AI Driven Agriculture-Predictive Analytics&Climate Smart Agriculture

Farming in the modern world is faced with the challenge of producing sufficient food with fewer wastes of water, fertilizer and crop losses. A recently developed AI-based technology named Crop Health and Disease Management can assist farmers and detect diseases and yield forecasts through computer vision and detailed predictions. It consists of three major components namely Crop Recommendation, Nutrient Analysis and Plant Disease Detection. The system involves a number of machine-learning models, which include Random Forest, XG Boost, SVM, the Decision Trees and the Naive Bayes to examine variables including fertilizer levels, temperature, and humidity and provide the recommendations on each field. It also possesses an intelligent nutrient module illustrating them as to how to improve soil well-being and PyTorch deep-learning apparatus telling farmers how to treat and prevent plant illness. It is effective in 92% test and its precision is 90%. The solution located in a Flask web application is simple to use, scalable, and assists farmers to increase in a more sustainable manner.

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