A Streamlined Vgg-19 Classification Model for Multi-Crop Leaf Disease Prediction

Every country economy is depending heavily on agriculture because it produces crops. Identifying leaf diseases is one of the most crucial parts of keeping a country that is agriculturally advanced. Artificial intelligence (AI) in agriculture has emerged as the most significant application in recent years. An early prediction of the type of disease affecting the plant leaves is an essential objective. To classify the current diseases type and distinguish between healthy and infected leaves, the proposed study mainly employs the Visual Geometry Group (VGG 19) classification model. The features extraction of images using a wavelet transform for extracting the features of the leaves. The model is trained and evaluated using the Apple Leaf Disease Symptoms dataset, and the adaptive wiener filter is used to enhance the images and remove noise from the leaf images, while the k means clustering algorithm divides the regions in the leaf images. Accuracy, sensitivity, specificity, recall, and F1 score were among the performance measure parameters that were computed and tracked. This classifier is implemented using the python software. The findings show that it offers greater accuracy and the results are then contrasted with those of earlier classification methods. By identifying plant illnesses early and implementing the right treatment, this approach aims to assist farmers in protecting resources and avoiding financial loss.