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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65067
Volume:
10 |
6 |
Jun 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Information Technology
RMD Sinhgad School of Engineering
Information Technology
RMD Sinhgad School of Engineering
Information Technology
RMD Sinhgad School of Engineering
Information Technology
RMD Sinhgad School of Engineering
Information Technology
RMD Sinhgad School of Engineering
Agriculture plays a vital role in the Indian economy and supports the livelihood of millions of people. However, improper crop selection, excessive fertilizer usage, and changing weather conditions often reduce agricultural productivity. FarmaSuit is a machine learning-based agricultural recommendation system developed to assist farmers in making informed farming decisions. The system provides crop recommendations and fertilizer suggestions based on soil nutrients (N, P, K), pH value, temperature, humidity, and rainfall data. Multiple machine learning algorithms including Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting, and Neural Networks were evaluated to identify the most suitable prediction model. The system was implemented using React for the frontend, FastAPI for the backend, and MongoDB for data management. Experimental results demonstrate that the proposed system provides accurate recommendations and supports sustainable farming practices through data-driven decision making. FarmaSuit serves as an intelligent decision support platform that bridges the gap between traditional agriculture and modern technology.
KEYWORDS
The FarmaSuit project successfully demonstrates the application of machine learning in modern agriculture through the development of an intelligent crop and fertilizer recommendation system. By analyzing soil nutrients, pH values, temperature, humidity, and rainfall parameters, the system provides accurate and personalized agricultural recommendations. The integration of machine learning models with React, FastAPI, and MongoDB resulted in a complete web-based solution capable of supporting data-driven farming decisions. The developed platform improves crop selection, optimizes fertilizer usage, and promotes sustainable farming practices. Its user-friendly interface enables easy adoption by farmers and agricultural stakeholders. The successful implementation and evaluation of FarmaSuit highlight the potential of artificial intelligence and machine learning technologies in transforming traditional farming into a more efficient, productive, and sustainable process. Future enhancements may include mobile application support, regional language integration, IoT sensor connectivity, crop disease prediction, and market price forecasting modules to further strengthen the capabilities of the system.
A. Wavare, M. Sonar, P. Tonde, and D. Salgar, "FarmaSuit: The Agricultural Recommendation System," International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 14, no. Jan. 2026. 1. D. Javanarayana Reddy and M. Rudra Kumar, “Crop Yield Prediction using Machine Learning Algorithm,” in Proceedings of the Fifth International Conference on Intelligent Computing and Control Systems (ICICCS 2021), pp. 1466–1472 IEEE, 2021 2. E. Elbasi, C. Zaki, A. E. Topcu, W. Abdelbaki, A. I. Zreikat, E. Cina, A. Shdefat, and L. Saker, “Crop Prediction Model Using Machine Learning Algorithms,” Applied Sciences, vol. 13, p. 9288, 2023 3.P. Jain and A. Mehta, "Data-Driven Decision Support for Smart Farming using Soil and Weather Analytics," IEEE Access, vol. 11, pp. 54710-54722, 2023 4.T. S. T. Tanaka, G. B. M. Heuvelink, T. Mieno, and D. S. Bullock, “Can machine learning models provide accurate fertilizer recommendations?” Precision Agriculture, vol. 25, pp. 1839–1856, 2024
Publication Date
6/16/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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