AI-Powered Sustainable Farming Assistant: A Conceptual Framework Using Machine Learning

Abstract

Food production and farm-level income in developing nations like India are under growing strain from shifting climate patterns, soil nutrient exhaustion, and the widespread unavailability of expert-level agronomic guidance at the point of need [1]–[9]. This paper puts forward a conceptual design for an AI-Powered Sustainable Farming Assistant (AISFA), which draws on supervised machine learning to convert raw soil and weather data into specific, actionable guidance for farmers. The parameters include nitrogen, phosphorus, potassium, soil pH, ambient temperature, relative humidity, and measured rainfall. The system generates tailored recommendations for crop selection, water scheduling, fertiliser dosage, and pest management. The architecture is built entirely in software and does not depend on embedded sensors, IoT gateways, or any physical hardware beyond a basic internetconnected device — addressing hardware barriers identified in Growlify[4], AgriVision[7], and Patil et al. [5]. No working software prototype has been constructed; the contribution is a rigorously grounded design framework.

Introduction

This paper proposes AISFA (Artificial Intelligence Smart Farming Assistant), a software-based agricultural advisory framework designed to help Indian smallholder farmers make better farming decisions without relying on expensive sensors or continuous internet connectivity. Agriculture remains a vital part of India’s economy, but farmers face challenges such as poor crop selection, inefficient use of water and fertilizers, and delayed detection of pests and diseases. Traditional experience-based farming practices are often insufficient in addressing changing environmental conditions and climate variability.

AISFA uses Artificial Intelligence (AI) and Machine Learning (ML) to provide personalized recommendations based on soil test data, weather information, crop history, and agricultural databases. Unlike many existing smart farming systems, AISFA is designed to operate with minimal hardware requirements and limited internet access, making it more accessible to rural farmers.

The literature review examined nine existing AI-based agricultural systems and identified common limitations, including internet dependency, high hardware costs, lack of offline functionality, limited geographic applicability, and poor language accessibility. AISFA is proposed as a solution to overcome these challenges through a unified and scalable framework.

The framework follows a five-layer architecture:

1. Data Acquisition Layer – Collects soil data, weather forecasts, and agricultural information.
2. Data Preprocessing Layer – Cleans, normalizes, and prepares data for analysis.
3. AI & Machine Learning Layer – Uses:
   • Random Forest for crop recommendation,
   • Support Vector Machine (SVM) for irrigation scheduling,
   • Gradient Boosting for fertilizer recommendations,
   • Convolutional Neural Networks (CNN) and risk-based methods for pest and disease detection.
4. Decision Support Layer – Converts model outputs into practical farming recommendations.
5. User Interaction Layer – Provides multilingual recommendations through web interfaces, conversational AI, voice input, and SMS notifications.

The proposed methodology includes data collection from agricultural datasets such as Crop Recommendation Dataset and PlantVillage, preprocessing, model training and evaluation, system integration through APIs, and usability testing with farmers.

AISFA is expected to achieve:

• Over 92% crop recommendation accuracy,
• 15–25% reduction in water and fertilizer usage,
• Above 90% pest and disease detection accuracy,
• More than 85% usability for first-time users.

Compared with existing systems, AISFA offers several advantages, including no IoT sensor requirement, minimal internet dependency, multilingual support, offline functionality, lower deployment cost, and an LLM-based conversational interface. The framework aims to improve agricultural productivity, resource efficiency, and sustainability while remaining affordable and accessible for smallholder farmers.

Conclusion

This paper has presented the conceptual design of AISFA, a machine learning-based agricultural advisory system built specifically to meet the needs and constraints of smallholder farmers in India. The case for developing such a system rests on three clear foundations. First, the existing literature [1]–[9] consistently shows that data-driven advisory systems outperform experience-based decision-making for crop selection, resource management, and pest response [6],[9]. Second, that same literature reveals a recurring set of infrastructure-related barriers — internet dependency [2],[3],[5],[6],[7],[8],[9], hardware costs [4],[5],[7], and language limitations [2],[3],[5],[6],[7] — that prevent even the most capable existing systems from reaching the farmers who need them most [1]–[9]. Third, the five-layer software architecture proposed for AISFA has been deliberately designed to address each of these barriers, without compromising on the quality of the recommendations it delivers. No working prototype has been developed as part of this research. The contribution of this paper is the design itself — a system architecture grounded in established machine learning practice [6],[7],[8],[9], motivated by real and documented farmer needs [1]–[9], and clearly differentiated from prior work [1]–[9] through its software-only approach, low-connectivity operation, scalable structure, and LLM-powered multilingual interface. Realising the full potential of this framework will require future work in several areas: complete software implementation using FastAPI and React, thorough model training and validation against benchmarks established in the reviewed literature [6],[7],[9], pilot deployment across different agro-climatic regions [4],[9], and iterative refinement based on feedback from real farmers in real conditions. It is through this next phase of work that the design proposed here can be transformed into a tool that makes a measurable difference to the lives and livelihoods of the farmers it is intended to serve [1]–[9].

References

[1] S. Tapkir, A. Vathare, and P. Suryawanshi, “Implementation of Artificial Intelligence in Agriculture,” Int. J. Novel Research and Development (IJNRD), 2023. [2] P. S. Jagtap, A. M. Suryawanshi, K. G. Kakade, and S. Y. Bhatkute, “Smart Agriculture Assistant: An AI-Powered Approach for Precision Farming and Crop Management,” Int. J. Advanced Research in Computer and Communication Engineering (IJARCCE), 2025. [3] P. Kaviya, M. Bhavyashree, M. D. Krishnan, and M. Sugacini, “Artificial Intelligence Based Farmer Assistant Chatbot,” Int. J. Research in Engineering, Science and Management (IJRESM), vol. 4, no. 5, 2021. [4] V. K. Kasturi and C. D. P. Navis, “Growlify: An AI-Powered Urban Farming Assistant,” Int. J. Innovative Research in Technology (IJIRT), vol. 12, no. 3, Aug. 2025. [5] R. S. Patil, A. K. Sharma, P. V. Deshmukh, and S. R. Kulkarni, “AI-Based Crop Recommendation and Smart Irrigation System for Sustainable Agriculture,” Int. J. Innovative Research in Computer Science and Technology (IJIRCST), 2024. [6] A. A. Mana, A. Allouhi, A. Hamrani, S. Rehman, I. el Jamaoui, and K. Jayachandran, “Sustainable AI-based Production Agriculture: Exploring AI Applications and Implications in Agricultural Practices,” Smart Agricultural Technology, Elsevier, vol. 7, Article 100416, 2024. [7] A. Siddiqui, A. Nagbansh, and S. Lamba, “AgriVision: AI-Driven Solutions for Sustainable Agriculture,” IRJEdT, vol. 6, no. 10, Oct. 2024. [8] G. Nanthakumar, R. Atchaya, S. Brindha, I. Harshaa, and R. Kaviya, “AgriPath: An AI-Driven Assistant Empowering Farmers with Real-Time Guidance, Multilingual Access, and Tailored Farming Support,” IJARSCT, vol. 5, no. 2, May 2025. [9] D. Kavitha, P. Kiranteja, V. J. V. Shridharan, P. K. Desigan, N. G. Raghav, and V. Pranav, “A Smart Agricultural Assistant for Crop Recommendation using Machine Learning,” School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India, 2025.

Copyright

Copyright © 2026 Snehal G. Pawar, Snehal A. Pisal, Sakshi S. Malpote. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.