Advancements in Deep Learning for Accurate Classification of Grape Leaves and Diagnosis of Grape Diseases

Abstract Plant diseases are a major factor contributing to agricultural production losses, necessitating effective disease detection and classification methods. Traditional manual approaches heavily rely on expert knowledge, which can introduce biases. However, advancements in computing and image processing have opened up possibilities for leveraging these technologies to assist non-experts in managing plant diseases. Particularly, deep learning techniques have shown remarkable success in assessing and classifying plant health based on digital images. This paper focuses on fine-tuning state-of-the-art pre-trained convolutional neural network (CNN) models and vision transformer models for the detection and diagnosis of grape leaves and diseases using digital images.The experiments were conducted using two datasets: PlantVillage, which encompasses four classes of grape diseases (Black Rot, Leaf Blight, Healthy, and Esca leaves), and Grapevine, which includes five classes for leaf recognition (Ak, Alaidris, Buzgulu, Dimnit, and Nazli). The results of the experiments, involving a total of 14 models based on six well-known CNN architectures and 17 models based on five widely recognized vision transformer architectures, demonstrated the capability of deep learning techniques in accurately distinguishing between grape diseases and recognizing grape leaves. Notably, four CNN models and four vision transformer models achieved 100% accuracy on the test data from the PlantVillage dataset, while one CNN model and one vision transformer model achieved 100% accuracy on the Grapevine dataset. Among the models tested, the Swinv2-Base model stood out by achieving 100% accuracy on both the PlantVillage and Grapevine datasets. The proposed deep learning-based approach is believed to have the potential to enhance crop productivity through early detection of grape diseases. Additionally, it is expected to offer a fresh perspective to the agricultural sector by providing insights into the characterization of various grape varieties.