Deep Learning Techniques for Automated Detection of Diseases in Guava Fruits and Leaves

Abstract

The "Deep Learning Techniques for Automated Detection of Diseases in Guava Fruits and Leaves" research project aims to revolutionize guava farming by creating an automated system for early disease identification. By harnessing cutting-edge deep learning techniques, we strive to significantly improve agricultural production by accurately diagnosing plant illnesses. Our extensive collection of tagged guava plant images will lead to the training of advanced deep learning models, ultimately benefitting guava farmers worldwide. Our evaluation of various models, including ResNet50, VGG16, Inception V3, and DenseNet121, revealed that the ResNet50 model outperformed the rest with an impressive test accuracy of 92%. Its unparalleled accuracy and robustness make it an ideal choice for practical implementation in guava farming, promising substantial advancements in disease detection. Capturing and analyzing data, creating models, deploying solutions, and rigorous testing are pivotal project phases essential for success. Our meticulously planned project management includes a well-structured calendar, clear work allocations, and key milestones to ensure efficient and timely execution. A comprehensive financial analysis forecasts the project's total cost to be between 75,900 and 90,900 taka over a six-month period, encompassing field trips, personnel, software, hardware, cloud services, and other related expenses. This study aims to deliver a reliable solution for guava growers, enabling timely and accurate identification of plant diseases, ultimately leading to healthier crops and increased yields