A novel sowing operation parameter learning optimization method using dataset of sown seeds with similar properties

Abstract

A learning optimization method used for the pneumatic roller-type seeder was put forward to determine optimal sowing operation parameters of a new type of seed without conducting performance tests. A feedforward neural network structure with two hidden layers were firstly optimized with Genetic Algorithm (GA) to establish predictive relationships among sowing operation parameters and performance indices. Then particle swarm optimization (PSO) was utilized to search the optimal sowing operation parameters for the new type of seed. Sowing data of ten types of seed from previous study with the 2BS-6 pneumatic roller seeder were gathered as dataset of sown seeds. Cosine similarity of seed physical properties was analyzed to extract training datasets for eggplant and mustard. The lowest R-value of 0.85 and the largest MAE value of 4.38 were obtained by regression analysis of the eggplant and mustard predictive model. The smallest R2 of 0.731of optimized sowing performance indices were reported. Optimized fitness values were 83.15% for eggplant and 87.44% for mustard. Fitness value deviations of eggplant and mustard were 6.51% and 1.58% respectively. For eggplant, experimental deviations of single-seeding rate, multi-seeding rate and miss-seeding rate were 2.1%, 2.39% and 0.29% respectively. All experimental deviations of mustard were less than that of eggplant. The study demonstrated high predictive accuracy, strong optimal ability, and promising applicability of the proposed method to determine sowing operation parameters efficiently for the pneumatic roller-type seeder.