A Deep Learning Approach for Precision Viticulture, Assessing Grape Maturity via YOLOv7.

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.

Plant phenotyping relevance

ブドウ果実の成熟段階という植物状態を画像から推定する物体検出法を開発し、複数アルゴリズムと比較検証しており、表現型取得手法が中心である。

Code and data availability

The article describes a custom grape maturity image dataset (Assyrtiko, Drama, Greece) and YOLOv7/Detectron2 experiments, but contains no public deposit, availability statement, or authors' URL for the dataset, annotations, code, or trained models. The only external URL in the text is a cited prior-work conference page

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