An Efficient Depthwise Multiscale Feature Learning Convolutional Network for Plant Leaf Disease Classification in Agriculture

Plant disease detection and early disease treatment are essential for sustainable crop production. Computer vision for crop science is growing with the advancement in deep learning. The proposed work systematically addresses these issues through three datasets as Plant Village Maize Dataset (D1), Paddy Doctor Dataset (D2), and Sugarcane Leaf Image Dataset (D3) with different classes. The dataset contains 4188, 16225, and 6748 images from dataset sets D1, D2, and D3, respectively. This work has used a Generative Adversarial Network (GAN) to generate a synthetic dataset. Further use data preprocessing, and the data has been resized to 224×224×3. The proposed model use Depth-wise Multiscale Feature Learning ConvoNet (DMFL-ConvoNet) model, which includes the Depth-Wise Convolutional Block (DCB ) block of DMFL-ConvoNet with 3 × 3 and 5 × 5, facilitates the extraction of multiscale plant disease characteristics. Furthermore, it has added 2.5 million parameters. The proposed DMFL-ConvoNet model offers state-of-the-art performance and decreases computational complexity at 33 frames per second, making it ideal for real-time applications. The proposed DMFL-ConvoNet model has been compared with several transfer learning models, including ResNet50V2, InceptionResNetV2, NASNetMobile, EfficientNetV2L, and EfficientNetV2B0 models, and the proposed model has achieved 99.52% data accuracy in the multiple datasets.