CONVOLUTIONAL NEURAL NETWORK FOR RAPID IDENTIFICATION OF WHEAT LEAF DISEASES: A FARMER- FRIENDLY TOOL - Khizra Shahzad; Maryam Waqas; Khadija Nadeem; Sobia Riaz; Aasma Khalid (Spectrum of Engineering Sciences, 2025)
The increasing impact of wheat diseases on crop yield poses a significant challenge to farmers worldwide. This project aims to develop a user-friendly website for detecting common wheat diseases using an artificial intelligence model. By leveraging a basic Convolutional Neural Network (CNN), the website allows users to upload images of wheat plants and receive immediate feedback on the presence of any disease. This project not only serves as a practical tool for farmers but also contributes to the growing field of artificial intelligence in agriculture. The outcomes of the project include a functional prototype capable of accurately detecting and providing relevant information on multiple wheat diseases, demonstrating the potential of AI in solving real-world agricultural problems. The simplicity and accessibility of the website ensure its usability for both farmers and researchers. Overall, the project holds significant value in promoting sustainable agriculture by enabling early detection and timely response to crop diseases, ultimately improving yield and reducing losses.