Deep Learning-Based System for Early Symptoms Recognition of Grapevine Red Blotch and Leafroll Diseases and Its Implementation on Edge Computing Devices

In recent years, the agriculture sector has undergone a significant digital transformation, integrating artificial intelligence (AI) technologies to harness and analyze the growing volume of data from diverse sources. Machine learning (ML), a powerful branch of AI, has emerged as an essential tool for developing knowledge-based agricultural systems. Grapevine red blotch disease (GRBD) and grapevine leafroll disease (GLD) are viral infections that severely impact grapevine productivity and longevity, leading to considerable economic losses worldwide. Conventional diagnostic methods for these diseases are costly and time-consuming. To address this, ML-based technologies have been increasingly adopted by researchers for early detection by analyzing the foliar symptoms linked to viral infections. This study focused on detecting GRBD and GLD symptoms using Convolutional Neural Networks (CNNs) in computer vision. YOLOv5 outperformed the other deep learning (DL) models tested, such as YOLOv3, YOLOv8, and ResNet-50, where it achieved 95.36% Precision, 95.77% Recall, and an F1-score of 95.56%. These metrics underscore the model’s effectiveness at accurately classifying grapevine leaves with and without GRBD and/or GLD symptoms. Furthermore, benchmarking was performed with two edge computer devices, where Jetson NANO obtained the best cost–benefit performance. The findings support YOLOv5 as a reliable tool for early diagnosis, offering potential economic benefits for large-scale agricultural monitoring.