AgroIntelligence: A Farmer Centric ML Solution for Smart Agriculture

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 1, pp. 175-180, January 2026.

https://doi.org/10.58482/ijersem.v2i1.24

This work is licensed under a Creative Commons Attribution 4.0 International License.

AgroIntelligence: A Farmer Centric ML Solution for Smart Agriculture

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Pathipati Haarika Naidu

Chanumallu Charan Jetty

D. Janani

Mothukuri Mounika

Vuggam Krishna Vamsi

Muktha Nithish

Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.

Abstract: Unpredictable climate patterns and declining soil health have intensified agricultural distress in India, posing serious challenges to food security and farmer livelihoods. This paper introduces AgroIntelligence, a machine-learning– based framework for real-time crop and fertilizer recommendation in precision agriculture. The system employs an optimized Random Forest classifier to model soil– climate relationships and uses K-Nearest Neighbors (KNN) imputation to handle missing soil nutrient data. To improve rural applicability, the framework supports two input modes: a Manual Mode for direct farmer input and an Auto Mode that leverages geolocation to obtain district-level NPK, pH, rainfall, and real-time weather information through API integration. A bilingual AI chatbot is incorporated to enhance accessibility for diverse farmer communities. Experimental evaluation on a custom dataset of 18,240 records from 26 districts of Andhra Pradesh reports a classification accuracy of 98%. The proposed framework supports sustainable precision agriculture by reducing unnecessary crop and fertilizer input usage and enabling data-driven agricultural planning.

Keywords: Smart Agriculture, Crop Recommendation, Fertilizer Recommendation, Weather integration, Local Language Support.

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This article is part of Volume 2, Issue 1 (January 2026)