SVM), random forest (RF) and maximum likelihood (MLC) method were used to extract the apple trees in the fused image and ordinary orthophoto respectively, calculate the confusion matrix, and compare and evaluate the recognition accuracy. The experimental results showed that the MLC method had the best recognition effect on fruit trees in the fused image, and its user accuracy, mapping accuracy, overall classification accuracy and Kappa coefficient were 88.57%, 93.93%, 93.00% and 0.8824, respectively; compared with ordinary orthophoto images, the final fusion image constructed effectively improved the recognition accuracy of the three methods. The fused image improved the user accuracy of RF method the most, which was 27.12 percentage points; the mapping accuracy of SVM method was improved the most, which was 9.03 percentage points; the overall classification accuracy of the three methods was improved by 13.00 percentage points; the Kappa coefficient of SVM method was improved the most, which was 22.55%, and the improvement of the other two methods was also more than 20%. Finally, after denoising, binarization and morphological transformation of the apple tree pixel extraction result image, the fruit tree row feature points were extracted by the region of interest division method, and the fruit tree row navigation line was obtained by fitting each row feature points by the least square method. The average angular deviation of this method was 0.5975°, and the overall average time after ten tests was 0.4023s. The research result can provide a basis for the identification and extraction of fruit tree pixels and fruit tree row navigation line in complex environments.

Abstract:In order to solve the difficulty of fruit recognition in near color background, an algorithm for identifying and locating fruits from the depth image was presented based on the near-spherical morphological characteristics of fruits. A depth camera was used to get depth image of a fruit tree. The gradient vectors of each pixel point from the depth image were calculated. The gradient vector was considered as a vector field of motion and the divergence of the vector field was calculated. Searching for divergence center points from vector fields according to the principle of maximum divergence. The fruit center point was selected from the divergence center point by using the difference of the contour image between the fruit and the leaf. The fruit boundary points were searched in eight directions from the center point of the fruit. The fruit images in the closed area formed by connecting the fruit boundary points were imported into the point cloud. Finally, the point cloud was used to find the fitting circle of the target fruit according to the random sample consensus (RANSAC) algorithm, and the size and spatial location of the fruit were obtained. The algorithm discarded the color features commonly used in traditional algorithms but used only the depth information in the depth image for fruit recognition and positioning. It can overcome the drawbacks of traditional algorithms affected by color, illumination and other factors. Because the algorithm did not use color image information at all, it can not only recognize and locate green fruits, but also enable the harvesting robot to work in dark environment. The research result can provide a method for fruits recognition and location in complex environment.

Abstract:The realization of automatic feeding of bait has always been the focus and difficulty of automatic aquaculture. Recognition of the fish feeding intensity can provide a reference for accurate feeding. At present, many laboratories have researches on the fish feeding intensity, but most of the researches on the fish feeding intensity are based on circulating farming ponds or self-made fish tanks, which are not suitable for open farming ponds. Aiming at the actual environmental background and difficulties, the water observation method was used to build a data acquisition system and produce a video data set of fish feeding intensity. Then a fish school feeding intensity classification model was proposed based on improved long-term recurrent convolutional networks（LRCN）, which embeded the attention mechanism squeeze-and-excitation block （SE-Block） into the convolutional neural networks. The SE-CNN networks was used to extract the features of the video frames, then input the features into the doublelayer gate recurrent unit networks. Finally, the video classification results were obtained through the fully connected classification layer. At the end, the proposed SE-LRCN model realized the intensity three classification of the fish school feeding intensity video. The test results showed that the classification accuracy of the proposed model reached 97%, and the F1 score reached 94.8%. Compared with the long-term recurrent convolutional networks before the improvement, the accuracy was increased by 12 percentage points, and the F1 score was increased by 12.4 percentage points. The research model can more finely recognize the fish feeding intensity, and provide a reference for automatic accurate feeding.

Abstract:In order to solve the problem of insufficient accuracy in evaluating the feather coverage of laying hens by using manual and temperature threshold segmentation methods, thermal infrared images and color images approaches were proposed to calculate the feather coverage. By using Otsu algorithm combined with different color models to extract the chicken body target and the well-covered area of the back feathers and calculate the area ratio, the coverage of the back feathers was obtained. Aiming at the problem of poor segmentation effect caused by the blurred edge of the target chicken body in thermal infrared images, an adaptive thermal infrared image enhancement method based on the gray histogram of R and B components was proposed. The experimental results showed that the segmentation accuracy of chicken body targets and well-covered areas with feathers in thermal infrared images reached 97.18% and 96.86%, and the segmentation accuracy of color images reached 99.58% and 97.86%. The calculation result of the thermal infrared images was closer to the real value of feather coverage than that of color images. The analysis revealed that the roots of white feathers exposed by behaviors of laying hens rubbing against the chicken coop were the main factors for the deviation of the coverage results of the color image calculation. Feather coverage affected chicken body surface temperature. The results showed that the chicken body feather coverage was significantly negatively correlated with the body surface temperature (P<0.01), and the average temperature of the damaged area of the same level was 10℃ higher than the average temperature of the back.

