A review on machine learning-based precision agriculture techniques for crop farming monitoring with IOT

Abstract

Abstract Precision agriculture, driven by advancements in machine learning (ML) and the Internet of Things (IoT), has revolutionized modern crop farming by enabling real-time monitoring, predictive analytics, and data-driven decision-making. Despite the growing integration of machine learning and IoT in precision agriculture, challenges such as data heterogeneity, sensor reliability, computational complexity, and cybersecurity continue to hinder optimal system performance. This paper addresses the need for robust, scalable, and secure ML-IoT frameworks to enhance real-time decision-making and sustainability in modern farming. This review explores the integration of ML techniques with IoT-based precision agriculture systems to enhance crop health monitoring, soil analysis, irrigation management, and yield prediction. Various ML algorithms have been extensively utilized for disease detection, nutrient optimization, and climate impact assessment. This paper examines the role of IoT sensors in collecting real-time data, such as temperature, soil moisture, and humidity, to facilitate precision farming. Furthermore, this study highlights challenges such as data heterogeneity, sensor reliability, computational complexity, and cybersecurity threats in ML-driven IoT frameworks. A comparative analysis of existing ML models, their accuracy, scalability, and computational efficiency is provided to evaluate their effectiveness in precision agriculture applications. Additionally, this review discusses the integration of edge computing and cloud-based architectures for optimizing data processing and decision support in smart farming. Future research directions focus on the development of hybrid ML models, explainable AI techniques, and blockchain-based secure data sharing for sustainable and scalable precision agriculture solutions.