Machine Learning-Based Pomegranate Disease Detection and Treatment

Abstract

There are two components to the assignment of pomegranate disease detection. A neural network model is created in the first part of the process, which includes image acquisition, labelling, segmentation, resizing, image stacking, and label stacking. The model is then trained using database images. The second component of the framework uses the developed model to predict image classification. The framework is implemented using a pomegranate leaf disease image dataset that is split into training and test sets. The suggested approach to solving the issue depends on creating machine learning-ml algorithm and ANN building blocks during the implementation phase.