Abstract:Pig counting is a critical task in large-scale breeding and intelligent management, and the manual counting method is time-consuming and labor-intensive. Since fattening pigs are more active and like to congregate, there are many high-density areas in the image, which causes problems such as adhesion and occlusion between pigs, making pig counting difficult. Based on the SOLO v2 instance segmentation algorithm, a high-density group pig counting model in natural breeding scenarios was proposed, which incorporated multi-scale feature pyramids and deformable convolutions networks version 2. Further, by optimizing the model structure, the consumption and occupation of computing resources were reduced. The pig count dataset published by iFLYTEK was divided into two parts: pig segmentation dataset and pig count test set. The pig segmentation dataset was used to train the segmentation model to achieve herd segmentation and pig counts, and the inventory accuracy was tested in the pig inventory test set. The experimental results showed that the high-density pig counting model proposed had a segmentation accuracy of 96.7% and a model weight size of 256 MB, which was 1/3 less than that before the improvement. Each improved method proposed improved the model's segmentation accuracy and achieved highaccuracy segmentation of individual pigs in the case of occlusion, adhesion, and overlap. In the 500 image pig counting test set, the number of images when the model counted pigs with an error of 0 was 207, which was 26% more than that before the improvement. The number of images when the error of the model counting pigs was less than two pigs, accounted for 97.2% of the total number of test images. The number of images with a counting error of more than three pigs, accounted for only 1% of the total number of images. Finally, for the pig herd counting method based on YOLO v5, the model had a better segmentation effect and counting accuracy, proving the methods effectiveness for counting pigs in groups. Therefore, the model presented not only achieved accurate counting of high-density pig herds, but it also significantly reduced the model's reliance on computing equipment by optimizing the model structure, which made it suitable for online counting of pig herds on real farms.

Abstract:Aggressive attacking behaviors of artificial rearing male sika deer on heat period are increased dramatically, which causes damages to deer’s antlers and even deer themselves. Automatic monitoring of their aggressive attacking behaviors can provide an important basis for the research to reduce them. A dual-stream neural network (optical flow attention attacking recognition network, OAAR) was proposed, which was based on the attention mechanism and long-short memory sequences. It was used to achieve automatic recognition and detection of sika deer behaviors, including attacking, feeding, lying down, and standing. The OAAR network consisted of a per-network, a base network, and a time-sequential network. The pre-network consisted of the LK optical flow algorithm（lucas kanade optical flow algorithm), which was used to extract the information from the RGB data. In the base network, a self-attentive module was added to the ResNet-152 to build a new design ARNet152 (Attention ResNet-152), which was used to combine the RGB and optical flow information, extract the features, and input them into the time-sequential network. The time-sequential network was based on an attention long short term network (ALST), which was composed of an attention long-short memory sequence that can classify the behavior and give scores. The experimental dataset was composed of 10942 video segments, with a total of 310574 frames, which were divided into four major categories of behaviors, including aggression, foraging, standing, and lying. From the aggressive behaviors, three sub-categories were further divided, including hitting, kicking, and chasing. The training, validation, and test sets were divided at a ratio of 3∶1∶1. The results of the study showed that the OAAR model reached an accuracy of 97.45%, a recall rate of 97.46%, and an F1 value of 97.45% on the test set, and good classification results in ROC curves and improved discrimination in feature embedding maps. All the results of OAAR were better than the results of LSTM, I3D, and ITSN networks. Meanwhile, the online deer behavior identification and recording system based on the OAAR network was developed to improve the management level and production efficiency of the sika deer farming industry.

Abstract:In the process and management of breeding birds breeding, with the help of noncontact and continuous sound detection as well as some intelligent equipment, the breeder can fully understand the health status and individual needs of breeding birds, which can improve production efficiency as well as animal welfare. A kind of lightweight convolution neural networks for breeding birds voice recognition was proposed. The sound of the Hy-line brown breeding birds was taken as the research object, and five kinds of common sounds in the breeding bird house were collected, then the one-dimensional signal of sound was converted into two-dimensional image signal. Based on the great advantages of convolutional neural network in image recognition, a lightweight deep learning model was established, with 80% data as training and 20% data as testing. This model realized the efficient detection process of animal sound signal from input to output of recognition results. By comparing and analyzing the recognition methods of previous studies, the proposed method greatly improved the overall accuracy rate of recognition of five kinds of common sounds in breeding birds' house by 3.7 percentage points. The experimental results showed that the average accuracy rate of this method was as high as 95.7%. The recall rate of the model for drinking water, fan noise and laying call were all up to 100%, and the precision rate and F1 value of fan noise and laying call were also up to 100%. While, the recall rate of stress call was the lowest value of 88.3%. The research result provided some theoretical reference for the research and development of unmanned intelligent equipment in the future large-scale chicken house.

