Deep Learning for Accurate Grape Disease Classification with ResNet50

Maintaining the health and output of vineyards depends on a categorization for grape diseases. use ResNet50 architecture and deep learning to classify grape illnesses. Highresolution pictures of grape leaves—which contain both healthy samples and those impacted by several diseases—make up the dataset used for this study. Renowned for its depth and efficiency in managing challenging image recognition tasks, the ResNet50 model is used because it preserves excellent performance free from the vanishing gradient problem. The Adam optimizer helps to maximize the training process; hence, the performance of the model is assessed on another test set to guarantee its resilience. ResNet50 models show an amazing accuracy of 96% in diagnosing grape illnesses, much above past methods in this field of research. This great accuracy emphasizes how early and accurate disease diagnosis made possible by deep learning methods may improve agricultural practices.