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.
