Precision Agriculture: A Machine Learning Approach to Enhance Crop Selection
Keywords:
Ensemble, Crop Prediction, Machine Learning, Robustness, Soil Parameters, Random ForestAbstract
This research work introduces an advanced crop recommendation system utilizing machine learning to assist farmers in choosing optimal crops based on specific environmental and soil conditions. By employing algorithms such as Random Forest, Logistic Regression, XGBoost, and Gaussian Naive Bayes, the system evaluates key parameters including nitrogen, phosphorus, potassium, pH, temperature, humidity, and rainfall. Achieving an accuracy of 96%, the ensemble model demonstrates its capability to deliver reliable crop predictions. A user-friendly web application enables farmers to input local conditions and receive customized recommendations, empowering them to make informed decisions that enhance productivity and address economic challenges. This innovation highlights the transformative role of machine learning in agriculture, promoting smarter, data-driven practices. Ultimately, the system aims to boost crop yields, minimize losses, and support sustainable agricultural development.
Downloads
Metrics
References
Geetha, V., Punitha, A., Abarna, M., Akshaya, M., Illakiya, S., & Janani, P. (2020). An Effective Crop Prediction Using Random Forest Algorithm. Pondicherry EngineeringCollege, Puducherry, India. DOI: 10.1109/ICCPEIC48611.2020.9079035
Jadhav, A., Riswadkar, N., Jadhav, P., & Gogawale, Y. (2023). Crop Recommendation System Using Machine Learning Algorithms. Journal of Emerging Technologies and Innovative Research (JETIR), 10(5), 584-590.
Shilpa Mangesh Pande, S. M., Kumar, P. R., Anmol, A., Raishwarya, B., Rohilla, K., & Shaurya, K. (2021). Crop Recommender System Using Machine Learning Approach. Proceedings of the Fifth International Conference on Computing Methodologies and Communication (ICCMC 2021), 1066-1071. DOI: 10.1109/ICCMC51019.2021.9418351
Shariff, S., Shwetha, R. B., Ramya, O. G., Pushpa, H., & Pooja, K. R. (2023). Crop Recommendation using Machine Learning Techniques. Department of Computer Science and Engineering, Jain Institute of Technology, Davanagere, India.
Ajay Lokhande, A., & Dixit, M. (2022). Crop Recommendation System Using Machine Learning. International Research Journal of Engineering and Technology (IRJET), 9(5), 3584-3590.
Saranya, N., & Mythili, A. (2020). Classification of Soil and Crop Suggestion Using Machine Learning Techniques. International Journal of Engineering Research & Technology (IJERT), 9(02), 78–84.
Vanadana Kale, V., & Mohapatra, B. N. (2024). Crop Recommendation System Using Machine Learning. Journal of Engineering and Technology for Industrial Applications (JETIA), 10(48), 63-68. DOI: 10.5935/jetia.v10i48.1186
Gosai, D., Patel, H., & Patel, K. (2021). Crop recommendation system using machine learning. International Journal of Scientific Research in Computer Science, Engineering and Information, 7(3), 554-557. DOI: 10.32628/CSEIT217357
A Suresh, P. Ganesh Kumar , M. Ramalatha, “Prediction of major crop yields of Tamilnadu using K-means and Modified KNN”, 3rd International Conference on Communication and Electronics Systems (ICCES), 2018.
Morshed. A., Dutta. R. & Aryal. J. (2013, April).Recommending environmental knowledge as linked open data cloud using semantic machine learning. In Data Engineering Workshops (ICDEW), 2013IEEE 29th International Conference held on (pp. 27-28). IEEE.
Parent. B. & Tardieu. F. (2014). Can current crop models be used in the phenotyping era for analyzing the genetic variability of yield of plants subjected to drought or high temperature?, Journal of experimental botany, 65(21), 6179-6189.
Kumar R, Singh MP, Kumar P, Singh JP, “Crop Selection Method to maximize crop yield rate using a machine learning technique”, International conference on smart technologies and management for computing, communication, controls, energy, and materials (ICSTM), 2015 May 6 (pp. 138-145). IEEE.
P. M. Manjunath, Gurucharan and M. Dsouza, Shwetha, "IoT Based Agricultural Robot for Monitoring Plant Health and Environment", Journal of Emerging Technologies and Innovative Research vol. 6, no. 2, pp. 551-554, Feb 2019
Melwin D'souza, Ananth Prabhu Gurpur, Varuna Kumara, “SANAS-Net: spatial attention neural architecture search for breast cancer detection”, IAES International Journal of Artificial Intelligence (IJ-AI), Vol. 13, No. 3, September 2024, pp. 3339-3349, ISSN: 2252-8938, DOI: http://doi.org/10.11591/ijai.v13.i3.pp3339-3349
Souza, M. D., Prabhu, A. G., & Kumara, V. (2019). A comprehensive review on advances in deep learning and machine learning for early breast cancer detection. International Journal of Advanced Research in Engineering and Technology (IJARET), 10(5), 350-359.
Veenadhari S, Misra B, Singh CD, “Machine learning approach for forecasting crop yield based on climatic parameters”, In 2014 International Conference on Computer Communication and Informatics, 2014 Jan 3 (pp. 1-5). IEEE.
Bang, Shivam, Rajat Bishnoi, Ankit Singh Chauhan, Akshay Kumar Dixit, and Indu Chawla. "Fuzzy logic based crop yield prediction using temperature and rainfall parameters predicted through ARMA, SARIMA, and ARMAX models." In 2019 Twelfth International Conference on Contemporary Computing (IC3), pp. 1-6. IEEE, 2019.
R. K. Ray, S. K. Das, and S. Chakravarty, ‘Smart crop recommender systemA machine learning approach’, in 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2022.
Motwani, P. Patil, V. Nagaria, S. Verma, and S. Ghane, ‘Soil analysis and crop recommendation using machine learning’, in 2022 International Conference for Advancement in Technology (ICONAT), Goa, India, 2022.
D. Modi, A. V. Sutagundar, V. Yalavigi, and A. Aravatagimath, ‘Crop recommendation using machine earning algorithm’, in 2021 5th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 2021.
M. D. Souza, V. Kumara, R. D. Salins, J. J. A Celin, S. Adiga and S. Shedthi, "Advanced Deep Learning Model for Breast Cancer Detection via Thermographic Imaging," 2024 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Mangalore, India, 2024, pp. 428-433, doi:10.1109/DISCOVER62353.2024.10750727
..
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
