RAI-Net: Tomato Plant Disease Classification Using Residual-Attention-Inception Network
Year: 2025
Type: article
Abstract: Pests and pathogens cause significant losses to tomato crop yield, resulting in billion-dollar economic impact worldwide. Early and accurate detection of tomato plant disease is important for an effective intervention and improved crop yield. In this work, a deep learning-based RAI(Residual-Attention-Inception)-Net model has been proposed for an effective feature extraction by deploying channel attention on the output obtained from fine-tuned ResNet18 model for improved feature extraction. Inception module has been integrated with the ResNet18 model augmented with channel attention module enhances the multi-scale feature analysis capability of the proposed model for tomato plant leaf disease detection task. The interpretability of the proposed network has been improved with the Gradient-weighted Class Activation Mapping (Grad-CAM) method which highlights the regions focused by the proposed RAI-Net model in making its predictions. RAI-Net achieves an accuracy of 97.88% on a test set comprising 4595 images across 10 different classes of tomato disease, demonstrating its effectiveness in automated detection of tomato leaf diseases.
Source: IEEE Access
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4409327453