Wheat Disease Detection Using Deep Convolutional Neural Networks: A Machine Learning Approach to Resolve the Agricultural Intrusion
DOI:
https://doi.org/10.52783/jns.v14.1608Keywords:
labor-intensive, stripe rust, Convolutional Neural Network (CNN), TensorFlowAbstract
A system for detecting wheat leaf diseases, specifically Septoria and stripe rust, has been developed using a Convolutional Neural Network (CNN) implemented with TensorFlow. The model was trained on a dataset of 407 images of wheat leaves and achieved 97% accuracy in differentiating between healthy leaves and those with diseases. The system is deployed as a user-friendly web application, allowing farmers to upload images of potentially infected leaves and receive instant disease predictions. The application also provides information on disease symptoms, lifecycles, and management strategies from trusted sources. This technology has the potential to revolutionize wheat disease management, enhance farm productivity, and strengthen food security in India and other wheat-producing regions. Farmers can make informed decisions, implement targeted interventions, and minimize yield losses by providing timely and accurate disease detection. The system aims to facilitate early detection and precise classification of diseases, ultimately helping farmers minimize resource wastage and prevent economic losses. The project underscores the potential of deep learning techniques for real-time disease detection and management in wheat crops. The web application is designed to be accessible to farmers in remote areas, addressing the limitations of traditional disease diagnosis methods. The system's accuracy and efficiency can help reduce the economic impact of wheat diseases, which can cause significant yield losses and impact livelihoods. Overall, the project demonstrates the potential of AI-powered solutions for improving agricultural practices and enhancing food security
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