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Volume 13 | Issue 4 | Year 2026 | Article Id. IJECE-V13I4P122 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I4P122

Smart Precision Agriculture using IoT Sensing and Machine Learning Analytics for Farming in Mysuru District

Noor Fathima, Vinay Kumar S B, Mohmad Umair Bagali

Received Revised Accepted Published
18 Jan 2026 18 Feb 2026 21 Mar 2026 30 Apr 2026

Citation :

Noor Fathima, Vinay Kumar S B, Mohmad Umair Bagali, "Smart Precision Agriculture using IoT Sensing and Machine Learning Analytics for Farming in Mysuru District," International Journal of Electronics and Communication Engineering, vol. 13, no. 4, pp. 265-276, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I4P122

Abstract

Precision farming has been found to be a viable solution to the problem of productivity, sustainability, and resource efficiency of the rural agricultural sector in India. In this paper, I introduce a combined Internet of Things (IoT) and Machine Learning (ML)-based precision farming system to be used in real-time monitoring of soil health and predicting crop yields, including a comprehensive case study performed in the Mysuru district of Karnataka, India. One complete set of 557 farm records of 7 taluks in Mysuru, and another 50 comparison records in adjacent districts, were taken, including basic parameters like temperature, humidity, soil pH, soil moisture, light intensity, nutrient level (N, P, K), and yield/acre. A multi-sensor hardware platform (along with a Soil Information System (SIS)) was created together with cloud storage and a mobile-based decision-support application that will allow acquiring data continuously and provide feedback to the farmer. Three machine learning models, Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT), were then trained on the preprocessed data consisting of normalized, imputed, and outlier-treated data to predict yields and compare them. As illustrated in experimental results, the Random Forest model performs better in terms of accuracy of 95.7 percent and a lesser prediction error on the Mysuru dataset compared to the SVM and DT models and models trained on neighboring district data. The results also show that the Mysuru soils have more coherent fertility and moisture retention properties, which make them better at predicting and multi-crop compatibility.

Keywords

Crop Yield Prediction, Internet of Things, Machine Learning, Precision Farming, Soil Information System.

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