Machine Learning-Based Plant Detection Algorithms to Automate Counting Tasks Using 3D Canopy Scans

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F21%3A88948" target="_blank" >RIV/60460709:41110/21:88948 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/1424-8220/21/23/8022" target="_blank" >https://www.mdpi.com/1424-8220/21/23/8022</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s21238022" target="_blank" >10.3390/s21238022</a>

Jazyk výsledku

čeština

Název v původním jazyce

Machine Learning-Based Plant Detection Algorithms to Automate Counting Tasks Using 3D Canopy Scans

Popis výsledku v původním jazyce

This study tested whether machine learning (ML) methods can effectively separate individual plants from complex 3D canopy laser scans as a prerequisite to analyzing particular plant features. For this, we scanned mung bean and chickpea crops with PlantEye (R) laser scanners. Firstly, we segmented the crop canopies from the background in 3D space using the Region Growing Segmentation algorithm. Then, Convolutional Neural Network (CNN) based ML algorithms were fine-tuned for plant counting. Application of the CNN-based (Convolutional Neural Network) processing architecture was possible only after we reduced the dimensionality of the data to 2D. This allowed for the identification of individual plants and their counting with an accuracy of 93,18% and 92,87% for mung bean and chickpea plants, respectively. These steps were connected to the phenotyping pipeline, which can now replace manual counting operations that are inefficient, costly, and error-prone. The use of CNN in this study was innovatively sol

Název v anglickém jazyce

Machine Learning-Based Plant Detection Algorithms to Automate Counting Tasks Using 3D Canopy Scans

Popis výsledku anglicky

This study tested whether machine learning (ML) methods can effectively separate individual plants from complex 3D canopy laser scans as a prerequisite to analyzing particular plant features. For this, we scanned mung bean and chickpea crops with PlantEye (R) laser scanners. Firstly, we segmented the crop canopies from the background in 3D space using the Region Growing Segmentation algorithm. Then, Convolutional Neural Network (CNN) based ML algorithms were fine-tuned for plant counting. Application of the CNN-based (Convolutional Neural Network) processing architecture was possible only after we reduced the dimensionality of the data to 2D. This allowed for the identification of individual plants and their counting with an accuracy of 93,18% and 92,87% for mung bean and chickpea plants, respectively. These steps were connected to the phenotyping pipeline, which can now replace manual counting operations that are inefficient, costly, and error-prone. The use of CNN in this study was innovatively sol

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

SENSORS

ISSN

1424-8220

e-ISSN

1424-8220

Svazek periodika

21

Číslo periodika v rámci svazku

23

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

20

Strana od-do

0-0

Kód UT WoS článku

000734652100001

EID výsledku v databázi Scopus

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