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September 10, 2025Scientific Reports43 citationsOpen Access

A lightweight and explainable CNN model for empowering plant disease diagnosis

CPChiranjit PalGuru Gobind Singh Indraprastha University SKSougata KarmakarIndian Institute of Technology Guwahati IMImon MukherjeeIndian Institute of Information Technology Allahabad

Puntos clave

Mob-Res achieved 97.73% accuracy on the Plant Disease Expert dataset and 99.47% on PlantVillage.
The model's lightweight design only consists of 3.51 million parameters, enabling efficient mobile application use.
Utilizing techniques like Grad-CAM and LIME, Mob-Res enhances interpretability, providing insights into its predictions.
The model shows competitive cross-domain validation rates, outpacing existing pre-trained CNN architectures while reducing computational costs.

Resumen

Crop disease is a significant challenge in agriculture, requiring quick and precise detection to safeguard yields and reduce economic losses. Traditional diagnostic methods are slow, labor-intensive, and rely on expert knowledge, limiting scalability for large-scale operations. To overcome these challenges, a novel architecture called Mob-Res, combining residual learning with the MobileNetV2 feature extractor, is introduced in this work. Despite having only 3.51 million parameters, Mob-Res is lightweight and well-suited for mobile applications while delivering exceptional performance. The proposed model is assessed using two benchmark datasets: Plant Disease Expert, consisting of 199,644 images across 58 classes, and PlantVillage, with 54,305 images across 38 classes. Through a rigorous training strategy, Mob-Res demonstrates robust performance, achieving 97.73% average accuracy on the Plant Disease Expert dataset and 99.47% on the PlantVillage dataset. The cross-domain validation rate (CDVR) is computed to assess its cross-domain adaptability, with the model showing competitive results compared to other pre-trained models. Additionally, Mob-Res outperforms prominent pre-trained CNN architectures, surpassing ViT-L32 while maintaining a significantly lower parameter count and achieving faster inference times. The proposed model enhances interpretability by utilizing Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++, and Local Interpretable Model-agnostic Explanations (LIME). These techniques provide visual insights into the neural regions influencing the predictions. The experimental results conducted in the current work highlight Mob-Res as a promising solution for automated plant disease detection, supporting large-scale agricultural operations and advancing global food security.

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