西兰花选择性采收机器人成熟度识别与自主采收方法研究

Maturity Recognition and Autonomous Harvesting Method of Selective Broccoli Harvesting Robot

摘要:

西兰花选择性采收的整个过程需要精确的成熟度识别、采摘姿态调整以及快速的作物行导航,但目前缺少一种完成全流程作业的视觉算法。为此,本研究提出了高效采收视觉系统EH-YOLO,旨在实现复杂农业环境下的准确成熟度识别和自主采摘,提高不同任务之间的协同性。本研究首先在YOLO v8n主干网络中加入了高效多尺度注意力(Efficient multi-scale attention)模块,并设计了空间金字塔池化(Soft spatial pyramid pooling-fast)模块,其次采用基于分组混合卷积(Grouped shuffle-convolution)的轻量化颈部结构,改进了损失函数,以增强特征提取和融合能力、降低计算复杂度,并提高模型在复杂环境中的性能。最后,通过分析作物的农艺特征,利用西兰花花球的中心点进行植株定位,根据西兰花所处的象限调整采收姿态,并利用聚类算法和最小二乘法提取作物行导航线。模型内存占用量为3.30MB,检测速度达到61.45f/s,对成熟西兰花的成熟度识别准确率达到92.84%,在精度和速度方面均超越了现有方法,并通过消融试验验证了改进模块的有效性。此外,与基线模型相比,EH-YOLO的计算参数量减少了38.68%,模型内存占用量减少了46.77%,平均准确率提高了4.01百分点,在田间试验中成功进行了全程自主作业,运动和定位成功率为96.77%,选择性采收成功率为87.37%。因此,EH-YOLO是一种实用且高效的西兰花选择性采收机器人视觉系统,对西兰花农田环境具有较好的适用性。

Abstract:

The entire process of selective harvesting of broccoli requires precise maturity recognition, harvesting posture analysis, and rapid crop row navigation. However, at present, there is a lack of a visual algorithm suitable for the full-process operation. To address these issues, an efficient harvesting visual system, EH-YOLO, was proposed for the selective harvesting robot of broccoli, aiming to achieve accurate maturity recognition and autonomous harvesting in complex agricultural environments. The efficient multi-scale attention module was incorporated into the YOLO v8n backbone network, and a soft spatial pyramid pooling-fast module was designed. Additionally, a lightweight neck structure based on grouped shuffle convolution was employed, and the loss function was improved to enhance feature extraction and fusion capabilities, reduce computational complexity, and improve model performance in complex environments. Furthermore, by analyzing the crop??s agronomic features, the plant location was determined based on the center point of the broccoli head. The harvesting process was adjusted according to the quadrant in which the broccoli was located, and clustering algorithms, combined with the least squares method, were used to extract the crop row navigation lines. The model size was 3. 3 MB, and the detection speed reached 61. 45 f/ s. The accuracy of maturity recognition for mature broccoli was 92. 84% . Additionally, compared with the baseline model, EH-YOLO reduced the computational parameters by 38. 68% , decreased the model size by 46. 77% , and increased the average accuracy by 4. 01 percentage points. In field trials, the system successfully completed the full-process selective harvesting task, with a motion and positioning success rate of 96. 77% and a selective harvesting success rate of 87. 37% . Therefore, EH-YOLO was a practical and efficient visual system for the selective harvesting robot of broccoli, with strong applicability for commercial broccoli fields.