Intelligent Crop Recommendation System Using Machine Learning

Abstract

The intelligent crop recommendation system is aimed at helping farmers to pick the most appropriate crops, depending on the different environmental factors including the nutrients of the soil (NPK) as well as pH, temperature, humidity and rainfall levels. The system, through machine learning algorithms, such as random forest, support vector machine (SVM) and K- nearest neighbors (KNN), can estimate the best crops to be planted in various environmental conditions. Training is done with a large agricultural dataset which represents the key attributes needed to be classified when giving recommendations on crops. It is connected to an easy-to-use web interface to make it accessible and easily usable by the farmers. This system is also beneficial to sustainable farming since it can offer some insights on the application of data-driven knowledge that increases crop yield. This project will add value to the agricultural efficiency and sustainability through the use of technology and agricultural expertise.

Introduction

This research proposes an Intelligent Crop Recommendation System that uses machine learning to help farmers select the most suitable crops based on soil and environmental conditions. Traditional farming methods often rely on experience and subjective decision-making, making it difficult to adapt to changing climate conditions such as soil degradation, water scarcity, and unpredictable weather. To address these challenges, the system analyzes key parameters including nitrogen (N), phosphorus (P), potassium (K), soil pH, temperature, humidity, and rainfall to provide accurate, data-driven crop recommendations.

The study reviews existing research on crop recommendation systems, highlighting the use of machine learning, ensemble learning, explainable AI (XAI), TinyML, IoT, and hybrid models. Although these approaches improve prediction accuracy, many suffer from high computational complexity, limited scalability, and implementation challenges. The proposed system aims to balance accuracy, efficiency, and usability by employing three machine learning algorithms: Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN).

The system is trained using the Crop Recommendation dataset from Kaggle, which contains 2,200 samples, 7 environmental features, and 22 crop classes. Data preprocessing includes label encoding and feature scaling using StandardScaler, while model performance is evaluated using 5-fold Stratified Cross Validation. The application is developed in Python using the Flask framework and machine learning libraries such as Scikit-learn, Pandas, and NumPy, with a simple web interface that enables farmers to enter environmental parameters and receive crop recommendations.

Experimental results show that the Random Forest classifier outperforms SVM and KNN, achieving 98% accuracy along with high precision, recall, and F1-score (approximately 0.98). These results demonstrate that Random Forest provides the most reliable and consistent crop predictions. Overall, the proposed system offers an accurate, user-friendly, and practical decision-support tool that can improve agricultural productivity, promote sustainable farming practices, and assist farmers in making informed crop selection decisions.

Conclusion

This paper has come up with an Intelligent Crop Recommendation System that is founded on machine learning technologies in order to help farmers decide on the best crops depending on the soil and environmental conditions. The system has 7 important parameters that it uses in getting correct predictions. Random Forest was the best in accuracy with a score of 98 percent compared to SVM and KNN. The findings indicate that machine learning models can be appropriate to examine agricultural data and make valid crop recommendations. The suggested system will be easy to use and give farmers an opportunity to make decisions based on data, which will increase crop production and enhance sustainable agriculture. On the whole, the system is an effective and feasible answer to the present-dayagriculture. In the future, the system can be improved by adding real-time information in the form of the IoT sensors to give more dynamic and accurate recommendations. An app is possible to create, which will make accessibility easier to the farmers in distant places. Moreover, weather forecasting and satellite information can also be added to enhance better prediction. It is also possible to expand the system to incorporate the recommendations of fertilizers and crop yield prediction. In addition, the system can be made more robust and scaled to the specific needs of various agricultural conditions using advanced deep learning models and datasets that are region-specific.

References

This paper has come up with an Intelligent Crop Recommendation System that is founded on machine learning technologies in order to help farmers decide on the best crops depending on the soil and environmental conditions. The system has 7 important parameters that it uses in getting correct predictions. Random Forest was the best in accuracy with a score of 98 percent compared to SVM and KNN. The findings indicate that machine learning models can be appropriate to examine agricultural data and make valid crop recommendations. The suggested system will be easy to use and give farmers an opportunity to make decisions based on data, which will increase crop production and enhance sustainable agriculture. On the whole, the system is an effective and feasible answer to the present-dayagriculture. In the future, the system can be improved by adding real-time information in the form of the IoT sensors to give more dynamic and accurate recommendations. An app is possible to create, which will make accessibility easier to the farmers in distant places. Moreover, weather forecasting and satellite information can also be added to enhance better prediction. It is also possible to expand the system to incorporate the recommendations of fertilizers and crop yield prediction. In addition, the system can be made more robust and scaled to the specific needs of various agricultural conditions using advanced deep learning models and datasets that are region-specific.

Copyright

Copyright © 2026 Ch. Suresh , T. Sowjanya, R. Yaswanthi, V. Pushya Manasa, M. Deepak, V. Sowjanya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.