A CNN-based model to count the leaves of rosette plants (LC-Net).

Abstract

Plant image analysis is a significant tool for plant phenotyping. Image analysis has been used to assess plant trails, forecast plant growth, and offer geographical information about images. The area segmentation and counting of the leaf is a major component of plant phenotyping, which can be used to measure the growth of the plant. Therefore, this paper developed a convolutional neural network-based leaf counting model called LC-Net. The original plant image and segmented leaf parts are fed as input because the segmented leaf part provides additional information to the proposed LC-Net. The well-known SegNet model has been utilised to obtain segmented leaf parts because it outperforms four other popular Convolutional Neural Network (CNN) models, namely DeepLab V3+, Fast FCN with Pyramid Scene Parsing (PSP), U-Net, and Refine Net. The proposed LC-Net is compared to the other recent CNN-based leaf counting models over the combined Computer Vision Problems in Plant Phenotyping (CVPPP) and KOMATSUNA datasets. The subjective and numerical evaluations of the experimental results demonstrate the superiority of the LC-Net to other tested models.

Plant phenotyping relevance

ロゼット植物の葉数という表現型を画像から推定するCNN手法を開発し、既存モデルおよび標準データセットで比較評価しており、方法が研究の中心である。

Code and data availability

The paper's leaf-counting experiments were run on the CVPPP and KOMATSUNA plant image datasets, which the authors explicitly state are publicly available with download links. No author code or trained model is released.