Fine-grained Classification of Plant Diseases and Pests Based on Transfer Learning
Identification of plant diseases and pests is an important issue in agricultural production.There are certain limitations in the practical application of manual recognition and traditional computational vision methods,so it is necessary to study plant disease and pest classification algorithms based on convolutional neural networks.In order to explore the application of convolutional neural networks in fine-grained classification of plant diseases and pests,based on existing partial plant disease and pest data image data,a discriminative region attention mechanism and in-ter channel attention mechanism model were fused to evaluate this dataset.The results showed that the classification performance was improved compared to the original model after fusing the attention mechanism,with a Top-1 accu-racy increase of 1.9％.In order to further explore the impact of attention mechanisms on convolutional neural net-works,the regions of attention of convolutional neural networks were visualized.The results showed that attention mechanisms make the network more focused on the discriminative feature regions of the target in fine-grained classi-fication tasks.
Convolutional neural networkAttention mechanismTransfer learningFine-grained classificationPlant disease and pest
万方数据
维普
