Lightweight Edge-AI Model for Real-Time Sorghum Disease Detection on Resource-Constrained Mobile Devices

Lightweight Edge-AI Model for Real-Time Sorghum Disease Detection on Resource-Constrained Mobile Devices presents an efficient artificial intelligence approach for identifying sorghum diseases directly on mobile and edge-computing platforms. The study focuses on developing a lightweight machine learning model capable of performing rapid and accurate disease detection while operating under limited computational resources, memory, and power constraints. Computer vision and deep learning techniques are used to analyse sorghum leaf images and distinguish healthy plants from diseased conditions. Model optimization strategies are considered to reduce computational complexity and improve inference speed without substantially compromising detection performance. The proposed approach supports real-time, on-device disease diagnosis, reducing dependence on cloud connectivity and enabling timely intervention in agricultural environments. The work highlights the potential of Edge AI and mobile computing to make intelligent crop monitoring more accessible, practical, and scalable, particularly for farmers and agricultural users operating in resource-constrained settings.