SOYBEAN PEST IDENTIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK AND TRANSFER LEARNING

Abstract

Despite the fact that the ensemble classifier improved classification precision by integrating texture and colour. However, the significant image preparation process is a laborious and time-consuming. Manually depicting constrained feature extraction can result in a semantic void in a picture. These limits cause inaccuracy of agricultural disease identification. Thus, this study proposes a soybean pest detection method based on a hybrid of Transfer learning and pyramidal convolutional neural networks that can identify soybean pests quickly and accurately on small sample sets. Bean borer, soybean poison moth, mite, stink bug, and pod borer photographs are first pre-processed using standard data improvement methods, and then manually categorized into six groups based on pest characteristics. The weight parameters from the VGG16 model trained on the ImageNet image dataset were then transferred to the recognition of soybean pests using the transfer learning method, and the VGG16 convolutional and pooling layers were used as feature extraction layers, while the top layer was redesigned as a pyramidal convolutional layer, an average pooling layer, and a SoftMax output layer, with some of the convolutional layers frozen during training. According to the testing statistics, the model's average test accuracy is 98.23%, and the model size is only 95.4 M. For bean borer, soybean poison moth, mite, skewed night moth, stink bug, and bean pod moth, the model's recognition accuracy is 96.4, 97.78, 98.12, 98.4, 99.56, and 99.16, respectively. The results of the experiments show that the method has a high identification efficiency and a good recognition effect.