Use of AI in Crop Health Monitoring

Accurate and timely diagnosis of plant diseases is crucial for maintaining agricultural productivity and ensuring global food security. Traditional methods, such as visual inspections, are time-consuming, error-prone, and impractical for large-scale farming operations. This chapter delves into the use of artificial intelligence (AI) technologies, including machine learning (ML), deep learning (DL), and the Internet of Things (IoT), for diagnosing plant diseases. By reviewing recent advancements, it emphasizes the high accuracy of AI models, such as convolutional neural networks (CNNs), in detecting and classifying plant diseases. The integration of IoT with AI has further enabled real-time disease detection and monitoring, offering significant benefits for large-scale agricultural applications. Nevertheless, challenges such as limited datasets, environmental variability, high implementation costs, and data privacy concerns remain. This analysis provides an in-depth overview of these technologies, addressing their benefits, limitations, and prospects, and aims to advance scalable AI-driven solutions for effective plant disease management.