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Title: Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery
Authors: Mamalis, Marios
Kalampokis, Evangelos
Kalfas, Ilias
Tarabanis, Konstantinos
Type: Article
Subjects: FRASCATI::Agricultural sciences
FRASCATI::Engineering and technology
Keywords: YOLO
deep learning
olive trees
precision agriculture
Issue Date: 2023
Source: Algorithms
Volume: 16
Issue: 7
First Page: 343
Abstract: The verticillium fungus has become a widespread threat to olive fields around the world in recent years. The accurate and early detection of the disease at scale could support solving the problem. In this paper, we use the YOLO version 5 model to detect verticillium fungus in olive trees using aerial RGB imagery captured by unmanned aerial vehicles. The aim of our paper is to compare different architectures of the model and evaluate their performance on this task. The architectures are evaluated at two different input sizes each through the most widely used metrics for object detection and classification tasks (precision, recall, mAP@0.5 and mAP@0.5:0.95). Our results show that the YOLOv5 algorithm is able to deliver good results in detecting olive trees and predicting their status, with the different architectures having different strengths and weaknesses.
ISSN: 1999-4893
Other Identifiers: 10.3390/a16070343
Appears in Collections:Department of Business Administration

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