Comparative analysis of popular cnn based deep learning models for tree trunk detection in orchards

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F25271121%3A_____%2F24%3AN0000129" target="_blank" >RIV/25271121:_____/24:N0000129 - isvavai.cz</a>

Výsledek na webu

<a href="http://nnw.cz/doi/2024/NNW.2024.34.014.pdf" target="_blank" >http://nnw.cz/doi/2024/NNW.2024.34.014.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2024.34.014" target="_blank" >10.14311/NNW.2024.34.014</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparative analysis of popular cnn based deep learning models for tree trunk detection in orchards

Popis výsledku v původním jazyce

This study compares machine vision deep learning models based on convolutional neural networks to detect tree trunks in orchards from camera images, with a primary focus on apple trees. Two distinct datasets are used, one original with apple trees and another publicly available featuring vineyard trunks. Multiple deep learning models are tested and compared in order to evaluate their efficacy in tree trunk detection. Research not only provides insight into the performance of various models but also serves as a valuable benchmark for assessing achievable results in orchard-based machine vision applications. The findings contribute to the field’s understanding of tree trunk detection, facilitating advancements in agricultural automation.

Název v anglickém jazyce

Comparative analysis of popular cnn based deep learning models for tree trunk detection in orchards

Popis výsledku anglicky

This study compares machine vision deep learning models based on convolutional neural networks to detect tree trunks in orchards from camera images, with a primary focus on apple trees. Two distinct datasets are used, one original with apple trees and another publicly available featuring vineyard trunks. Multiple deep learning models are tested and compared in order to evaluate their efficacy in tree trunk detection. Research not only provides insight into the performance of various models but also serves as a valuable benchmark for assessing achievable results in orchard-based machine vision applications. The findings contribute to the field’s understanding of tree trunk detection, facilitating advancements in agricultural automation.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40500 - Other agricultural sciences

Projekt

<a href="/cs/project/QK21010170" target="_blank" >QK21010170: Nová koncepce sadů s nástupem technologií 4.0</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Neural Network World

ISSN

2336-4335

e-ISSN

—

Svazek periodika

34

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

15

Strana od-do

263-277

Kód UT WoS článku

001419989000001

EID výsledku v databázi Scopus

—