A Deep Learning Approach for Precision Viticulture, Assessing Grape Maturity via YOLOv7
Author:
Badeka Eftichia1, Karapatzak Eleftherios2ORCID, Karampatea Aikaterini2, Bouloumpasi Elisavet2ORCID, Kalathas Ioannis1ORCID, Lytridis Chris1ORCID, Tziolas Emmanouil1ORCID, Tsakalidou Viktoria Nikoleta1, Kaburlasos Vassilis G.1ORCID
Affiliation:
1. Human-Machines Interaction Laboratory (HUMAIN-Lab), Department of Computer Science, International Hellenic University (IHU), 65404 Kavala, Greece 2. Department of Agricultural Biotechnology and Oenology, International Hellenic University, 66100 Drama, Greece
Abstract
In the viticulture sector, robots are being employed more frequently to increase productivity and accuracy in operations such as vineyard mapping, pruning, and harvesting, especially in locations where human labor is in short supply or expensive. This paper presents the development of an algorithm for grape maturity estimation in the framework of vineyard management. An object detection algorithm is proposed based on You Only Look Once (YOLO) v7 and its extensions in order to detect grape maturity in a white variety of grape (Assyrtiko grape variety). The proposed algorithm was trained using images received over a period of six weeks from grapevines in Drama, Greece. Tests on high-quality images have demonstrated that the detection of five grape maturity stages is possible. Furthermore, the proposed approach has been compared against alternative object detection algorithms. The results showed that YOLO v7 outperforms other architectures both in precision and accuracy. This work paves the way for the development of an autonomous robot for grapevine management.
Funder
Technology for Skillful Viniculture Operational Program “Competitiveness, Entrepreneurship and Innovation” Greece and the European Union
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference35 articles.
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