Precise Selection and Visualization of Maize Kernels Based on Electromagnetic Vibration and Deep Learning

Abstract

Abstract. With the development of precision agriculture, the selection of maize kernels has gained more importance in scientific research and practical significance in agricultural production. In this study, the deep learning technology of machine vision was used to select maize kernels, solving the problems of previous maize kernel selection for specific sorting problems, the cumbersome process of artificial feature modeling, the problem of a small number of features, and the challenge of limited data. First, the maximum size of a model based on convolutional neural networks (CNNs) that could run under finite hardware conditions was determined by experiments. Four different network models (Faster R-CNN, Model 1.0, Model 2.0, and Model 3.0) were then designed and trained using a data set of maize kernels. Finally, the accuracy of the models was verified by comparison test, and the detection results of the models were analyzed according to their precision, recall, FPR, F1, precision-recall curve, average precision (AP), mean average precision (mAP), and detection speed. The results show that for the validation set not used for training, Model 1.0 had the highest average recall rate of 98.42% among the four models. Without taking into account the identification of the removed kernels, only excellent maize kernels were identified, and the mAP of Model 1.0 was as high as 97.27%. Moreover, Model 1.0 requires less computer resources, and its computer hardware requirement is lower. The precision, recall, and F1 value of Model 2.0 were increased by 3.73%, 3.55%, and 3.79%, respectively, and the false positive rate of Model 2.0 was reduced by 1.31% on average compared with the Faster R-CNN model. By comparing Model 1.0, Model 2.0, and Model 3.0, it was found that the overall performance of Model 2.0 was best. The size of the network model has an effect on the accurate selection of maize kernels, and a moderate-size model is the best. This study laid a good foundation for the further application of deep learning technology in the real-time sorting of maize kernels and additional applications in the field of agriculture. Keywords: Convolutional neural networks, Deep learning, Maize kernel, Selection, Visualization.