RTR_Lite_MobileNetV2: A lightweight and efficient model for plant disease detection and classification

Early identification and management of plant diseases are paramount for sustaining crop health, ensuring optimal yields, and safeguarding food security in agricultural systems. Left untreated, diseases caused by fungi, bacteria, viruses, and pests can significantly diminish agricultural output, posing a threat to global food production. While recent research has explored machine learning-based techniques for early disease detection, many proposed models are resource-intensive, characterized by large model sizes and millions of trainable parameters. Recognizing resource-constrained devices' needs, recent studies have developed lightweight models, but their shallow structure may hinder accurate disease identification. This study proposes the RTR_Lite_MobileNet model, an enhanced version of the original MobileNetV2 model designed for efficient deployment on resource-constrained devices. Different attention techniques, such as Squeeze-and-Excitation Networks (SENet), Efficient Channel Attention (ECA), and Triplet Attention, are added to reduce the model's computational footprint while boosting its ability to capture complicated disease patterns. Extensive experimentation validates the efficacy of RTR_Lite_MobileNet, consistently outperforming MobileNetV2 with top accuracies across multiple datasets: 99.92% on Plant Disease, 82.00% on PlantDoc, 97.11% on PaddyDoctor, 90.84% on Coffee, 100% on Wheat, 96.78% on Soybean, and 96.67% on Sugarcane. Deployment on edge devices such as Raspberry Pi 4 and 5 demonstrates its computational efficiency, as evidenced by lower latency and memory consumption. Research results indicate that RTR_Lite_MobileNet is a practical and effective option for real-time plant disease diagnosis, paving the way for additional uses in agricultural monitoring and IoT systems. • Efficient, lightweight disease detection model for resource-constrained devices. • Utilization of advanced attention mechanisms for improved plant disease detection. • Hybrid attention modules embed lightweight deep learning models for disease identification. • Optimized for low-power devices without compromising disease identification accuracy.