A lightweight hybrid model for scalable and robust plant leaf disease classification

Plant leaf diseases significantly impact crop yield and quality, causing substantial economic loss and risking food security. Despite significant progress in the field of automated plant disease diagnosis, there are still several challenges that need to be addressed. Accurate classification of plant leaf diseases at an early stage is crucial for diagnosis and effective treatment of these plant diseases. As the agricultural industry faces growing challenges from plant diseases, quickly identifying these diseases in a field environment while considering the computational resource limitations is more important than ever. To overcome these challenges, this study proposed a lightweight and compact convolutional neural network model, HPDC-Net (Hybrid Plant Disease Classification Network). The network used a block architecture with three blocks termed as Depth-wise Separable Convolution Block (DSCB), Dual-Path Adaptive Pooling Block (DAPB), and Channel-Wise Attention Refinement Block (CARB). The model extracts a robust but limited number of features due to the use of depth-wise separable convolutions in DSCB, making it accurate but lightweight. The proposed model has been trained to classify potato and tomato leaf diseases on three datasets. The model achieves a high accuracy score > 99% on all three datasets while keeping GFLOPs limited to 0.06 and the number of parameters to 0.52 M (for 10 classes) and 0.17 M (for 03 classes), yielding 19.82 FPS on CPU and 408.25 FPS on GPU in our setup. The code for implementation of proposed model is available on GitHub: https://github.com/ZahidFarooqKhan/HPDC-Net .