Machine Learning and IoT for Sustainable Development in Agriculture
TL;DR
It is concluded that combining ML and IoT is fundamental to achieving sustainable agricultural development and the United Nations Sustainable Development Goals, particularly Zero Hunger, Clean Water, Responsible Consumption and Production, Responsible Consumption and Production, and Climate Action.
Abstract
Agriculture is undergoing a digital transformation driven by Machine Learning (ML) and the Internet of Things (IoT), enabling sustainable food production, efficient resource utilization, and climate-resilient farming. Traditional agricultural practices are increasingly challenged by climate change, soil degradation, water scarcity, pest outbreaks, and the growing global demand for food. Integrating IoT sensors with ML algorithms provides real-time monitoring, predictive analytics, and intelligent decision-making for precision agriculture. This paper reviews recent developments in ML and IoT applications for sustainable agriculture, focusing on crop monitoring, smart irrigation, disease detection, yield prediction, livestock management, and resource optimization. The proposed framework integrates IoT sensing devices, wireless communication, cloud edge computing, machine learning analytics, and farmer decision support systems to improve productivity while minimizing environmental impact. The study further discusses current challenges, including cyber security, interoperability, infrastructure cost, and limited digital literacy among farmers. Recent studies demonstrate that AI enabled IoT systems significantly improve irrigation efficiency, reduce fertilizer consumption, enhance crop productivity, and support climate-smart agriculture. The paper concludes that combining ML and IoT is fundamental to achieving sustainable agricultural development and the United Nations Sustainable Development Goals (SDGs), particularly Zero Hunger (SDG 2), Clean Water (SDG 6), Responsible Consumption and Production (SDG 12), and Climate Action (SDG 13). Finally, future research directions involving edge intelligence, federated learning, block chain integration, and digital twins are highlighted.