Abstract
The detection of plant constituents is a crucial issue in precision agriculture, as monitoring these enables the automatic analysis of factors such as growth rate, health status, and crop yield. Tomatoes (Solanum sp.) are an economically and nutritionally important crop in Mexico and worldwide, which is why automatic monitoring of these plants is of great interest. Detecting leaves on images of outdoor tomato plants is challenging due to the significant variability in the visual appearance of leaves. Factors like overlapping leaves, variations in lighting, and environmental conditions further complicate the task of detection. This paper proposes modifications to the Yolov11n architecture to improve the detection of tomato leaves in images of complex outdoor environments by incorporating attention modules, transformers, and WIoUv3 loss for bounding box regression. The results show that our proposal led to a 26.75% decrease in the number of parameters and a 7.94% decrease in the number of FLOPs compared with the original version of Yolov11n. Our proposed model outperformed Yolov11n and Yolov12n architectures in recall, F1-measure, and mAP@50 metrics.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
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Claims 3
Claim · model-written summary
The proposed model reduced the number of parameters by 26.75% compared with the original Yolov11n.
- Taxon
- tomato
Solanum lycopersicume1 - Outcome
- number of parameterse2
- Direction
- decreasee2
- Value
- 26.75%e2
- Comparator
- original version of Yolov11ne2
- Setting
- complex outdoor environmentse1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“improve the detection of tomato leaves in images of complex outdoor environments”
e2“led to a 26.75% decrease in the number of parameters and a 7.94% decrease in the number of FLOPs compared with the original version of Yolov11n”
Claim · model-written summary
The proposed model reduced the number of FLOPs by 7.94% compared with the original Yolov11n.
- Taxon
- tomatoSolanum lycopersicume1
- Outcome
- number of FLOPse2
- Direction
- decreasee2
- Value
- 7.94%e2
- Comparator
- original version of Yolov11ne2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“improve the detection of tomato leaves in images of complex outdoor environments”
e2“led to a 26.75% decrease in the number of parameters and a 7.94% decrease in the number of FLOPs compared with the original version of Yolov11n”
Claim · model-written summary
The proposed model outperformed Yolov11n and Yolov12n in recall, F1-measure and mAP@50 for tomato leaf detection.
- Taxon
- tomatoSolanum lycopersicume1
- Outcome
- recall, F1-measure, and mAP@50e3
- Direction
- outperformede3
- Comparator
- Yolov11n and Yolov12n architecturese3
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“improve the detection of tomato leaves in images of complex outdoor environments”
e3“Our proposed model outperformed Yolov11n and Yolov12n architectures in recall, F1-measure, and mAP@50 metrics”