Multi-objective optimization of seeding performance of a pneumatic precision seed metering device using integrated ANN-MOPSO approach

Abstract

Uniform seed spacing within the row is the most desirable prerequisite for better crop yield. The seeding uniformity of a pneumatic seed metering device is significantly affected by its key design and operational parameters, i.e., shape and size of the suction hole, vacuum pressure, and forward speed of operation. Therefore, these parameters need to be optimized to achieve better seeding performance. In this study, A novel intelligent multi-objective optimization methodology based on artificial neural network (ANN) and multi-objective particle swarm optimization (MOPSO), named as integrated ANN-MOPSO approach, was employed to accomplish the set goal. Maximizing the quality feed index (QFI) and minimizing the precision index (PI) were chosen as two objectives. The multilayer perceptron (MLP) and radial basis function (RBF) neural network models were developed for predicting the QFI and PI. The results revealed that the RBFNN (4-15-2) model outperformed the MLPNN (4-7-2) model; hence it was further coupled with the MOPSO algorithm for retrieving the Pareto-optimal set of the design and operational parameters corresponding to the maximum QFI and minimum PI.The most appropriate optimum entry shape and size of the suction hole, vacuum pressure, and operating speed were found to be chamfered holes with 3 mm diameter, 3.5 kPa, and 2.84 km/h, respectively. The validation results showed a variation of -2.11 % and +5.398 % between the observed and predicted values of the QFI and PI, respectively, thus confirming the adequacy of the proposed approach.