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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68593
Volume:
10 |
10 |
Oct 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.
Department of Artificial Intelligence And Machine Learning
Zeal Institutes
Department of Artificial Intelligence And Machine Learning
Zeal Institutes
Department of Artificial Intelligence And Machine Learning
Zeal Institutes
Department of Artificial Intelligence And Machine Learning
Zeal Institutes
Department of Artificial Intelligence And Machine Learning
Zeal Institutes
Agriculture increasingly depends on timely information about crop health, suitable farming inputs, market prices, and public support programmes. However, these services are often distributed across different websites, local providers, and government resources, making it difficult for farmers to obtain a unified view. This survey reviews artificial-intelligence approaches relevant to an integrated agricultural advisory system named KrishiMitra AI. The proposed concept combines five modules: crop disease detection from leaf images, agricultural shop demand or sales prediction, crop price forecasting, a recommendation system for crops and agricultural inputs, and a multilingual chatbot for government schemes. Recent studies indicate that computer vision, machine learning, time-series forecasting, recommendation methods, and multilingual natural-language processing can support parts of this workflow. The survey compares these approaches, identifies practical limitations such as data quality, local adaptation, explainability, and language accessibility, and outlines a modular methodology for integrating them. KrishiMitra AI is presented as a proposed system; its performance must be established through implementation and evaluation using suitable datasets and user testing.
KEYWORDS
This survey reviewed AI approaches relevant to crop disease detection, agricultural shop demand prediction, crop price forecasting, crop and input recommendations, and multilingual government-scheme assistance. Related research shows that computer vision, machine learning, time-series forecasting, and multilingual natural-language processing can contribute to agricultural decision support. KrishiMitra AI proposes to combine these capabilities in a single modular platform. Its usefulness will depend on trustworthy data, transparent predictions, current official information, local-language usability, and careful testing. Implementation and empirical evaluation remain necessary before claims about accuracy or real-world impact can be made.
[1] M. Iliyas, S. K. Mishra, and P. Pathak, “AgroBot: A Universal AI-Based Agricultural Assistant Integrating CNN Disease Recognition and Multilingual Chatbot Support,” International Journal of Digital Technologies, vol. 5, no. 1, 2026. Available: https://journal.nielit.edu.in/index.php/01/article/view/158 [2] S. B. Demse, N. S. Sonawane, H. S. Girase, and D. S. Lohar, “IntelliFarm: A Unified AI-Driven Decision Support System for Crop Planning, Disease Detection, and Market Price Forecasting,” International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, vol. 14, no. 3, 2026. DOI: 10.37082/IJIRMPS.v14.i3.233149. [3] J. Gajre, S. Manasubdar, I. Chaudhari, S. Panigrahi, and S. Patil, “Krishi Mitra: A Multilingual AI-Powered Conversational Agent for Indian Farmers Integrating Government Schemes, Real-Time Mandi Prices, Crop Disease Detection, and Agricultural Advisories,” International Journal of Advanced Research in Computer and Communication Engineering, 2026. DOI: 10.17148/IJARCCE.2026.155127. [4] “AI-Powered Smart Agriculture Advisor,” in Proceedings of the 2025 3rd International Conference on IoT, Communication and Automation Technology (ICICAT), 2025. DOI: 10.1109/ICICAT68430.2025.11414685. [5] L. T. Shakya, “Agrisathi: A Multilingual AI-Based Advisory Platform for Smallholder Farmers in India,” International Journal of Agriculture, Biology & Environment, 2025. DOI: 10.47504/IJAGRI.2025.4.2. [6] P. Thirugnanam et al., “AgroNLP-SimAssist: a multilingual transformer-based decision support simulator for crop disease diagnosis, advisory generation, and market intelligence,” BMC Plant Biology, 2026. DOI: 10.1186/s12870-026-10040-8. [7] M. S. Sidhu, R. Rajput, and G. Thapa, “Dual AI Framework for Indian Agriculture: LSTM-Based Crop Price Forecasting and Soil Mineral-Based Crop Recommendation System,” International Journal of Creative Research Thoughts, vol. 14, no. 2, 2026.
Publication Date
10/31/2026
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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