A Multilingual Offline AI-based Crop Disease Diagnosis and Treatment Advisor using CNN and Native-Language Voice Assistance

A Multilingual Offline AI-Based Crop Disease Diagnosis and Treatment Advisor Using CNN and Native-Language Voice is an artificial intelligence-based system designed to identify crop diseases from leaf images and provide treatment recommendations. The system aims to assist farmers in rural areas by offering accessible disease diagnosis and advisory services. The agriculture industry requires the application of AI as the modern world is more and more in need of the disease diagnostics accessibility in rural India. It assists you in identifying the disease of crops based on leaf images, and also in providing a treatment advice using the native languages by voice synthesis (natural voice). The key objective of this project is to improve the output of farmers and minimize agricultural losses. The implementation is a web based system only that can be accessed on low end devices that have minimal connectivity. To identify disease, convolutional neural networks (CNN) models that are trained with plant pathology data are added to the backend to enable effective inference of the server side with offline capability.