Crop Disease Detection for Vietnamese Farmers

Agriculture plays a central role in Vietnam’s economy, with millions of smallholder farmers depending on healthy crops for their livelihood. One of the most pressing challenges faced by these farmers is the timely identification of crop diseases. Traditional methods rely heavily on expert consultations, which are often inaccessible in rural regions, leading to delays in diagnosis, crop losses, and reduced yields.

This project addresses the problem by developing an AI-powered crop disease detection system tailored for Vietnamese farmers. Using image recognition and machine learning, specifically convolutional neural networks (CNNs), the system can analyze photographs of crop leaves taken by a smartphone and provide real-time predictions of potential diseases. The mobile application will present results in Vietnamese, offering disease details and practical treatment recommendations. Importantly, the system is designed to function offline, ensuring accessibility even in areas with poor internet connectivity.



Beyond practical benefits for farmers, the project contributes to research on applying lightweight deep learning models, such as MobileNet, in low-resource agricultural settings. By bridging technology and agriculture, this project not only aims to reduce crop losses and improve food security but also demonstrates how AI solutions can be localized and adapted to the unique needs of developing countries.