AI-Driven Deep Learning for Automated Wheat Disease Detection

The title “AI-Driven Deep Learning for Automated Wheat Disease Detection” points to a major change in how we identify wheat diseases. Crop diseases are a major cause of reduced agricultural yields and contribute significantly to global economic losses in the farming sector. To mitigate these effects, it is essential to monitor and manage crop health effectively. Diagnosing wheat diseases involves figuring out what's affecting the plants, whether it's fungi, bacteria, or other harmful organisms. Accurate diagnosis is essential for safeguarding crops and helping farmers achieve a healthy and productive harvest. Here we use the advanced and powerful technologies like deep learning and Artificial Intelligence. For wheat farming, AI can swiftly and precisely identify diseases in plants. Deep Learning, a branch of AI, improves by analysing large amounts of data, like images of healthy and sick wheat. In earlier research, image classification for disease detection relied on manually designed features. However, recent advancements in Deep Learning have significantly enhanced the precision of object detection and classification. This research focuses on developing a deep learning model to identify wheat diseases from images captured directly in the field. Along with the detection of 15 various diseases of the wheat crop it also suggest the suitable pesticide. This research helps to build more accurate outputs than the earlier research by using advanced technologies.