Title: CropRec: AI-Based Crop Recommendation System for Smarter Farming


Authors: Avi Kedare, Ayush Patel, Dheer Panchal, Ayush Chourasiya, Nisha Rathi, Ashish Anjana


Published in: Volume 3 Issue 1 Jan June 2026, Page No. 76-79


DOI: 10.63844/IJAITR.v3.i1.2026.76-79 cite


Keywords: AI crop recommendation, machine learning, smart farming, sustainable agriculture, soil analysis, weather-based prediction, market trend analysis, tree-based models, ensemble learning methods, deep learning techniques, MERN technology stack.


Abstract: Farming today faces many challenges and often rely on traditional knowledge or personal experience. But this approach may not work well with changing weather, soil conditions, or market trends. Studies show that a 1 °C rise in temperature can reduce crop output value by up to 21%, while rainfall and temperature together account for over 30% of yield variation worldwide. However, many farmers — especially in developing regions — still depend on old, experience-based methods to choose crops. CropRec is an AI-powered crop recommendation system designed to solve this problem by providing data-driven guidance. It uses machine learning techniques such as decision trees, random forest models, and neural networks to study inputs such as soil type, previous crop performance, live weather data, and market trends. The platform offers a simple and user-friendly web interface where farmers can provide their soil and location details to instantly get smart crop suggestions. It connects with real-time APIs to ensure up-to-date suggestions. By removing guesswork, CropRec allows farmers to improve yield, save resources, and follow sustainable farming practices. The system shows how AI can bring smart, climate-ready, and profitable solutions to agriculture. Our proposed system believes in “Take the guesswork out of farming”. CropRec helps farmers grow the right crops, at the correct time with intelligent AI support.


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