Real-Time Plant Disease Detection Using Computer Vision, Machine Learning, and IoT-Integrated Environmental Sensing

Plant diseases cause significant crop losses, lower crop quality, and higher production costs, posing a major threat to agricultural sustainability and global food security. To reduce these risks, plant diseases must be identified early and accurately. However, conventional disease testing techniques are frequently labor-intensive, slow, and prone to human error. Current developments in computer vision (CV), artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) present viable ways to automate the disease detection process, offering large-scale agricultural operations quicker, more dependable, and scalable techniques. The goal of this research is to create an automated system for detecting plant illnesses in real time by combining environmental sensors, machine learning algorithms, and high-resolution imagery. The system analyzes data using computer vision techniques. The proposed system can accurately and automatically detect the symptoms of a variety of plant illnesses, even in their early stages, by using deep learning models and high-resolution images. Furthermore, incorporating environmental sensor data—such as soil moisture, temperature, and humidity—improves disease detection accuracy.