Weed detection in cabbage fields using RGB and NIR images
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 docum...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525004630 |
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| author | Adam Hruška Pavel Hamouz Jakub Lev Josef Pavlíček Milan Kroulík Kateřina Hamouzová Pavlína Košnarová Josef Holec Pavel Kouřím |
| author_facet | Adam Hruška Pavel Hamouz Jakub Lev Josef Pavlíček Milan Kroulík Kateřina Hamouzová Pavlína Košnarová Josef Holec Pavel Kouřím |
| author_sort | Adam Hruška |
| collection | DOAJ |
| 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 (mAP@0.5) 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, mAP@0.5 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. |
| format | Article |
| id | doaj-art-8e4d14bf29e049a68bcd564678fcba67 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-8e4d14bf29e049a68bcd564678fcba672025-08-20T03:58:14ZengElsevierSmart Agricultural Technology2772-37552025-12-011210123210.1016/j.atech.2025.101232Weed detection in cabbage fields using RGB and NIR imagesAdam Hruška0Pavel Hamouz1Jakub Lev2Josef Pavlíček3Milan Kroulík4Kateřina Hamouzová5Pavlína Košnarová6Josef Holec7Pavel Kouřím8Czech University of Life Sciences Prague, Kamýcká 129, Prague 165 00, Czech RepublicCorresponding author.; Czech University of Life Sciences Prague, Kamýcká 129, Prague 165 00, Czech RepublicCzech University of Life Sciences Prague, Kamýcká 129, Prague 165 00, Czech RepublicCzech University of Life Sciences Prague, Kamýcká 129, Prague 165 00, Czech RepublicCzech University of Life Sciences Prague, Kamýcká 129, Prague 165 00, Czech RepublicCzech University of Life Sciences Prague, Kamýcká 129, Prague 165 00, Czech RepublicCzech University of Life Sciences Prague, Kamýcká 129, Prague 165 00, Czech RepublicCzech University of Life Sciences Prague, Kamýcká 129, Prague 165 00, Czech RepublicCzech University of Life Sciences Prague, Kamýcká 129, Prague 165 00, Czech RepublicThis 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 (mAP@0.5) 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, mAP@0.5 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.http://www.sciencedirect.com/science/article/pii/S2772375525004630Weed detectionComputer visionYoloRGB+NIR imagingVegetables |
| spellingShingle | Adam Hruška Pavel Hamouz Jakub Lev Josef Pavlíček Milan Kroulík Kateřina Hamouzová Pavlína Košnarová Josef Holec Pavel Kouřím Weed detection in cabbage fields using RGB and NIR images Smart Agricultural Technology Weed detection Computer vision Yolo RGB+NIR imaging Vegetables |
| title | Weed detection in cabbage fields using RGB and NIR images |
| title_full | Weed detection in cabbage fields using RGB and NIR images |
| title_fullStr | Weed detection in cabbage fields using RGB and NIR images |
| title_full_unstemmed | Weed detection in cabbage fields using RGB and NIR images |
| title_short | Weed detection in cabbage fields using RGB and NIR images |
| title_sort | weed detection in cabbage fields using rgb and nir images |
| topic | Weed detection Computer vision Yolo RGB+NIR imaging Vegetables |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525004630 |
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