Abstract:Sugarcane yield prediction is important for making accurate management decisions during sugarcane growth. Genetic algorithm (GA) optimized neural network can improve the prediction efficiency and prediction accuracy, and find the optimal solution quickly by high-speed calculation. Based on the meteorological factors (atmospheric humidity, atmospheric temperature, rainfall), field hydrothermal factors and sugarcane yield obtained from the field IOT at Zhanjiang Observation and Experimental Station during 2011—2020, BP neural network and GA-BP neural network models were used to predict and correlate the sugarcane yield in the selected areas. The results showed that the correlation coefficients of Pearson and Spearman showed that sugarcane yield was highly significantly correlated with monthly maximum soil temperature, monthly minimum soil temperature, monthly average soil temperature, monthly maximum atmospheric temperature, monthly average atmospheric temperature, monthly average atmospheric humidity with correlation coefficients higher than 0.7, significantly correlated with monthly average soil water content, monthly rainfall, and weakly correlated with monthly minimum atmospheric temperature. Under the GA-BP neural network model, the prediction accuracy of sugarcane yield was significantly higher than that of the BP neural network model, with R2 reaching 0.8428, MAPE of only 0.90%, and RMSE of 1.10t/hm2. The degree of fit between the predicted and experimental values was high, and the V-cross validation results showed that the model prediction results were accurate and stable. Therefore, GA-BP prediction can predict sugarcane yield more scientifically and rationally, which was an important guiding significance for sugarcane field management measures and coordinated allocation.

Abstract:In order to investigate the response of nitrogen on the morphological indices of various organs in maize growth and development, and build a model that could simulate the morphological indicators of organs in maize, a field experiment was conducted with maize, and four nitrogen fertilization treatments were set with nitrogen content of 0kg/hm2(N1), 225kg/hm2(N2), 338kg/hm2(N3) and 450kg/hm2(N4), respectively. The changes of organ morphological indicators, including leaf length, leaf width, internode length, internode diameter and leaf sheath length at different growth stages of maize under different nitrogen application levels were investigated, and the simulation model of crop organ morphological structure was established. The results showed that nitrogen fertilizer treatments had significant effects on leaf length (P<0.01), while different leaf positions had extremely significant effects on leaf length (P<0.001). Nitrogen treatment had no significant effects on leaf width, internode length and stem diameter, among which, leaf sheath length had significant effects on each growth stage in 2020 (P<0.01).In addition, the effects of growth stage, nitrogen treatment and leaf position on leaf sheath length were not significant. A simulation model of maize organ scale was constructed, and the model parameters were calibrated and verified by using two years of experimental data. The model simulation efficiency (EF) was above 0.72.The root mean square error (RMSE) of leaf length and leaf width were 7.285cm and 1.200cm, respectively. The RMSE of node length, internode diameter and leaf sheath were 1.593cm, 0.171cm and 1.282cm, respectively. The mean absolute error (MAE) of leaf length and leaf width were 5.817cm and 0.708cm, respectively. The MAE of node length, internode diameter and leaf sheath were 1.111cm, 0.116cm and 0.923cm, respectively. The results showed that the model had good prediction and interpretation for the simulation of the developmental process of various organs in maize.

Abstract:The traditional robot V-SLAM frontend positioning algorithm is based on manually set feature point extraction and descriptor local matching for positioning. Due to the subjectivity of manual setting, the extraction method will have poor robustness and weak adaptability to complex scenes (scene brightness changes, the introduction of noise, motion blur) and the low accuracy of local descriptor matching. For this reason, a front-end positioning algorithm (SuperPoint Brief and K-means visual location， SBK-VL） was proposed. The algorithm firstly used an improved p-probability-SuperPoint deep learning framework extracted feature points to solve the problem of low robustness of feature points and weak adaptability to complex scenes; secondly, a combination of global information (feature point clustering) and local information (Brief descriptor) was proposed. Descriptors can reduce the mismatch of traditional descriptors and improve the problem of low matching accuracy. The experimental results showed that the average matching accuracy rate was 92.71%. Finally, replacing the SBK-VL with the front end of ORB-SLAM2, a Ransac random sampling method was used to test the pose, and the absolute trajectory error index was used. Relative trajectory error index and average tracking time were compared with that of ORB-SLAM2 algorithm and GCNv2-SLAM algorithm. The experimental results showed that the algorithm had better equalization performance. On the one hand, it can improve the complex scene adaptability and estimation accuracy of the classic V-SLAM algorithm. On the other hand, it had better real-time performance and computational cost than the traditional deep learning SLAM algorithm.

