PPLC-Net:Neural network-based plant disease identification model supported by weather data augmentation and multi-level attention mechanism

Abstract

The accurate detection and identification of plant diseases is an essential step in the development of intelligent and modernized agricultural production. This study proposes a deep learning model (PPLC-Net) incorporating dilated convolution, multi-level attention mechanism, and GAP layers. The model uses novel weather data augmentation to expand the sample size to enhance the generalization and robustness of feature extraction. The feature extraction network extends the perceptual field of the convolutional domain using sawtooth dilated convolution with a variable expansion rate, which can effectively address the problem of insufficient spatial information extraction. The lightweight CBAM attention mechanism is located in the middle layer of the feature extraction network. It is used to enhance the information representation of the model. the GAP layer prevents over-fitting of the model by reducing the number and complexity of parameters computed by the network. The validation of the retained test dataset shows that the recognition accuracy and F1 score of the PPLC-Net model are 99.702% and 98.442%, and the number of parameters and FLOPs are 15.486 M and 5.338G, respectively, which can meet the requirements of accurate and fast recognition. In addition, the proposed combined CAM visualization method can fully validate the effectiveness of the proposed model.

Keywords

• Convolutional neural network
• Dilated convolutions
• Global average pooling
• Attention mechanism (CBAM)
• Weather data augmentation
• Leaf disease recognition