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 | AI-Based Crop Recommendation System using Machine Learning |
|---|---|
| ABSTRACT | Agriculture is the backbone of many countries, including India, and provides livelihoods to millions of people facing challenges such as climate change and plant disease outbreaks. Through research, a web application has been developed that provides real-time recommendations for crop selection based on various factors such as soil nutrients, temperature, humidity, pH levels, and rainfall. Recent advances in machine learning and artificial intelligence offer promising solutions to these problems, enabling accurate, data-driven decision-making in agriculture. These technologies have the potential to transform how we predict crop yields and detect plant diseases, thus improving agricultural practices. To achieve this, we trained and examined seven machine learning models, Decision tree, Naive Bayes, SVM, Logistic Regression, Random Forest, XG Boost, and KNN. Among these, Random forest gives the highest accuracy, making it the best choice for crop forecasting. In addition to crop recommendation, the web application also integrates a Plant Disease Identification system using Convolutional Neural Network (CNN). By analysing leaf images, CNN detects and accurately classifies plant diseases, allowing farmers to intervene early to prevent crop losses. This study aims to empower farmers with accessible technology to make informed decisions, improve crop selection, and effectively cure plant diseases. Combining crop recommendation with disease detection, intelligent crop recommendation systems with plant disease detection contribute to sustainable agriculture, economic stability, and food security in India and beyond. |
| AUTHOR | PROF. RAGHU B R, BINDU S A, CHANDANA K M, DARSHAN B, ANANYA G C KUNDUR Assistant Professor, Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India UG Student, Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312141 |
| pdf/141_AI-Based Crop Recommendation System using Machine Learning.pdf | |
| KEYWORDS | |
| References | [1] R. Ramesh, A. Kumar, and S. Verma, “A sustainable crop selection system using federated learning for privacy-preserving agricultural intelligence,” Journal of Smart Agriculture and AI Systems, vol. 6, no. 1, pp. 15–28, 2025. [2] P. Mehta and R. Shah, “Deep learning-based intelligent fertilizer recommendation using LSTM networks,” International Journal of Agricultural Informatics, vol. 9, no. 2, pp. 45–59, 2025. [3] N. Sharma and R. Bedi, “AI-driven crop and soil nutrient prediction using CNN and satellite imagery,” Remote Sensing Applications in Agriculture, vol. 11, no. 3, pp. 101–114, 2025. [4] A. Patel and G. Kaur, “Reinforcement learning-based smart crop planning for sustainable agriculture,” IEEE Transactions on Artificial Intelligence in Agriculture, vol. 4, no. 2, pp. 67–79, 2025. [5] S. Prity, M. Hasan, and T. Rahman, “An AI-based smart crop recommendation system using Random Forest classifiers,” Computers and Electronics in Agriculture, vol. 205, pp. 107–118, 2025. [6] R. Jatav, P. Singh, and S. Mishra, “Comparative analysis of machine learning algorithms for crop recommendation,” International Journal of Computer Applications in Agriculture, vol. 8, no. 1, pp. 33–42, 2025. [7] A. Jain and P. Deshmukh, “Transformer-based deep learning models for crop recommendation under climate variability,” Expert Systems with Applications, vol. 233, pp. 120–134, 2024. [8] S. Das, R. Banerjee, and A. Mukherjee, “CropMate: A mobile-first AI-based crop advisory system using ensemble learning,” Proceedings of the International Conference on ICT in Agriculture, pp. 210–217, 2014. |