Abstract:Aimed at the problems of insufficient information and poor reusability of the sparse map constructed by ORB_SLAM2, a visual navigation method based on improved ORB_SLAM2 was proposed. It included two stages of building a multi-layer map and navigation. In the stage of building a multi-layer map, a local dense point cloud was calculated by the key frame of ORB_SLAM2, outliers were removed by radius filter and fast itreative closest point (FAST ICP) algorithm was used to register the processed point cloud. After that, 3D occupancy information was calculated by local dense point cloud; 3D occupancy information was extracted by means of the height of mobile robot in 3D space and 2D occupancy information was calculated by 2D mapping; 3D, 2D occupancy information and 3D, 2D features of the key frames were fused to generate a globally consistent multi-layer map. In navigation stage, according to the prior information of positioning layer, 2D features of the key frame were clustered to generate a visual dictionary, the visual dictionary was indexed according to the characteristics of current image to obtain the reference key frame; the initial pose was calculated by perspective-n-point (PnP) algorithm, and then reprojection error was used to construct inter-frame constraints, final result of localization was obtained through Gauss-Newton optimization; in planning layer, A* algorithm was used to plan path so that mobile robot visual navigation was realized. Verified by TUM dataset, the proposed method was about 50% faster than RGB-D SLAM, and the pose estimating was almost improved by 10%, the localization result based on prior map were consistent with the original map. In addition, the experiments on the real robot platform showed that the proposed method can construct a high-precision multi-layer map, and the error between the measured value of lAC and the real value was 6.7%, and the error between the measured value of lBD and the real value was 5.6%, and the fast and accurate navigation was achieved on the basis of multi-layer map.

Abstract:Crop disease monitoring has always been a research hotspot in the field of agricultural engineering because of its serious damage to the world’s food. In recent years, the use of microfluidic chip microbial sensors for crop disease detection has received attention from scholars. However, most of current microfluidic chips have the defect of low reuse rate. In response to this problem, a parallel double sheath flow focusing microfluidic chip was proposed, which was composed of an injection channel, a double sheath flow channel, a partial pressure channel, a detection channel and a circular chamber. Fungal spores entered the chip from the sampling channel with the airflow, and then were arranged in the center of the chip by the action of the double sheath airflow. Air pressure was controlled by the partial pressure channel to ensure that the fungal spores entered the detection area of the chip at the speed required by the test. Subsequently, fungal spores followed the airflow into the circular enrichment area. Experimental data showed that the spore velocity was decreased with the increase of the chamber diameter. Circular chamber diameter of 2500μm had the best enrichment effect, and the particle enrichment rate can reach 96.7%. An air pump was connected to the outlet of the chip to extract fungal spores, which can improve the reuse rate of the chip. Rice spores used were from the China National Rice Research Institute, and polystyrene microsphere samples were purchased from Tianjin Daye Technology Co., Ltd.. The experimental platform was built by aerosol generator, semiconductor laser, microfluidic chip, circuit board, focusing optical path device and other equipment. In order to realize the focused arrangement of fungal spores, the sample inlet flow rate and sheath flow rate needed to be optimized. Optimization results showed that, when the sampling flow rate and sheath flow rate of the chip were 2.5mL/min and 12mL/min, respectively, the particle focus width was 8μm, which can realize the particle focus arrangement and flow through the detection area in a row. The entire detection system was also composed of a focusing optical path device and a signal acquisition circuit. The focusing optical path device was composed of a filter, a half lens with a focal length of 14mm and an aperture surface of 10μm, which can focus the light source to about 10μm. The laser would excite spores passing through the detection area to produce forward and side scattered light, and then these two scattered lights would be collected by the signal collection circuit and transmitted to the upper computer. The forward scattered light signal contained the size information of the particles. Based on the experimental results, a detection model for the particle size and light intensity was established, with coefficient of determination of 0.9666, which had a good linearity. The side-scattered light signal contained the complexity of the particles. The forward and sidescattered light intensity information was fused to achieve effective classification of rice spore spores and polystyrene microspheres, with an average detection error of 7.04%, and the chip reuse rate was increased by about 9 times. The research result can provide a basis for the research and development of crop disease monitoring sensors.

