Fusing Xception and ResNet50 features for robust grape leaf disease classification

Abstract

Grapes are one of the most widely cultivated fruits worldwide, and their economic and nutritional value makes them a significant crop in agriculture. However, grape plants are vulnerable to various diseases that can have detrimental effects on crop yield and quality. Accurate and timely identification of grape leaf diseases is crucial for efficient disease control and ensuring sustainable viticulture practices. In this study, we present a disease classification model specifically designed for grape leaves. The model incorporates bilinear pooling, utilizing the intermediate features extracted from two powerful convolutional neural network (CNN) models, Xception, and ResNet50. The outer product operation is applied to the extracted features, enabling the capture of intricate interactions and relationships between the features. The accurate classification of grape leaf diseases provided by our model offers significant benefits for grape farmers, vineyard owners, and agricultural researchers. It facilitates early disease detection, enabling proactive disease management strategies. Additionally, it assists in optimizing crop health, minimizing yield losses, and ensuring sustainable grape production.