Invertebrates detection with YOLOv5: Towards study of soil organisms using deep learning

The investigation of the complicated underground life via automatic technique is in high demand in recent days. Using Convolutional Neural Network (CNN) to detect soil invertebrates is an interesting approach, although most studies on the topic have focused on other solutions. The creation of state-of-the-art technique through this work will be a significant step in soil ecology, bio-science and agriculture in effectively exploring the different types of invertebrates, their behaviors and interactions. In this paper, generating and annotating images containing seven classes of invertebrates is firstly presented. Then various automatic detections of the invertebrates using YOLOv5 algorithm on these images are performed and evaluated.

Bibliographic Details

Main Authors:	Pruvost, Emma, Tulet, Hadrien, Delort, Etienne, Shokouh, Ghulam Sakhi, Montesinos, Philippe, Magnier, Baptiste, Jourdan, Christophe, Belaud, Emma, Hedde, Mickaël
Format:	conference_item biblioteca
Language:	eng
Published:	IEEE
Online Access:	http://agritrop.cirad.fr/603811/ http://agritrop.cirad.fr/603811/8/ID603811.pdf
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