Weed detection in cabbage fields using RGB and NIR images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41310%2F25%3A102590" target="_blank" >RIV/60460709:41310/25:102590 - isvavai.cz</a>
Alternative codes found
RIV/60460709:41110/25:102590 RIV/60460709:41210/25:102590
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2772375525004630" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2772375525004630</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.atech.2025.101232" target="_blank" >10.1016/j.atech.2025.101232</a>
Alternative languages
Result language
angličtina
Original language name
Weed detection in cabbage fields using RGB and NIR images
Original language description
This article evaluates the effectiveness of integrating near-infrared (NIR) data with RGB imaging in enhancing weed detection and classification in real-time field settings using the YOLO deep learning model family. Data was gathered from sown weed plots and various locations across Bohemia to document diverse plant phenotypes under different field conditions. A multispectral RGB+NIR camera combined with an LED flashlight system was used for imaging. Besides the cabbage crop, 13 weed classes were classified in the images using various YOLO models. The YOLOv10l model provided the best classification results. The use of RGB+NIR data in training resulted in the mean average precision ([email protected]) value of 94.9 %, compared to 94.5 % for RGB-only images, underscoring NIR’s benefits in weed detection. When calculated exclusively for sown species, [email protected] of 97.8 % was achieved for RGB+NIR data. The addition of the NIR images not only increased the classification accuracy but also improved semi-automated annotation efficiency, facilitating faster dataset preparation. These results suggest that NIR-enhanced YOLOv10l holds potential for precision agriculture, enabling targeted interventions that reduce herbicide use. Future research will focus on expanding model adaptability and accessibility for broader agricultural applications.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
40106 - Agronomy, plant breeding and plant protection; (Agricultural biotechnology to be 4.4)
Result continuities
Project
<a href="/en/project/QK22010348" target="_blank" >QK22010348: Autonomous systems as tools for integrated vegetable production.</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
SMART AGRICULTURAL TECHNOLOGY
ISSN
2772-3755
e-ISSN
2772-3755
Volume of the periodical
12
Issue of the periodical within the volume
DEC 2025
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
8
Pages from-to
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UT code for WoS article
001543354000001
EID of the result in the Scopus database
2-s2.0-105011945408