Crop Prediction, Disease Detection and Multilingual Support Using Machine Learning Techniques

This paper presents a comprehensive, machine learning-driven solution to enhance agricultural practices by addressing key challenges such as crop yield prediction, pest and disease detection, and multilingual communication for farmers. A neural network model, with an accuracy of 87.6%, predicts optimal crops based on environmental and soil data, facilitating informed planting decisions. Random Forest, achieving an accuracy of 82.4%, is utilized for early pest and disease detection, helping farmers take preventive measures in time. A notable feature of the solution is the integration of the ChatGPT API, which provides real-time multilingual support, allowing farmers to interact with the platform in their preferred languages. This integration significantly enhances accessibility and usability, bridging language barriers and making advanced agricultural technology more inclusive for farmers across diverse regions. The solution overcomes the challenges posed by fragmented technology platforms and accessibility issues. By integrating crop prediction and pest and disease detection, the paper offers a comprehensive, scalable system aimed at improving agricultural efficiency, boosting productivity, and promoting sustainable farming practices.