Non-destructive leaf area estimation based on a semantic segmentation deep neural network

Abstract

Leaves play a fundamental role in the plant body by performing photosynthesis. Their morphological characteristics, leaf area and other surface parameters, can help to explain various processes, such as climate change, ecological relationships, and agricultural productivity. However, most existing methods for measuring leaf surface dimensions are expensive and often complicated. Additionally, several methods employ destructive approaches, preventing the monitoring of plant growth. In this work, we introduce a new deep neural network to estimate bean’s leaf area from images containing a salient leaf and a marker. Our method is based on the DeepLabv3+ architecture for semantic segmentation and comprises one encoder and two decoders, which estimate the image segmentation and the pixel areas of the objects of interest. An extensive quantitative and qualitative analysis was conducted with the model’s predictions on 3374 images of 300 different leaves. Results indicate that the trained model is capable of estimating the leaf and marker areas with only one input image.

Plant phenotyping relevance

画像から葉面積を非破壊推定する深層学習手法を開発し、多数画像で評価しており、植物表現型の取得・抽出が研究の中心である。

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