AgroShield: Plant Disease Detection and Organic Recommendation using Deep Learning

Crop health management remains a major challenge in modern agriculture, particularly for small and marginal farmers who lack access to agricultural experts, diagnostic facilities, and reliable digital advisory tools. Early identification of plant diseases is essential to prevent yield losses, yet farmers often rely on guesswork or delayed consultation, resulting in ineffective or harmful treatment decisions. Existing digital solutions depend heavily on cloud-based machine learning and chemical-centric recommendations, increasing farming costs, degrading soil health, and making them unsuitable for low-connectivity rural regions. To overcome these limitations, this research proposes AgroShield, a mobile-first, AI-powered crop health scanner engineered for real-world rural environments. AgroShield utilizes an Ultra-Lightweight Efficient Network (ULEN) to perform fast, fully offline leaf disease, pest, and nutrient-deficiency detection directly on low-end smartphones. The system eliminates internet dependency, reduces latency, and ensures consistent performance through on-device inference. Beyond diagnosis, AgroShield integrates an organic remedy recommendation engine offering region-specific, eco-friendly treatments sourced from credible institutions such as ICAR, FAO, and Krishi Vigyan Kendras. The platform further enhances accessibility through multilingual support and intuitive visual guidance, with optional IoT soil sensor integration to enable context-aware recommendations. Initial evaluations demonstrate that AgroShield delivers high classification accuracy across diverse disease categories while maintaining exceptional computational efficiency, enabling inference within milliseconds after image capture. By combining lightweight offline AI, sustainable treatment guidance, and affordability, AgroShield presents a scalable and inclusive solution for improving crop health management. The system directly contributes to global goals of sustainable agriculture, climate resilience, and ethical AI deployment in underserved farming communities.