Plant Leaf Disease Detection Using Deep Learning

Abstract

Plant leaf diseases pose a danger to food security, and their rapid identification is made more difficult in many areas by a lack of infrastructure. This thesis is a concentrated attempt to address this important problem by utilizing state-of-the-art deep learning techniques, with a focus on the YOLOv5 model, to offer a dependable and effective solution for plant leaf disease detection in agriculture. The introduction emphasizes the serious effects that plant diseases have on a global and financial level, underscoring the critical necessity for early detection to lessen these effects. Driven by the promise of technology to revolutionize agriculture, this work carefully investigates the complex use of deep learning techniques. YOLOv5 is trained to demonstrate its ability to distinguish between healthy and diseased plant leaves using a carefully chosen tomato dataset. The dataset contains nine different types of illnesses. The model achieves an impressive 92.6 percent average precision, indicating a high degree of disease detection accuracy. Plant leaf disease detection in agriculture faces many complicated obstacles, and the successful deployment of the trained model through the Flask framework represents a significant leap in the practical application of deep learning to address these issues. Our multimodal approach places our research at the forefront of efforts to improve agricultural technology and guarantee global food security while also making a significant contribution to the scientific understanding of disease identification and laying the foundation for future advances.