Advancing precision agriculture with computer vision: A comparative study of YOLO models for weed and crop recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12410%2F25%3A43908340" target="_blank" >RIV/60076658:12410/25:43908340 - isvavai.cz</a>
Alternative codes found
RIV/60076658:12220/25:43908340
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0261219424005040?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0261219424005040?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.cropro.2024.107076" target="_blank" >10.1016/j.cropro.2024.107076</a>
Alternative languages
Result language
angličtina
Original language name
Advancing precision agriculture with computer vision: A comparative study of YOLO models for weed and crop recognition
Original language description
In this study, we investigated the application of three convolutional neural network models YOLOv5, YOLOR, and YOLOv7 for precisely detecting individual radish plants, radish rows, and weeds. A comprehensive dataset was created, capturing diverse conditions and annotated for three target classes: radish, radish-line, and weed. Through extensive experimentation involving 39 combinations of model types, batch sizes (2, 4, 8), and learning rates (0.1, 0.01, 0.001), we determined that the YOLOv5-x model with a batch size of 4 and a learning rate of 0.01 offers superior performance. This configuration achieved a remarkable 99% accuracy for the radish class, 98% for radish-line, and 91% for weed, as confirmed by confusion matrices. Further analysis using the F1-score, Precision-Recall (PR) curves, and training progress plots underscored the model's robustness, particularly its high mAP_0.5:0.95 score. Despite the Weed class posing greater detection challenges, likely due to its lower representation in the dataset, the YOLOv5-x outperformed YOLOR-D6 and YOLOv7-D6 in critical metrics after 300 epochs. This research not only highlights the efficacy of YOLOv5-x in agricultural applications but also suggests potential enhancements in data annotation and model training strategies to further improve weed detection. Our findings provide significant insights for developing automated, high-precision plant-weed detection systems, contributing to more efficient and sustainable agricultural practices.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/TM03000063" target="_blank" >TM03000063: Joint research and development of key technologies for efficient intelligent fine production and intelligent management and control of facility vegetable plants</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
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
Crop protection
ISSN
0261-2194
e-ISSN
1873-6904
Volume of the periodical
190
Issue of the periodical within the volume
April
Country of publishing house
GB - UNITED KINGDOM
Number of pages
11
Pages from-to
"1–11"
UT code for WoS article
001434346400001
EID of the result in the Scopus database
2-s2.0-85211168412