Abstract

The global cultivation of grapes reaches approximately 77.8 million tons annually, according to the International Organization of Vine and Wine. While grapes remain a vital agricultural commodity and dietary staple worldwide, their production faces serious threats from common diseases like black rot, Esca, and leaf blight. Current disease detection methods in modern vineyards primarily depend on manual visual inspection, a practice that often delays diagnosis and leads to reduced yields and compromised fruit quality. The integration of automated detection methods, particularly those based on machine learning, is crucial for promoting sustainable viticulture. This study leverages the power of deep learning, a subfield of machine learning particularly adept at interpreting image data. Specifically, we implemented Convolutional Neural Networks (CNNs) to classify images of grapevine leaves as either healthy or diseased. The models employed included a custom-built baseline CNN and a range of advanced transfer learning models: DenseNet121, EfficientNetB7, MobileNetV2, ResNet50, and VGG16. While we initially hypothesized that ResNet50 would yield the highest accuracy due to its deep architecture, experimental results revealed that EfficientNetB7 outperformed all other models. To further enhance classification performance, we constructed a max-voting ensemble using the top three performing models. This ensemble approach outmatched the performance of individual models. The final model was deployed via a web-based interface, enabling vineyard professionals and growers to detect black rot, Esca, leaf blight, or confirm leaf health by uploading images captured in real-world vineyard environments.