Edge optimized multimodal cross fusion model with statistical validation for multi crop disease detection

Abstract

Accurate and timely crop disease detection is crucial for mitigating agricultural losses and ensuring food security, particularly in resource-limited settings. Traditional diagnostic methods are inefficient and prone to errors, while existing deep learning models, such as ResNet50 and Inception V3, struggle with generalizability and computational efficiency. This study proposes a Dynamic Edge-Optimized Multimodal Fusion (DEMF) model, integrating EfficientNetV2 and MobileNetV2 to enhance feature learning and scalability. The model was trained on a 76-class dataset comprising PlantVillage and locally collected images of crop diseases, ensuring robustness across diverse conditions. Feature fusion via concatenation, combined with compound scaling and transfer learning, enabled the model to capture fine-grained patterns of disease. Extensive experiments, including ablation studies and comparative evaluations against DenseNet-121, DenseNet-50, AlexNet, and ResNet-152, validated the model’s superiority. The proposed model achieved 99.2% accuracy, a Kappa of 0.9919, and an AUC of 0.9999, outperforming benchmarks. Statistical validation confirmed significant improvements (p<0.05p<0.05) and stability. To enhance accessibility, an AI-powered mobile application was deployed on the Google Play Store, enabling real-time disease detection and actionable recommendations. To improve accessibility, an AI-powered mobile application was developed and deployed on the Google Play Store, enabling real-time disease detection and actionable recommendations. This research advances transfer learning, feature fusion, and statistical validation for robust, scalable crop disease detection in low-resource environments.