Abstract:In order to clarify the response mechanism of carbon, nitrogen and phosphorus absorption, accumulation and allocation in black soil rice to water-nitrogen coupling mode and analyze the nitrogen and phosphorus nutrient restriction, three irrigation models, conventional flooded irrigation (F), shallow wet irrigation (S) and controlled irrigation (C), and four nitrogen application levels 0kg/hm2, 85kg/hm2, 110kg/hm2 and 135kg/hm2(N0, N1, N2, N3), were set up in the experiment, which were a total of 12 treatments. To study the effects of different water-nitrogen coupling modes on carbon, nitrogen and phosphorus contents, accumulation, allocation ratio, stoichiometric ratio and nitrogen, phosphorus nutrient restriction of rice plants during each growth periods. The results showed that under different water-nitrogen coupling treatments, the contents of carbon, nitrogen and phosphorus in stem sheaths were 35.87%~39.43%, 0.44%~2.19% and 0.14%~0.32%, respectively, and the contents of carbon, nitrogen and phosphorus in leaves were 36.34%~40.83%, 0.76%~3.70% and 0.14%~0.36%, respectively and the contents of carbon, nitrogen and phosphorus in panicle were 37.05%~41.72%, 0.82%~1.63% and 0.24%~0.39%, respectively. Controlled irrigation could increase the carbon and nitrogen accumulation from jointing booting stage to mature stage, and the phosphorus accumulation during the growth period of conventional flooded irrigation was always higher than that of shallow wet irrigation and controlled irrigation. Under three irrigation modes, compared with N0, N1, N2 and N3 treatments increased carbon accumulation by 31.46%, 52.55% and 57.37%, nitrogen accumulation was increased by 52.98%, 117.63% and 144.88%, and phosphorus accumulation was increased by 50.28%, 79.85% and 93.89%, respectively, at maturity stage. The proportion of carbon, nitrogen and phosphorus in stem sheaths was increased first and then decreased, the proportion of carbon, nitrogen and phosphorus in leaves was decreased continuously, and the proportion of carbon, nitrogen and phosphorus in panicle was increased continuously. Compared with conventional flooded irrigation and shallow wet irrigation, the controlled irrigation mode had less effect on the carbon content of rice plants, but it can increase the nitrogen content in the middle and late stages of rice plant growth, and reduce the plant phosphorus content, thereby reducing rice plant C/N and increasing rice plant C/P and N/P. Nitrogen application significantly increased the nitrogen content of rice plants, slightly increased the phosphorus content of rice plants, and had relatively little effect on the carbon content of rice plants, and then decreased the C/N and C/P of rice plants, and increased the N/P of rice plants. Under conventional flooded irrigation and shallow wet irrigation, the nutrition of shoot plants of rice transited from phosphorus restriction to nitrogen and phosphorus co-restriction and then to nitrogen restriction, while under controlled irrigation, shoot plants of rice only transited from phosphorus restriction to nitrogen and phosphorus co-restriction. In general, controlled irrigation can promote nitrogen absorption and increase rice yield, and it can be considered comprehensively that CN2 can be the best water-nitrogen coupling mode.

Abstract:In order to explore the influence of biochar addition on soil hydraulic characteristics under indirect subsurface drip irrigation with biogas slurry, four levels of biochar mass percentages (biochar and soil mass ratios of 0, 1%, 2%, and 5%) and four levels of biogas slurry content (the volume ratio of biogas slurry and water was 0, 1∶8, 1∶6 and 1∶4) were set in the experiment. The biochar and the biogas slurry used wheat straw and cow dung as raw materials, respectively. The soil water holding capacity, soil saturated water conductivity and soil wetting characteristics were investigated. The results showed that soil water holding capacity was increased with the increase of biochar and biogas slurry contents. The Van-Genuchten model can accurately fit the soil moisture characteristic curve of each treatment. Both biochar addition and biogas slurry irrigation alone can increase soil porosity and capillary porosity, and their coupling effects on soil porosity and capillary porosity were more obvious. The soil saturated water conductivity was decreased firstly and then increased with the increase of biochar content, and reached the minimum value when the biochar content was 2%; while it was decreased with the increase of biogas slurry content; moreover, there were an interactive effect between the influence of biochar addition and biogas slurry irrigation on soil saturated water conductivity. The power function can accurately describe the relationship between the wetting front transportation distance and irrigation time under different treatments. The difference of biogas slurry and biochar content among different treatments had obvious effects on the shape of soil wetting body under indirect subsurface drip irrigation, when the biochar content was 1% or 2%, the migration distance of wetting front and volume of wetting body was decreased with the biogas slurry application, the range in the wetting body with high water content (greater than soil field water capacity, 0.3062cm3/cm3) was increased; for treatment with 5% biochar content, the horizontal and vertically upward migration distance of wetting front was decreased with the application of biogas slurry, while the vertically downward migration distance was increased, resulting in an relative smaller area of wetting body with high water content although the volume of wetting body which was as big as that of treatment with 2% biochar content. Taking all indexes into comprehensively consideration, the coupling treatment of Z1∶4B2, and the fine loam soil under indirect subsurface drip irrigation can obtain better soil hydraulic characteristics.

Abstract:Aiming to determine the soil water-groundwater dynamics and their transformation relationship in the Hetao Irrigation District under the background of restricted water diversion, so as to provide a theoretical basis for optimizing farmland water management strategies, a typical bucket canal area in Hetao Irrigation District was selected, and the dynamic changes of farmland soil water in different crop planting areas and groundwater in different irrigation periods were analyzed based on the monitoring data of soil water and groundwater in two years. Using soil water balance to quantitatively study the transformation relationship between farmland soil water and groundwater in shallow groundwater areas, the results showed that the change of farmland soil moisture during the growth period belonged to the “irrigation precipitation infiltration supplement-evapotranspiration type”; it was different due to irrigation. The dynamics of groundwater depth during the period had significant characteristics, and the groundwater depth fluctuated drastically with the leakage of soil moisture in the farmland; during the growth period of crops, soil water and groundwater were recharged in both directions, and different periods had different transformation characteristics; the internal irrigation and precipitation replenish soil water were 544.56mm and 541.85mm in 2a growth periods, respectively. The average evapotranspiration in the study area was 465.5mm and 434.8mm, respectively, the soil water storage was reduced by 61.96mm and 63.1mm, respectively, and the soil water replenishing groundwater was 207.73mm and 236.94mm, respectively. The research results can provide a scientific basis for agricultural water-saving irrigation in the local and similar areas.

