EVALUATING YOLO MODELS FOR GRAPE MOTH DETECTION IN INSECT TRAPS

被引：0

作者：

Teixeira, Ana Claudia ^{[1
,2
]}

Carneiro, Gabriel ^{[1
,2
]}

Morais, Raul ^{[1
,3
]}

Sousa, Joaquim J. ^{[1
,2
]}

Cunha, Antonio ^{[1
,2
]}

机构：

[1] Univ Tras Os Montes & Alto Douro, P-5000801 Vila Real, Portugal

[2] Inst Syst & Comp Engn Technol & Sci INESC TEC, P-4200465 Porto, Portugal

[3] Univ Tras os Montes e Alto Douro, Ctr Res & Technol Agro Environm & Biol Sci, P-5000801 Vila Real, Portugal

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

基金：

欧盟地平线“2020”;

关键词：

Insect detection; smart pest monitoring; deep learning; YOLO models;

D O I：

10.1109/IGARSS52108.2023.10282249

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

The grape moth is a common pest that affects grapevines by consuming both fruit and foliage, rendering grapes deformed and unsellable. Integrated pest management for the grape moth heavily relies on pheromone traps, which serve a crucial function by identifying and tracking adult moth populations. This information is then used to determine the most appropriate time and method for implementing other control techniques. This study aims to find the best method for detecting small insects. We evaluate the following recent YOLO models: v5, v6, v7, and v8 for detecting and counting grape moths in insect traps. The best performance was achieved by YOLOv8, with an average precision of 92.4% and a counting error of 8.1%.

引用

页码：3526 / 3529

页数：4

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