AI-BASED CROP RECOMMENDATION FOR FARMERS

Authors:

1. Anurag Atram 2. Shreyash Kapse 3. Ayush Khonde 4. Parth Nalkande 5.Prof. D.N. Aswar Department of Computer Engineering, PVPIT, Bavdhan, Pune, India

Abstract- The growing need for better agricultural productivity, along with challenges like soil variability, unpredictable climate, and a lack of data-driven decision- making, has made it tough for farmers to choose the right crops. Many traditional farming methods lean more on experience than on scientific analysis, which can lead to wasted resources and lower crop yields. To tackle these issues, this research introduces an AI-powered Smart Crop Recommendation and Agricultural Assistance System designed to improve decision-making through machine learning and real-time analysis.

This innovative system takes into account essential agricultural factors such as Nitrogen (N), Phosphorus (P), Potassium (K), pH levels, temperature, humidity, rainfall, and soil type to suggest the best crop options. It employs machine learning models like Decision Tree and Random Forest algorithms to analyze the data and provide accurate predictions. The system is built as a web application using Flask, offering an interactive interface where users can enter their data and receive crop recommendations, complete with confidence scores and estimated profitability.

Beyond just predicting crops, the system also includes helpful features like weather data retrieval, chatbot assistance, and community interaction to enhance usability and support for farmers. It ensures real-time responses, easy access, and scalability, making it ideal for practical use.

By merging artificial intelligence with a user-friendly design, this solution aims to boost agricultural productivity, minimize risks linked to poor crop choices, and encourage the adoption of smart farming techniques. The system presents a cost- effective and scalable pathway toward modern precision agriculture.