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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM61851
Volume:
10 |
4 |
April 2026
Pages:
1-12
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.
Department of Computer Science
Datta Meghe College of Engineering, Airoli, Navi Mumbai, India
Department of Computer Science
Datta Meghe College of Engineering, Airoli, Navi Mumbai, India
Department of Computer Science
Datta Meghe College of Engineering, Airoli, Navi Mumbai, India
Department of Computer Science
Datta Meghe College of Engineering, Airoli, Navi Mumbai, India
Department of Computer Science
Datta Meghe College of Engineering, Airoli, Navi Mumbai, India
Smart Krishi is a web-based agricultural decision support system that uses data-driven methods to improve crop yields and long-term viability. The proposed system doesn't need expensive hardware as most smart farming solutions do. Instead, it uses historical agricultural datasets, soil parameters, weather data, and user inputs. The system combines several modules into one platform. These modules include crop recommendation, yield prediction, disease detection, weather advice, and market price analysis. To make smart and accurate recommendations, machine learning algorithms like Random Forest, Naïve Bayes, and Convolutional Neural Networks (CNN) are used. The crop recommendation module gets 85% correct, the disease detection module gets 88% correct, and the yield prediction model gets an R² score of 0.82. React is used for the front end and Supabase for the back end of the platform. This makes sure that it can grow and be used easily. The proposed system is a cheap and easy-to-use way to help small and marginal farmers, which is a big step forward for digital agriculture. The proposed system demonstrates the effectiveness of AI-driven agriculture solutions in improving productivity and decision-making for sustainable farming.
KEYWORDS
This paper introduced a web-based agricultural decision support system called Smart Krishi that can help farmers improve crop productivity by making appropriate decisions based on the recommendations provided by the system. The results show that the system can effectively recommend crops to the farmer and predict the yield of the crops. The proposed system is cost-effective and useful for small and marginal farmers by avoiding the use of IoT devices. The proposed system contributes to the development of digital agriculture by introducing an intelligent and user-friendly assistant for the farmer.
[1] A. U. Rehman, "Smart agriculture technology: An integrated framework of renewable energy resources," Cleaner Energy Systems, 2024. [2] A. S. e. a. Ibrahim, "AI-IoT smart pivot for plant disease detection," Scientific Reports, 2025. [3] R. Hanchante, "Smart Agriculture Crop Advisor System for Kisan Using AI," Journal of Emerging Technologies and Innovative Research (JETIR)., 2025. [4] S. A. Marjiya, "Real Time Smart Farming Analytics using IoT and Cloud Computing," International Journal of Engineering Development and Research (IJEDR), 2025. [5] V. Sardeshmukh, "An AI-Driven Smart Crop Recommendation and Advisory Framework," International Research Journal of Advances in Engineering and Humanities (IRJAEH), 2025. [6] S. K. e. a. Apat, "Artificial Intelligence-Based Crop Recommendation System using Machine Learning," 2023. [7] H. e. al., "Krishi Mitra – Intelligent Crop and Fertilizer Recommender," 2023. [8] Dongare, "Krishi Mitra: A Farming Assistance Application for Next Generation Farmers," 2021. [9] B. Saroja and Beriya, "Data-Driven Decision Making for Smart Agriculture," 2019. [10] Patel, "Survey of Android Applications for the Agriculture Sector," 2016. [11] S. P. Wani, "Mission India for Transforming Agriculture (MiTrA)," 2016. [12] P. &. Nirbhan, Krishi Panan Mitra, 2013.
Publication Date
4/30/2026
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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