International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Crop Recommendation System using Machine Learning |
|---|---|
| ABSTRACT | Agriculture is a major contributor to the Indian economy and plays a vital role in ensuring food security and employment. However, farmers often face challenges in selecting appropriate crops due to lack of awareness about soil properties, nutrient composition, and uncertain climatic conditions. Conventional farming practices rely heavily on experience-based decisions, which may lead to inefficient resource utilization and reduced productivity. This paper proposes a machine learning–based crop recommendation and yield prediction system to assist farmers in making informed agricultural decisions. The proposed system analyzes essential soil parameters such as nitrogen, phosphorus, potassium, and pH, along with environmental factors including temperature, humidity, and rainfall. Supervised machine learning techniques are employed to learn patterns from historical agricultural data and recommend suitable crops for given conditions. The system is implemented as a web-based application integrated with trained machine learning models, enabling real-time recommendations. Experimental analysis indicates that the proposed approach enhances decision accuracy, improves crop productivity, and supports sustainable precision agriculture. |
| AUTHOR | RANJEET KUMAR, TAMANNA RAJPUT, SATYAM CHOUHAN, SANDESH SADAPHAL, PROF. RITU BHADAURIA Under Graduate Students, Dept. of C.S(AI&ML), Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India Assistant Professor, Dept. of C.S(AI&ML), Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312142 |
| pdf/142_Crop Recommendation System using Machine Learning.pdf | |
| KEYWORDS | |
| References | 1. Pudumalar, S., Ramanujam, E., Rajashree, R. H., Kavya, C., Kiruthika, T., & Nisha, J., “Crop Recommendation System for Precision Agriculture,” IEEE Eighth International Conference on Advanced Computing (ICoAC), pp. 32–36, 2016. 2. Solanki, V. K., Singh, P. K., & Singh, S., “Crop Prediction Using Machine Learning Algorithms,” International Journal of Engineering Research & Technology (IJERT), Vol. 7, Issue 06, 2018. 3. Kumar, A., Sarkar, S., & Pradhan, C., “Machine Learning Techniques for Crop Yield Prediction,” International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 8, Issue 5, pp. 1–5, 2018. 4. Cauvery, N. K., Reddy, S. M., & Kumar, P. R., “Ensemble Based Crop Recommendation System Using Machine Learning,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol. 5, Issue 2, 2019. 5. Patel, H., Shah, D., & Patel, P., “Crop Recommendation System Using Machine Learning,” International Journal of Computer Sciences and Engineering, Vol. 7, Issue 5, pp. 34–38, 2019. |
