Fruit Volume and Leaf-Area Determination of Cabbage by a Neural-Network-Based Instance Segmentation for Different Growth Stages

Abstract

Fruit volume and leaf area are important indicators to draw conclusions about the growth condition of the plant. However, the current methods of manual measuring morphological plant properties, such as fruit volume and leaf area, are time consuming and mainly destructive. In this research, an image-based approach for the non-destructive determination of fruit volume and for the total leaf area over three growth stages for cabbage ( brassica oleracea ) is presented. For this purpose, a mask-region-based convolutional neural network (Mask R-CNN) based on a Resnet-101 backbone was trained to segment the cabbage fruit from the leaves and assign it to the corresponding plant. Combining the segmentation results with depth information through a structure-from-motion approach, the leaf length of single leaves, as well as the fruit volume of individual plants, can be calculated. The results indicated that even with a single RGB camera, the developed methods provided a mean accuracy of fruit volume of 87% and a mean accuracy of total leaf area of 90.9%, over three growth stages on an individual plant level.

Plant phenotyping relevance

画像分割と深度情報を組み合わせ、キャベツ個体の葉面積・果実体積を非破壊推定する手法を開発・精度評価しており、植物表現型取得が研究の中心です。

Code and data availability

The paper's cabbage image dataset (600 images), ground-truth segmentation masks, and trained Mask R-CNN model are not publicly deposited; the Data Availability Statement says data are available only on request from the corresponding author. No authors' public code repository is provided (Mask_RCNN, TensorFlow, Agisoft,

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