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Publication
Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models
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Advisor(s)
Abstract(s)
Modern agriculture faces important challenges for feeding a
fast-growing planet’s population in a sustainable way. One of the most
important challenges faced by agriculture is the increasing destruction
caused by pests to important crops. It is very important to control and
manage pests in order to reduce the losses they cause. However, pest
detection and monitoring are very resources consuming tasks. The recent
development of computer vision-based technology has made it possible
to automatize pest detection efficiently.
In Mediterranean olive groves, the olive fly (Bactrocera oleae Rossi)
is considered the key-pest of the crop. This paper presents olive fly
detection using the lightweight YOLO-based model for versions 7 and 8,
respectively, YOLOv7-tiny and YOLOv8n. The proposed object detection
models were trained, validated, and tested using two different image
datasets collected in various locations of Portugal and Greece. The
images are constituted by sticky yellow trap photos and by McPhail
trap photos with olive fly exemplars. The performance of the models
was evaluated using precision, recall, and mAP.95. The YOLOV7-tiny
model best performance is 88.3% of precision, 85% of Recall, 90% of
mAP.50, and 53% of mAP.95. The YOLOV8n model best performance
is 85% of precision, 85% of Recall, 90% mAP.50, and 55% of mAP.50
YOLO8n model achieved worst results than YOLOv7-tiny for a dataset
without negative images (images without olive fly exemplars). Aiming at
installing an experimental prototype in the olive grove, the YOLOv8n
model was implemented in a Ubuntu Server 23.04 Raspberry PI 3 microcomputer.
Description
Keywords
Olives sustainable production Convolutional Neural Network Deep Learning YOLOv7 YOLOv8
Pedagogical Context
Citation
Alves, Adília; Pereira, José; Khanal, Salik; Morais, A. Jorge; Filipe, Vitor (2024). Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 50–62. ISBN 978-3-031-53035-7