A Predictive Analytics Framework for Crop Recommendation Using Ensemble Learning Techniques

Abstract: This paper presents the development and implementation of a data-driven Crop Recommendation System designed to assist farmers in making informed decisions regarding optimal crop selection. The system is deployed as a responsive web application featuring a user-friendly interface, allowing easy access and interaction for end-users. The application comprises three main modules: the Home Page, which introduces the system and outlines its significance; the User Guide Page, which provides step-by-step instructions for effective usage; and the Crop Recommendation Page, where users input specific environmental and soil parameters to receive crop suggestions tailored to their conditions.The core of the recommendation engine is a Random Forest Classifier, selected for its high accuracy and robustness in handling agricultural datasets. The model was trained on a comprehensive dataset that includes critical features such as nitrogen, phosphorus, potassium levels, temperature, humidity, pH, and rainfall. By analyzing these inputs, the system delivers personalized crop recommendations that align with the user's agro-climatic conditions, thereby enhancing decision-making for better agricultural outcomes.The model’s performance was evaluated using standard classification metrics, confirming its effectiveness in providing reliable crop suggestions. This intelligent system addresses a crucial need in modern agriculture for precision-based recommendations that can lead to increased crop productivity and resource optimization. The solution promotes sustainable farming practices by leveraging machine learning techniques and facilitating easy access through a web-based platform.

Crop Recommendation System, Machine Learning, Random Forest Classifier, Precision Agriculture, Web Application, Smart Farming, Agricultural Decision Support, Soil and Climate Parameters, Data-Driven Farming

1. 1. "Crop Recommendation System Using Data Mining Technique" by Ashish Kadam and Manisha Sharma. International Journal of Scientific and Research Publications, Volume 7, Issue 11, November 2017. [Google Scholar] [Crossref]

2. 2. "Crop Recommendation System Using Data Mining Techniques for Precision Agriculture" by M. Mohamed Thasin, S. Anandhi, and K. Santhi. International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume-8 Issue-9, July 2019. [Google Scholar] [Crossref]

3. 3. "A Machine Learning Based Crop Recommendation System for Precision Agriculture" by S. Deepika, S. Aruna, and K. Gowtham Kumar. International Journal of Innovative Technology and Exploring Engineering (IJITEE), Volume-9 Issue-1, November 2019. [Google Scholar] [Crossref]

4. 4. "Crop Recommendation System Using Machine Learning Algorithms" by Ravi Kumar K V and P K Giridhar. International Journal of Engineering and Advanced Technology (IJEAT), Volume-9 Issue-5, June 2020. [Google Scholar] [Crossref]

5. 5. "Crop Recommendation System using Hybrid Machine Learning Technique" by T. Saravanakumar, V. Vanitha, and R. Amalraj. Indian Journal of Science and Technology, Volume-13 Issue-24, June 2020. [Google Scholar] [Crossref]

6. 6. "Crop Recommendation System using Machine Learning Algorithms for Precision Agriculture" by S. S. Gupta, S. Bhatt, and M. C. Trivedi. International Journal of Computer Sciences and Engineering, Volume-8 Issue-2, February 2020. [Google Scholar] [Crossref]

7. 7. Mayank Champaneri, ChaitanyaChandvidkar, DarpanChachpara, MansingRathod, “Crop yield prediction using machine learning” International Journal of Science and Research, April 2020. [Google Scholar] [Crossref]

8. 8. Pavan Patil, VirendraPanpatil, Prof.ShrikantKokate, “Crop Prediction System using Machine Learning Algorithms”, International Research Journal of Engineering and Technology, Feb 2020. [Google Scholar] [Crossref]

9. 9. Ramesh Medar, Shweta, Vijay S. Rajpurohit, “Crop Yield Prediction using Machine Learning Techniques”, 5th International Conference for Convergence in Technology, 2019. [Google Scholar] [Crossref]

10. 10. TruptiBhange, Swati Shekapure, Komal Pawar, HarshadaChoudhari, “Survey Paper on Prediction of Crop yield and Suitable Crop”, International Journal of Innovative Research in Science, Engineering and Technology, May 2019. [Google Scholar] [Crossref]

11. 11. E. Manjula, S. Djodiltachoumy, “A Modal for Prediction of Crop Yield”, International Journal of Computational Intelligence and Informatics, March 2017. [Google Scholar] [Crossref]

12. 12. Nishit Jain, Amit Kumar, SahilGarud, Vishal Pradhan, Prajakta Kulkarni, “Crop Selection Method Based on Various Environmental Factors Using Machine Learning”, International Research Journal of Engineering and Technology (IRJET), Feb 2017. [Google Scholar] [Crossref]

13. 13. Rakesh Kumar, M.P. Singh, Prabhat Kumar, J.P. Singh, “Crop Selection Method to Maximize Crop Yield Rate using Machine Learning Technique”, 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, T.N., India., May 2015. [Google Scholar] [Crossref]

14. 14. Liu Qingyuan, and Wang Tianchuan, “Market price theory and practice”. Dalian: Northeast University of Finance and Economics Press, 1998, pp. 15-20. [Google Scholar] [Crossref]

15. 15. R. S. Pindyck, and D. L. Rubinfeld, “Econometric Models and Economic Forecasts”, The McGwar-Hill Companies, Inc, 1998, pp. 251-260. [Google Scholar] [Crossref]

16. 16. Yao Xia, Peng Hangen, and Zhu Yan, “ARIMA Time Series Modeling and Applying on Fresh Agricultural Products”, System Sciences and Comprehensive Studies in Agriculture, vol. 23, Feb. 2007 , pp. 88-94. [Google Scholar] [Crossref]

17. 17. Nie Rong, Qin Keming, and Zhang Xiaohong, “Stochastic Model and Risk Measuring on the Farm-produce′s Price”, Mathematics In Practice and Theory, vol. 34, Nov. 2004, pp. 108-112. [Google Scholar] [Crossref]

18. 18. Zhang Xiaioshuan, “Research of Aquatic Products Price Forecasts Support System”, Beijing: China Agricultural University, 2003, pp. 10-15. [Google Scholar] [Crossref]

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