Abstract:Detection of incisions on natural rubber trees and identification of rubber tapping lines are of great significance for achieving automated tapping and enhancing the intelligence of operations. In response to the problem that the recognition of incisions and the identification of tapping lines by tapping machines are easily disturbed by the natural environment under complex conditions, leading to incomplete incision recognition and inaccurate tapping line fitting, a method for detecting incisions and identifying tapping lines on rubber trees in complex environments based on an improved UNet was proposed. To address the issue of low recognition efficieney of the UNet model, the VGG16 downsampling module in the original UNet was improved to ChostNet, reducing the model's parameter quantity, improving the model's accuracy and detection efficiency; the improved SegNext attention mechanism was integrated to optimize the model's segmentation effect on smaller features; and an improved feature pyramid network (FPN) decoding architecture was introduced to enhance the model's ability to capture semantic information. Secondly, after detecting the target incision, the Shi - Tomasi algorithm was used, with the minimum eigenvalue of the matrix as the corner response function, and non-maximum suppression was introduced to select the best starting and ending points of the tapping line. Finally, the random sample consensus (RANSAC) algorithm was used for multiple iterations to select the lower edge points of the incision and then fit the precise tapping line. The experimental results showed that the average intersection over union and average pixel accuracy of the improved UNet model vere 92. 86% and 96. 96%, respectively, which were 1.38 and 0. 52 percentage points higher than those of the original UNet model. In addition, the model's memory usage was compressed to 18. 55 MB through structural optimization, a reduction of 80. 46% compared with that of the original UNet. The average positioning error of the starting point by the Shi - Tomasi cormer detection algorithm was 10. 2 pixels, and the average positioning error of the ending point was 8. 99 pixels, which can effectively detect the starting and ending points of the incision. The average inlier mean square error of the fitted line by the RANSAC algorithm was 4. 26 pixels2, and the average inlier root mean square error was 1. 86 pixels. The research results can provide a reference for the detection of incisions and identification of tapping lines in complex environments in rubber plantations using intelligent equipment.