Abstract:In order to improve the data visualization and information level, a kind of greenhouse remote monitoring system was designed and developed, which included inspection robot, mobile communication network, cloud server and remote monitoring center. Three kinds of data transmitted between the greenhouse and the remote monitoring center, including text, image and video. Machine learning and deep learning algorithms were used for human-computer interaction and tomato recognition tasks. On the one hand, administrator face recognition was achieved based on Haar cascade and LBPH algorithms, and the recognition success rate was 90%. Then YOLO v3 and ResNet-50 algorithms were used to recognize the hand and the key points of hand respectively, and the recognition confidence of singlehand and two-hand was 0.98 and 0.96, respectively. Based on the extracted coordinates of the forefinger and the center points of the left and right hand candidate frame, finger interaction and image size scaling were realized. On the other hand, the model framework of Swin Small+Cascade Mask RCNN was used for tomato recognition. Aiming at the problem of limited agricultural data set, the effect of tomato detection before and after applying transfer learning method was compared and analyzed. By using transfer learning method, the experimental results showed that the convergence rate of the model was increased and the loss value was decreased. In terms of semantic segmentation, AP value was used to evaluate the model performance, when IoU was set to be 0, 0.5 and 0.75, test results showed that the mask average precisions were improved by 7.8 percentage points, 6.4 percentage points and 7.2 percentage points, respectively after using transfer learning method.

Abstract:In order to realize the precise automatic irrigation of melon cultivation in facilities, an irrigation decision-making system based on the growing demand of melon was designed. Taking into account the three categories of melon yield, quality, and water and nitrogen use efficiency, a comprehensive evaluation system was established involving ten indicators, and the AHP method integrating the maximum membership degree was adopted to determine the comprehensive optimal daily irrigation level for melon cultivation. The K-means clustering algorithm was used to analyze the daily optimal irrigation amount and ambient temperature and humidity, and a quantitative irrigation decision model based on the clustering results of vapor pressure deficit (VPD) was established for melon in different growth stages. The results showed that the comprehensive growth of melon was optimal under the irrigation level of 120%evapotranspiration. When the number of cluster centroids was 3, the contour coefficient was the largest of 0.72. The contours and boundaries between groups were clear, and the irrigation amount was increased significantly with the increase in VPD, and the clustering results were the best. For the automatic irrigation system, RS485 sensors of temperature and humidity were used to detect the greenhouse environmental parameters in real time, and model-based irrigation control was realized by microcontroller of STM32F103ZET6, and electronic flowmeter was used to measure and feedback the amount of irrigation water, and then remote monitoring function was realized through cloud platform. The system application verification test showed that the melon produced by this system was slightly better than that of conventional agronomic management in yield, soluble solids and soluble protein, and it had significant advantages in water saving, with 15.9% reduction in irrigation amount for the whole growth period, which also greatly reduced labor costs, and realized automatic precision irrigation.

Abstract:Nitrite content is one of the most important quality indices of milk. The traditional methods used to measure the nitrite content of milk are high in precision and good in repeatability, but the process is complex, the equipment is expensive, and these methods just can be used in the laboratory. To realize the detection of nitrite content of milk low in cost, fast and convenient in detection, a portable nitrite detector was developed based on the principle that a purplered azo-compound was produced when nitrite reacted with p-aminobenzenesulfonic acid and naphthylenediamine hydrochloride under weak acid conditions. Taking commercial milk as object and the NaNO2 given in Chinese national standard as nitrite, the characteristic wavelengths for detecting the nitrite content was extracted from the spectra range of 380~780nm by successive projection algorithm. Five light-emitting diodes at the wavelengths of 469nm, 500nm, 546nm, 628nm and 665nm were determined for collecting the intensity of diffuse reflected light. The developed detector consisted of a microcontroller module, a light source module, a light sensor module, a power supply, and an input and output module. Based on the intensity of diffuse reflected light at the five wavelengths, a partial least squares model was developed to predict the nitrite content of milk. The precision of the detector was verified, and the results showed that the measurement error on nitrite mass concentration ranged from -0.13mg/L to 0.07mg/L, and the average absolute error was 0.03mg/L.

