Machine Learning in Smart Agriculture: Predictive Analytics for Crop Health and Yield

Abstract

Machine learning, combined with IoT sensors, is transforming agriculture by enabling real-time crop monitoring, pest detection, and yield prediction. This paper explores ML models such as convolutional neural networks (CNNs), support vector machines (SVMs), and time-series forecasting for precision agriculture. It discusses challenges like data heterogeneity, sensor calibration, and model generalization. Case studies highlight successful implementations of ML-driven agricultural solutions, demonstrating improved crop productivity, reduced resource consumption, and sustainable farming practices.