A Deep Neural Network Approaches for Detection of Guava Leaf Disease

Abstract

Guava production is seriously threatened by guava leaf diseases as anthracnose, rust, and leaf spot, which result in severe yield and quality reductions. Effective management and the reduction of financial losses depend on the early and precise detection of these illnesses. In this work, a deep learning-based system for automatically identifying and categorizing guava leaf diseases is developed. Images of both healthy and diseased guava leaves are analyzed using convolutional neural networks (CNNs), with preprocessing methods like scaling, normalization, and augmentation improving model performance. To maximize feature extraction and computational efficiency, transfer learning techniques are used, including architectures like VGG16, ResNet50, and MobileNet. Metrics like accuracy, precision, and recall are used to assess the system's efficacy, showing that it can accurately classify the conditions of guava leaves. With the help of this computerized technology, farmers can detect illnesses early and take prompt action, lowering their reliance on chemical treatments. This study demonstrates how artificial intelligence can be used practically to advance precision farming by promoting sustainable agricultural practices.