A Comprehensive Survey of IoT and Machine Learning Innovations in Agriculture

Abstract

The efficient management of soil moisture and water levels is crucial for optimizing crop yield and sustainable agricultural practices. This paper presents a comprehensive review of over 20 research studies focusing on the prediction of soil moisture levels and water requirements for crops using Internet of Things (IoT) devices and Machine Learning (ML) algorithms. By systematically analysing and synthesizing key findings from these studies, the paper highlights the methodologies, technologies, and algorithms that have been most effective in this domain. Artificial Intelligence (AI) and Machine Learnng enhances precision agriculture by enabling the analysis of large datasets to identify patterns and insights that improve decision-making. AI facilitates real-time monitoring and predictive analytics, optimizing resource usage and crop management. Through automation and intelligent systems, AI contributes to increased efficiency, reduced costs, and sustainable agricultural practices. Machine Learning (ML) algorithms, including regression, classification, and neural networks, are essential for modelling and predicting soil moisture levels and water requirements. These algorithms learn from historical and real-time data to provide accurate predictions and recommendations for irrigation scheduling. By continuously improving with new data, ML algorithms enhance the reliability and effectiveness of agricultural management systems. IoT devices, such as soil moisture sensors and weather stations, enable the continuous collection of real-time data from agricultural fields.