Machine Learning Algorithms for Integrating IoT Sensor into a Smart Irrigation system

Abstract

Water management is a critical challenge in agriculture, particularly for small-scale farms that face resource limitations and unpredictable environmental conditions. Smart irrigation technologies that integrate the Internet of Things (IoT) and machine learning offer significant solutions in enhancing water efficiency and boosting crop production. This study investigates the synergistic application of IoT-enabled sensors alongside machine learning methodologies, specifically Decision Trees (DT) and Support Vector Machines (SVM), to augment irrigation effectiveness. Real-time sensor data collection, featuring elements like soil moisture, temperature, and humidity, serves to direct irrigation techniques. The proposed utilizes solution supervised learning techniques to establish optimal irrigation timetable and reinforcement learning to modify decisions based on real-world performance. Preliminary findings suggest that SVM outperforms DT in reducing false positives and negatives, leading to more precise irrigation control. The study underlines the benefits of AI-driven irrigation system, such as enhanced water conservation, higher crop yields, and increased sustainability. Furthermore, the difficulties of establishing IoT-based irrigation systems, such as data security, connectivity constraints, and cost considerations, are addressed. The findings add to the literature of precision agriculture and provide useful insights for small-scale farmers who are willing to implement smart irrigation solutions. The study's goal is to enhance efficient water use, strengthen food security, and support sustainable farming methods by combining IoT and AI. To get the most out of AI-powered irrigation systems, future research should focus on enhancing algorithm accuracy, expanding real-world trials, and tackling scalability challenges.

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DOI: https://doi.org/10.46676/ij-fanres.v6i4.511

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