Abstract:Aiming to study the nutritional quality and volatile flavor composition of Xing'an lambs, taking Xing'an lambs as the object, the nutritional composition of mutton was analyzed by Kjeldahl method, Soxhlet extraction method and burning method, and the flavor substances of mutton were analyzed by solidphase microextraction combined with gas chromatography-mass spectrometry. The effects of different genders and different parts on the nutrition and flavor of lambs were compared. The results showed that the water content, crude fat content, crude protein content and ash content of Xing'an lambs were 75.32%, 2.41%, 19.81% and 1.11%, respectively. Totally 28 kinds of fatty acids and 18 kinds of amino acids were detected. The crude fat content in muscle of female lambs tended to be higher than that of male lambs. Quadriceps femoris muscle contained more unsaturated fatty acids and fat oxidation products. Totally 72 volatile flavor compounds were detected in Xing'an lambs, among which aldehydes and alcohols were the most important compounds. Different genders and different parts had certain influence on nutritional composition and flavor substance of lamb meat. The relative contents of seven volatile flavor compounds in female lambs were significantly higher than that in male lambs. Due to the low fat content, small odor and grease smell, the female lamb was more acceptable than the male lamb. The relative content of 2(5H)furanone and hexanol in longissimus dorsi muscle was significantly higher than that in quadriceps femoris, but the relative content of tridecaldehyde was significantly lower than that in quadriceps femoris.

Abstract:Aiming at the problems that ham, sausage and other meat products are prone to quality spoilage and shortened shelf life in the process of production, transportation and sales, the quality spoilage parameters of ham were studied based on nearinfrared spectroscopy, a ham quality and corruption portable detection device was developed with automatic modeling function, and a ham shelf-life early warning system was designed based on the Internet of Things technology. Based on the developed detection device, the visible/nearinfrared spectra of 74 ham samples were collected. After different pretreatments, the ham color (L*, a*, b*), pH value, and volatile base were established respectively. Partial least squares (PLS) prediction model for nitrogen (total volatile basic nitrogen, TVB-N) and total colony (total viable count, TVC). On this basis, a competitive adaptive weighted sampling (CARS) algorithm was used to screen feature variables, and a simplified PLS model was established. The root mean square errors were 0.3530,0.2529, 0.0961, 0.0354, 0.3724mg/(100g), and 0.4398lgCFU/g, respectively. The results showed that the device can be used for the detection of ham quality spoilage parameters. At the same time, the TVC growth law during ham storage was studied, the TVC growth curve was fitted by the modified Gompertz equation, and the TVC growth kinetic model was established. The shelf life was established by the TVC spectrum prediction model and growth kinetic model prediction model. Alibaba cloud server and MySQL database were selected as server-side development tools, and based on the TCP/IP network communication protocol, the detection data of the device was transmitted to the server-side in real time, and then saved in the database. Finally, the data of shelf life was fed back to the client, and real-time early warning of ham spoilage can be realized by monitoring the shelf life. The experimental test showed that the system can be used for real-time early warning of ham shelf life.

Abstract:For autonomous navigation systems, when the agricultural machinery changes speed, it usually leads to the worsening of control precision and stability. As the Beidou satellite navigation technology and the MEMS inertial sensor technology become increasingly mature, the research and development of autonomous navigation system of agricultural machinery have been greatly promoted. The Dongfeng agricultural tractor DF1004-2 was modified to be an experimental platform, and the crucial devices such as intelligent controller, wheel angle sensor, RTK base station were designed and developed for the tractor’s intelligent modification. Moreover, the state space model, including yaw rate state was established to achieve a stable feedback controller that adapted to large speed changes. Traditionally, the path tracking control of intelligent tractors assumed speed to be constant and designed controllers with a kinematic model or used pursuit strategy without a model. For these controllers, the convergence speed can be slow due to lack of yaw rate information, moreover, when speed switched, the steering magnitude of front wheel grew, which decreased the control accuracy and stability. The state-space model with yaw dynamics was established via system identification, and then a flexible LQR control strategy was designed based on the state-space model, which provided a solution to the challenge. Experimental results from agricultural fields showed that the control precision was 3cm without speed switch, and 5cm with speed switch, which met the production requirements of agricultural machinery.

Abstract:Dynamic analysis and performance optimization of the threetranslation parallel mechanism（PM）2P4R(Pa)-PR(Pa)R proposed by the authors were performed. Firstly, based on the symbolic positive solution of the mechanism, the (angular) velocity and (angular) acceleration of each member were obtained directly. Then, the dynamic model of the mechanism was established by using the sequential single-open-chain (SOC) method based on the virtual work principle. The SOC was taken as the basic unit, and the coefficient matrix of the dynamic equation can be directly called by the results of the mechanism velocity analysis, which improved the calculation efficiency. The correctness of the dynamic modeling was verified by comparing the theoretical calculation values of the three driving forces with the simulation results. Further, based on the differential evolution algorithm, taking the length and mass parameters of the rod as the design variables and the two performance indexes of reachable workspace and energy transfer efficiency as the objective functions, the two-level progressive optimization of the kinematic and dynamic performance of the mechanism was carried out, and the optimal solution of the mechanism under the above two performance indexes was obtained, which laid a foundation for the design of mechanism and the development of prototype. The three-translation parallel mechanism not only had analytic positive solution, but also had partial input-output motion decoupling, which was conducive to the trajectory planning and motion control of the mechanism. The mechanism was suitable for handling, grasping, loading and unloading of large-size workpieces along the length direction.

