Integrating IoT and AI for Precision Agriculture: Enhancing Water Management and Crop Monitoring in Small-Scale Farms

Abstract

Water management is a major issue in agriculture, particularly for small-scale farms that frequently confront resource limits and changing environmental circumstances. Implementing a smart irrigation system using IoT devices has various advantages but also presents some problems. The purpose of the study was to create a smart irrigation system that uses IoT sensors and machine learning algorithms to optimize water usage in small-scale farms, decreasing waste while increasing crop output. The suggested system uses IoT sensors to monitor soil moisture and meteorological conditions, with data processed in real time via edge computing. Machine learning methods, notably Decision Trees and Support Vector Machines (SVMs), are trained to anticipate optimal irrigation schedules using gathered data. The system design incorporates connection infrastructure that enables seamless data transmission and real-time decision-making. Preliminary field studies on a small-scale tomato farm showed a 25% boost in crop output and a 35% reduction in water usage while using the smart irrigation system. The results illustrate the efficacy of merging the Internet of Things and machine learning. The smart irrigation system developed in this study efficiently optimizes water utilization in agriculture, making it a feasible alternative for small-scale farmers confronting water constraint. This study advances the field of precision agriculture by proving the practical use of IoT and machine learning in smart irrigation systems as a main contribution.

https://doi.org/10.59200/ICONIC.2024.017