AI-powered Solution for Plant Disease Detection in Viticulture

• Published in: Procedia computer science 2024, Vol.238, p.468-475
• Main Authors: Madeira, Miguel, Porfírio, Rui Pedro, Santos, Pedro Albuquerque, Madeira, Rui Neves
• Format: Article
• Language: English
• Subjects: Convolutional Neural Networks; Data Visualization; Deep Learning; Digital Agriculture; Plant Disease Detection; Viticulture
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2024.06.049

In an era dominated by the intersection of advanced technology and traditional industries, the domain of agriculture is on the verge of a revolutionary transformation. This article introduces a solution for vineyard producers, harnessing satellite imagery, weather data, and deep learning (DL) to identify vineyard diseases robustly. This solution, designed for proactive plant health management, stands as a transformative tool towards digital viticulture. Such tools transition from luxuries to essentials as vineyards confront evolving challenges like climate change and new pathogens. Our research builds on the hypothesis that customising deep learning architectures for specific tasks is crucial in enhancing their effectiveness. We contribute by introducing a tailored convolutional neural network (CNN) architecture, developed specifically for the classification of plant diseases using vineyard imagery. The experimental results demonstrate that our custom CNN architecture exhibits performance on par with established state-of-the-art models like ResNet50 and MobileNetV2, underscoring the value of specialized solutions in addressing the unique challenges of viticulture. This paper introduces an overview of the solution’s architecture, presents the implementation of DL modules with their corresponding results, and describes use case scenarios.