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.

Experiments and modeling of mechanism analysis of maize picking loss

Maize picking is the main form of maize harvest in China.Maize picking loss accounts for a large proportion of the current maize harvest loss.An experimental study and a theoretical analysis were conducted to explore the influencing factors and rules of maize picking loss.First,the boundary conditions,established by analyzing the mechanism of maize picking,determined the influences of maize picking loss.Then,single-factor experiments and a central composite design(CCD)method were used to determine the influence of various factors and their interactions on maize picking loss.Finally,the models of kernel loss and ear loss were set up to determine the optimal parameter combination of maize picking harvest.Field experiment verification was conducted.The results indicated that the optimal parameters of the maize picking harvest were the rotational speed of pulling rollers of 1120 r/min,operating speed of 1.94 m/s,the inclination of the header of 18°and clearance between the picking plates of 30 mm.By establishing these optimal parameters,the kernel loss rate was 0.065%,and the ear loss rate was 0%.The obtained experimental results and regression models could be used to predict the performance of the maize picking harvest,guide the adjustment of header working parameters,and provide a theoretical basis for reducing the mechanical loss of maize harvesting.