Kisan Mitra

Agriculture forms the backbone of the economy and provides the primary livelihood for a large section of society, yet farmers often struggle to select the most appropriate crop for cultivation due to soil fertility variations, changing climatic conditions, and unpredictable rainfall. Traditionally, farmers rely on their experience to choose crops, but this approach does not always ensure optimal productivity. To address these challenges, we propose a Crop Recommendation System powered by machine learning and data analytics, which analyzes soil parameters (pH, nitrogen, phosphorus, potassium), climatic factors (temperature, humidity, rainfall), and environmental data to recommend the most suitable crop under specific conditions. By integrating scientific analysis with modern computing techniques, the system improves crop yield, reduces risks of crop failure, and promotes sustainable agricultural practices. The methodology involves collecting soil and climatic datasets, preprocessing them for noise reduction and normalization, and applying algorithms such as Decision Trees, Random Forest, and Naïve Bayes to build predictive models. An interactive web- or mobile-based interface will enable farmers to input data and receive crop recommendations, and the system can also extend to fertilizer suggestions and irrigation guidance, making it a comprehensive farming assistant. Ultimately, this project aims to deliver a functional, intelligent, and data-driven chatbot that assists farmers in scientific decision-making, thereby enhancing agricultural productivity, profitability, and sustainability.

Built With

• ai
• css3
• html5
• javascript
• ml
• openai