Combining enhanced EfficientNet architectures and threshold-based pixels filtering for guava disease identification

The traditional methods of monitoring and detecting diseases in crops are being replaced by automated techniques that have the potential to revolutionize the innovative agriculture industry. Guava, a crop of paramount importance in Asian countries, is unfortunately susceptible to disease and fungal attacks that significantly impact production. This study utilized renowned Convolutional Neural Networks (CNN) to diagnose diseases on guava fruit and leaves. The data set used in the study, derived from Mendeley data, encompassed four types of guava diseases and healthy ones. The data underwent preprocessing, including image scaling, data augmentation techniques, and a Threshold-Based pixel filtering approach to focus on Guava. The study's findings revealed that EfficientNetB3 outperformed AlexNet, ResNet50, various versions of MobileNet, and previous studies in Guava Disease Identification tasks.