AI-BASED CROP RECOMMENDATION SYSTEM FOR FARMERS
DOI: 
SHARE THIS ARTICLE
DOWNLOAD ARTICLE
REVIEW REPORT
CITE THIS ARTICLE
The CropXo System is an AI-powered crop selection system whose primary aim is to help farmers choose the best crop that can maximize their profits and yield per season. The system takes into consideration various essential elements such as the type of soil, climate conditions, prices in the market, and specific requirements from the farmers like budgets, land sizes, and risks tolerance levels.
By applying sophisticated machine learning algorithms, the system makes predictions concerning the yield, profit margins, and risks involved in cultivating each crop. In summary, the system ranks the crops based on the above parameters and gives an explanation and recommendations about how to farm each crop.
Keywords
Article Information
Article Metrics
HOW TO CITE
Copy
Bordolai, P., Bag, J. & Dabi, K. (2026). AI-Based Crop Recommendation System for Farmers. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.304
Bordolai, Priti, et al.. "AI-Based Crop Recommendation System for Farmers." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.304.
Bordolai, Priti,Jit Bag, and Kalyani Dabi. "AI-Based Crop Recommendation System for Farmers." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.304.
References
[2]Rajak, R. K., Pawar, A., Pendke, M., Shinde, P., Rathod, S., & Devare, A. (2017). Crop recommendation system to maximize crop yield using machine learning technique. International Research Journal of Engineering and Technology, 4(12), 950-953.
[3]Thilakarathne, N. N., Bakar, M. S. A., Abas, P. E., & Yassin, H. (2022). A cloud enabled crop recommendation platform for machine learning-driven precision farming. Sensors, 22(16), 6299.
[4]Aarthi, S., Manimegalai, S., & Sakthivel, R. (2025). AI-based Smart Crop Recommendation System for Sustainable Agricultural Production: A Data-driven Approach to Minimize Resource Use and Maximize Yield. Madras Agricultural Journal, 112(2), 135-139.
[5]Belviso, C., Satriani, A., Lovelli, S., Comegna, A., Coppola, A., Dragonetti, G., ... & Rivelli, A. R. (2022). Impact of zeolite from coal fly ash on soil hydrophysical properties and plant growth. Agriculture, 12(3), 356.
[6]Roman, D. L., Voiculescu, D. I., Filip, M., Ostafe, V., & Isvoran, A. (2021). Effects of triazole fungicides on soil microbiota and on the activities of enzymes found in soil: A review. Agriculture, 11(9), 893.
[7]Shams, M. Y., Gamel, S. A., & Talaat, F. M. (2024). Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making. Neural Computing and Applications, 36(11), 5695-5714.
[8]Shastri, S., Kumar, S., Mansotra, V., & Salgotra, R. (2025). Advancing crop recommendation system with supervised machine learning and explainable artificial intelligence. Scientific Reports, 15(1), 25498.
[9]Gosai, D., Raval, C., Nayak, R., Jayswal, H., & Patel, A. (2021). Crop recommendation system using machine learning. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(3), 558-569.
[10]Reddy, A. (2019). Crop recommendation system to maximize crop yield in ramtek region using machine learning. International Journal of Scientific Research in Science and Technology.
Ethics and Compliance
Indexed In
Similar Articles
Previous Article
