Grape Leafs Disease Detection Using Customized CNN Model

Abstract

This study looks at the diagnosis of grape leaf illnesses using a Kaggle dataset that is categorized into four categories: "esca," "black rot," "healthy," and "leaf blight." The study introduces a brand-new illness categorization method based on Convolutional Neural Networks (CNN) models. For comparison, the popular pre-trained models MobileNet and VGG16 are also used. The primary goal is to offer a reliable and effective technique for the automated identification and categorization of diseases affecting grape leaves, an essential task for the timely diagnosis and medical care of illnesses in the wine industry. Preprocessing methods, such as data augmentation and normalization, are used in the study to improve model performance. Experimental assessments are performed on the dataset to compare the proposed CNN model with MobileNet and VGG16 in terms of accuracy, precision, recall, and F1-score. The modified CNN model is effective at correctly recognizing grape leaf diseases, according to the results. In summary, this thesis advances automated disease identification in viticulture by shedding light on which CNN architectures are most suited for a given job and laying the groundwork for future studies in agricultural image processing.