Abstract:For the integrated assembly of large precision and complex parts, a wheeled parallel gesturing robot that integrated omnidirectional motion and gesturing micro-motion was proposed. The omnidirectional motion features were integrated into the parallel mechanism by sharing some degrees of freedom, which solved the problems of large range of arbitrary directional movement and efficient continuous operation of precision posture in small space. Based on the spiral theory, the reasonableness of the position control axis selection was analyzed, and the performance of the two control methods in different end attitude domains was evaluated to obtain the force-position hybrid control active input selection strategy for different target attitudes of the device. Based on ADAMS and Simulink software, a mechatronic model with good environmental consistency and strong control can be built to verify the feasibility and accuracy of wheeled parallel attitude adjustment equipment by using force-position hybrid control strategy. The experimental results of the engineering prototype verified that the proposed redundant control strategy can realize the functions of wheeled parallel posture robot and ensure the posture accuracy, and laid the theoretical and practical foundation for the development of a 6-degree of freedom parallel posture, alignment and assembly intelligent robot with high reliability, large scale, heavy load, high accuracy, and high precision omnidirectional motion and precision posture functions.

Abstract:Robot inverse kinematics analysis is the basis of many researches, but the process of multi-degree-of-freedom robot with a general structure inverse kinematics is complicated, and the analytical method may have no solution. Combining the improved fitness function of intelligent algorithm with the analytical method, a method for solving the inverse kinematics of multi-degree-of-freedom robot with a general structure was proposed, which improved the efficiency and accuracy of multi-DOF inverse kinematics analysis. The general structure satisfied at least one set of adjacent joint axes intersect at one point. Using the method of disconnection and reconnection to construct the fitness function of disconnection and reconstruction, and intelligent algorithm was used to verify the fitness function of disconnection and reconstruction and solve some joint angles, and then the analytical method was combined to find the other joint angles. Compared with the fitness function constructed by other methods, this method simplified the process of establishing the fitness function and reduced the algebraic dimensionality of the fitness function. Taking the FANUC CRX-10iA collaborative robot as an example and using the improved CMA-ES algorithm to verify the fitness function of disconnection and reconstruction, the simulation results showed that in the solution of point-to-point inverse kinematics under the same conditions, the single average solution time was 0.0040s, the fitness function value was stable at the order of 10-6, and the iteration convergence times was stable at about 25 times. In order to verify the effectiveness of this method in environments with different levels of complexity, continuous trajectory tracking was performed in two-dimensional and three-dimensional spaces, the average time for a single time was 0.0068s and 0.0102s, respectively, the position error was stable at the level of 10-7m, and the motion curve of each joint was smooth. Taking REBot-V-6R robot of six-DOF as the experimental object, the MFC experimental platform was created based on VC++6.0, and continuous space trajectory tracking experiments were carried out. Simulation and experimental results verified the effectiveness of the method.

Abstract:To meet the requirement of high-performance 3D printing, a three-translational (3T) parallel manipulator with simple structure and high efficiency was proposed. Firstly, in order to obtain the desired degree of freedom configuration, a topological evolution design method was proposed, that was, a parallel mechanism with 3-DOF translations and 1-DOF rotation (3T1R) was taken as the initial configuration. According to the screw theory, the degree of freedom and its properties of the mechanism were analyzed. On this basis, the moving platform was infinitely converged to a point, and the end connecting rods of the three branches were hinged at this point. Then the end hinged rod of the mechanism was redesigned. Thus, a three-translational parallel mechanism with only revolute pairs and end hinged structure was obtained. Secondly, based on the relationship between the input joint and the end articulated point, the kinematic equations of the parallel mechanism were constructed by using the geometric projection method and the closed-loop vector method, the position forward/reverse solutions were derived, and the velocity Jacobian matrix was further obtained, and then the velocity and acceleration models were formulated. Subsequently, the hierarchical slicing search algorithm was employed to predict the workspace of manipulator, and then the singularities were analyzed. To comprehensively evaluate the performance of manipulator, the dexterity, speed, load carrying capacity and stiffness performance indexes of the mechanism were analyzed, and then a multi-objective optimization design model was proposed, and the optimal dimension parameters were obtained through dimensional synthesis. Finally, trajectory tracking simulation verification was carried out by using multi-body dynamics software. The research results manifested that the parallel manipulator had a simple and compact structure and excellent performance, which laid a theoretical foundation for its subsequent prototype manufacturing and practical application in future.