Reliable plant segmentation under variable greenhouse illumination conditions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15640%2F25%3A73627759" target="_blank" >RIV/61989592:15640/25:73627759 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0168169924011025?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0168169924011025?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compag.2024.109711" target="_blank" >10.1016/j.compag.2024.109711</a>

Result language

angličtina

Original language name

Reliable plant segmentation under variable greenhouse illumination conditions

Original language description

The effectiveness of image-based sensors in capturing meaningful morphological and physiological information relies on the accuracy of the segmentation strategy used. Fluctuating light conditions and background noise can reduce the reliability of plant tissue pixel classification using color-based threshold strategies even under semicontrolled greenhouse conditions. The implementation of multiple criteria classification strategies can provide a more reliable segmentation of focal objects regardless of the environmental influence, but some approaches can be time-consuming and computationally intensive. To identify the most accurate method for semantic segmentation of lettuce plants grown in a greenhouse under varying light conditions, we compared a single-color threshold baseline strategy using Otsu segmentation with two advanced machine learning segmentation techniques: Random Forest classification and U-Net Convolutional Neural Network (CNN). Each method was trained and evaluated to determine its effectiveness in accurately segmenting lettuce plants based on their performance under different light conditions. The U-Net model demonstrated superior performance on images captured under varying illumination conditions and crop developmental stages. U-Net achieved average Intersection over Union (IoU) and F1 Score values of 0.940 and 0.969, respectively. In contrast, the Random Forest yielded average IoU and F1 Score values of 0.842 and 0.913, while Otsu segmentation produced values of 0.842 and 0.906. These results highlight the efficacy of the U-Net model in segmentation tasks under variable greenhouse conditions. Additionally, the U-Net model automatic feature extraction step outperformed the inefficient feature extraction step involved in training a Random Forest-based segmentation. Despite a relatively small set of training images, U-Net&apos;s architecture more effectively resolved the segmentation inaccuracies associated with varying greenhouse light conditions, demonstrating U-Net&apos;s advantage for non-robust, color-based segmentation.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

40106 - Agronomy, plant breeding and plant protection; (Agricultural biotechnology to be 4.4)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

COMPUTERS AND ELECTRONICS IN AGRICULTURE

ISSN

0168-1699

e-ISSN

1872-7107

Volume of the periodical

229

Issue of the periodical within the volume

February

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

nestránkováno

UT code for WoS article

001388521000001

EID of the result in the Scopus database

2-s2.0-85211626946