Title: Enhancing fruit farming efficiency through IoT-driven soil moisture analysis and classifier ensemble

Authors: Chinmayee Senapati; Swagatika Senapati; Satyaprakash Swain

Addresses: Department of Civil Engineering, ITER - Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Civil Engineering, ITER - Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Computer Science & Engineering, ITER - Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

Abstract: Effective soil moisture management is a key to optimising fruit farming productivity. This research work model integrates IoT devices, clustering techniques, PCA and ensemble learning to enhance soil moisture classification for crops like pomegranate, mulberry, mango, grapes, ragi and potato. Among various classifiers tested, the Random Forest model demonstrated superior accuracy. However, a stacked Random Forest-SVM model further improved accuracy to 94.65%. This research underscores the importance of IoT-driven data and machine learning in precision agriculture, demonstrating how advanced techniques can refine soil moisture management. By optimising soil moisture, we directly enhance nutrient availability, root development, and water uptake, leading to better crop yield, quality and sustainability. This approach highlights the synergy between technology and machine learning, advancing sustainable and efficient fruit cultivation.

Keywords: IoT; PCA; NBM; RBF; K-star; RF; SVM; KNN; GNB; DT; clustering.

DOI: 10.1504/IJGUC.2025.148534

International Journal of Grid and Utility Computing, 2025 Vol.16 No.5/6, pp.407 - 420

Received: 16 Apr 2024
Accepted: 25 Jun 2024
Published online: 11 Sep 2025 *

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