Design and experiment of a picking robot for Agaricus bisporus based on machine vision

Harvesting represents the crucial stage in the cultivation process of Agaricus bisporus mushrooms. An important way for the production process of Agaricus bisporus to reduce costs and increase income is to ensure timely harvest of Agaricus bisporus, reduce harvesting costs, and improve harvesting efficiency. There are many disadvantages in manual picking, such as high labor intensity, time-consuming work and high cost. In this study, a set of mushroom picking platform including climbing mechanism, picking robot, and control system was designed and developed. The picking robot consisted of a truss mechanism, an image acquisition device, a mushroom collection device, and a picking actuator. The profile picking actuator could realize the function of constant force clamping. An online size detection algorithm for Agaricus bisporus based on deep image processing was proposed. The algorithm included removal of abnormal noise points, background segmentation, coordinate conversion, and diameter detection. The precision picking system for Agaricus bisporus with coordinate compensation function controlled by Industrial Personal Computer was designed, and the visual control interface was developed based on Labview. Through the performance test, the reliability of machine vision recognition and the overall operating stability of the picking platform were verified. The test results showed that in the process of machine vision recognition, the recognition accuracy rate was higher than 92.50%, the missed detection rate was lower than 4.95%, the false detection rate was lower than 2.15%, and the diameter measurement error was less than 4.50%. The image processing algorithm had high recognition rate and small diameter measurement error, which could meet the requirements of picking operation. The picking platform’s picking success rate was higher than 95.45%, the picking damage rate was lower than 3.57%, and the picking output rate was higher than 87.09%. Compared with manual picking, the recognition accuracy rate of the picking platform was increased by 6.70%, the picking output rate was increased by 1.51%. The overall performance of the picking platform was stable and practical.