Spatial attention-guided pre-trained networks for accurate identification of crop diseases

The maintenance of agricultural productivity is critically dependent on the efficient and accurate identification of plant diseases. As observed, the manual inspection to the illness is often inefficient and error-prone, particularly under conditions such as inconsistent lighting, leaf deformities, and subtle distinctions between disease symptoms. To address these challenges, we introduce an enhanced crop disease classification framework that incorporates EfficientNet-B3 with an ancillary convolutional layer and a spatial attention module (ACSA). EfficientNet-B3 offers a strong foundation for feature extraction due to its compound scaling and efficient computation, while the spatial attention module improves classification accuracy by directing the model to focus on critical regions of diseased leaves. Additionally, the integration of ancillary convolutional layer to this architecture enhances the ability of the model to detect subtle disease variations. To further improve the adaptability, the proposed method incorporates a preprocessing and data augmentation techniques. Together, these enhancements create a more effective process for identifying disease pattern in wide range of plant species. The model was evaluated using an extensive crop disease dataset and against state-of-the-art methods such as EffiNet-TS, PlantXViT, and MobileNet V2 to assess its effectiveness. The proposed approach achieved an accuracy of 99.89% and a recall rate of 99.87%, demonstrating its suitability for crop classification with minimal computational overhead. Ablation studies further validate the significant contributions of the spatial attention module and the ancillary convolutional layer to the overall performance of the proposed model.