Sugarcane mechanized planting technology consists of seed preparation and field planting. This study aims at the issues of easy damage to the seeds during the operation of the automatic cutting machine for single-bud segment sugarcane, lack of intelligent seed selection and calibration technology, low recognition accuracy, and the need for manual feeding of the planting machine’s seed meter which leads to seed leakage. This study, based on machine vision and deep learning, optimizes the seed calibration method and proposes an improved YoloV5-STD target detection algorithm to improve the recognition accuracy of seed characteristics and optimize the overall engineering structure. For the planting machine, a new type of hopper for the seed meter is designed using natural rubber as the base material mixed with polystyrene, and the flexible automatic seed metering mechanism is analyzed to achieve automatic feeding and seed metering. Test assessment indicators were formulated based on the enterprise standards of the Institute of Agricultural Machinery Research, Chinese Academy of Tropical Agricultural Sciences. Experimental results show that the recognition accuracy of the 2DZ-2 type single-bud segment intelligent cutting machine is ≥95%, the bud injury rate is <1.8%, the qualified rate of cutting is 95.8%, and the single-channel cutting efficiency is 64 buds/min. The 2CZD-2C type single-bud segment planter has a planting qualification rate of 96.6%, a planting efficiency of 208 buds/min, and a seed leakage rate of <2.1%.

To improve the survival rate of larvae during material separation after biotransformation of existing residual film mixtures of Protaetia brevitarsis larvae, this paper adopts the method of combining physical test and EDEM simulation test, and selects Hertz Mindlin with JKR contact model to calibrate the discrete element simulation contact parameters of the Protaetia brevitarsis larvae and the frass mixture. First, the cylinder lifting method was used to determine the actual repose angle of the mixture of larvae and frass. The collision recovery coefficients between larvae-frass and steel, static friction coefficient, kinetic friction coefficient and the collision recovery coefficient between larvae were measured through physical tests such as the inclined plane method. The Plackett-Burman test was then used to screen out the factors that have a significant impact on the repose angle: Poisson’s ratio of frass, frass-frass rolling friction coefficient, frass JKR surface energy, frass-larvae JKR surface energy. The optimal value intervals of four significant factors were determined based on the steepest climb test, Based on the Box-Behnken response surface analysis test, the second-order regression model between the repose angle and four significant factors was determined, and variance and interaction effects were analyzed. And with the actual repose angle as the goal, the significant factors were optimized and the optimal parameter combination of the four significant factors was determined. The simulation test of material repose angle and screening was carried out with the optimal parameter combination, and compared with the physical test. It was found that the maximum relative errors of the two tests were 1.48% and 3.79% respectively, indicating that the calibrated parameter values are true and reliable, It can provide a reference for the discrete element simulation of the transportation and separation of the Protaetia brevitarsis larvae-frass mixture.