Evaluation of Convolutional and Transformer-Based Detectors for Weed Detection in Tomato Plantations
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917525931950080 |
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| author | Espinosa, Alcides Toledo Hernández, Gerardo Antonio Álvarez Zamora-Suárez, Ángel Eduardo Bolaños, Miguel Vásquez, Juan Irving |
| author_facet | Espinosa, Alcides Toledo Hernández, Gerardo Antonio Álvarez Zamora-Suárez, Ángel Eduardo Bolaños, Miguel Vásquez, Juan Irving |
| contents | This paper presents a comparative evaluation of convolutional and transformer-based object detection architectures for early weed detection in tomato plantations. Representative models from each paradigm are considered, including YOLOv26-nano, a recent variant of the YOLO family, and RT-DETR Large and RF-DETR Medium as transformer-based architectures. The evaluation was conducted on the GROUNDBASED_WEED dataset, considering six weed classes and an additional category corresponding to unidentified plants, which allowed for the assessment of performance in terms of detection accuracy and computational efficiency using metrics such as precision, recall, average precision, and inference speed, as well as non-parametric statistical tests. The results highlight a clear trade-off between efficiency and contextual modeling: CNN-based detectors achieve high performance at a lower computational cost, while transformer-based approaches offer better global context capture at the expense of higher resource demands. These results provide practical criteria for model selection in precision agriculture applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00908 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evaluation of Convolutional and Transformer-Based Detectors for Weed Detection in Tomato Plantations Espinosa, Alcides Toledo Hernández, Gerardo Antonio Álvarez Zamora-Suárez, Ángel Eduardo Bolaños, Miguel Vásquez, Juan Irving Computer Vision and Pattern Recognition This paper presents a comparative evaluation of convolutional and transformer-based object detection architectures for early weed detection in tomato plantations. Representative models from each paradigm are considered, including YOLOv26-nano, a recent variant of the YOLO family, and RT-DETR Large and RF-DETR Medium as transformer-based architectures. The evaluation was conducted on the GROUNDBASED_WEED dataset, considering six weed classes and an additional category corresponding to unidentified plants, which allowed for the assessment of performance in terms of detection accuracy and computational efficiency using metrics such as precision, recall, average precision, and inference speed, as well as non-parametric statistical tests. The results highlight a clear trade-off between efficiency and contextual modeling: CNN-based detectors achieve high performance at a lower computational cost, while transformer-based approaches offer better global context capture at the expense of higher resource demands. These results provide practical criteria for model selection in precision agriculture applications. |
| title | Evaluation of Convolutional and Transformer-Based Detectors for Weed Detection in Tomato Plantations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.00908 |