Recognition of mango and location of picking point on stem based on a multi-task CNN model named YOLOMS

Zhang, Bin; Xia, Yuyang; Wang, Rongrong; Wang, Yong; Yin, Chenghai; Fu, Meng; Fu, Wei

doi:10.1007/s11119-024-10119-y

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…b cx and b cy are the corresponding coordinates of the predicted box. θ is an adjustable parameter used to control how much to focus on the shape cost, which is set to 4 in this study ( Zhang et al., 2024 ).…”

Section: Methodsmentioning

confidence: 99%

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Phenotypic detection of flax plants based on improved Flax-YOLOv5

Sun,

Liu,

Han

et al. 2024

Front. Plant Sci.

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Accurate detection and counting of flax plant organs are crucial for obtaining phenotypic data and are the cornerstone of flax variety selection and management strategies. In this study, a Flax-YOLOv5 model is proposed for obtaining flax plant phenotypic data. Based on the solid foundation of the original YOLOv5x feature extraction network, the network structure was extended to include the BiFormer module, which seamlessly integrates bi-directional encoders and converters, enabling it to focus on key features in an adaptive query manner. As a result, this improves the computational performance and efficiency of the model. In addition, we introduced the SIoU function to compute the regression loss, which effectively solves the problem of mismatch between predicted and actual frames. The flax plants grown in Lanzhou were collected to produce the training, validation, and test sets, and the detection results on the validation set showed that the average accuracy (mAP@0.5) was 99.29%. In the test set, the correlation coefficients (R) of the model’s prediction results with the manually measured number of flax fruits, plant height, main stem length, and number of main stem divisions were 99.59%, 99.53%, 99.05%, and 92.82%, respectively. This study provides a stable and reliable method for the detection and quantification of flax phenotypic characteristics. It opens up a new technical way of selecting and breeding good varieties.

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Section: Methodsmentioning

confidence: 99%

“…(2023) proposed an improved YOLOv5-SE-BiFPN model, which could more effectively detect brown spot lesion areas in kidney beans. Zhang et al. (2024) proposed a multi-task learning method named YOLOMS for mango recognition and rapid location of major picking points.…”

Section: Introductionmentioning

confidence: 99%