Deep learning-based high-throughput detection of <i>in vitro</i> germination to assess pollen viability from microscopic images

Zhang, Mengwei; Zhao, Jia; Hoshino, Yoichiro

doi:10.1093/jxb/erad315

Cited by 3 publications

(1 citation statement)

References 53 publications

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“…The average test time of a single image was 0.011s, and the average absolute error in prediction of germination rate was within 0.1. Zhang et al (2023) proposed a mask R-CNN model trained with microscopic images of tree peony pollen for fast testing of the pollen germination rate and pollen tube length.…”

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confidence: 99%

An Exploration of the Influence of ZnO NPs Treatment on Germination of Radish Seeds under Salt Stress based on the YOLOv8-R Lightweight Model

Ouyang,

Fu,

Zhong

et al. 2024

Preprint

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Background Since traditional germination test methods have drawbacks such as slow efficiency, proneness to error, and damage to seeds, a non-destructive testing method is proposed for full-process germination of radish seeds, which improves the monitoring efficiency of seed quality. Results Based on YOLOv8n, a lightweight test model YOLOv8-R is proposed, where the number of parameters, the amount of calculation, and size of weights are significantly reduced by replacing the backbone network with PP-LCNet, the neck part with cross-scale feature fusion module CCFM, the original C2f of the neck part with OREPA, and the original SPPF with FocalModulation. The ablation test and comparative test prove the performance of the model. With adoption of germination rate, germination index, and germination potential as the three vitality indicators, the seed germination phenotype collection system and YOLOv8-R model are used to analyze the full time-series sequence effects of different ZnO NPs concentrations on germination of radish seeds under varying degrees of salt stress. Conclusions The results show that salt stress inhibits the germination of radish seeds and that the inhibition effect is more obvious with the increased concentration of NaCl solution; in cultivation with deionized water, the germination rate of radish seeds does not change significantly with increased concentration of ZnO NPs, but the germination index and germination potential increase initially and then decline; in cultivation with NaCl solution, the germination rate, germination potential and germination index of radish seeds first increase and then decline with increased concentration of ZnO NPs.

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confidence: 99%

An Exploration of the Influence of ZnO NPs Treatment on Germination of Radish Seeds under Salt Stress based on the YOLOv8-R Lightweight Model

Ouyang,

Fu,

Zhong

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

An exploration of the influence of ZnO NPs treatment on germination of radish seeds under salt stress based on the YOLOv8-R lightweight model

Ouyang,

Fu,

Zhong

et al. 2024

Plant Methods

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Background Since traditional germination test methods have drawbacks such as slow efficiency, proneness to error, and damage to seeds, a non-destructive testing method is proposed for full-process germination of radish seeds, which improves the monitoring efficiency of seed quality. Results Based on YOLOv8n, a lightweight test model YOLOv8-R is proposed, where the number of parameters, the amount of calculation, and size of weights are significantly reduced by replacing the backbone network with PP-LCNet, the neck part with CCFM, the C2f of the neck part with OREPA, the SPPF with FocalModulation, and the Detect of the head part with LADH. The ablation test and comparative test prove the performance of the model. With adoption of germination rate, germination index, and germination potential as the three vitality indicators, the seed germination phenotype collection system and YOLOv8-R model are used to analyze the full time-series sequence effects of different ZnO NPs concentrations on germination of radish seeds under varying degrees of salt stress. Conclusions The results show that salt stress inhibits the germination of radish seeds and that the inhibition effect is more obvious with the increased concentration of NaCl solution; in cultivation with deionized water, the germination rate of radish seeds does not change significantly with increased concentration of ZnO NPs, but the germination index and germination potential increase initially and then decline; in cultivation with NaCl solution, the germination rate, germination potential and germination index of radish seeds first increase and then decline with increased concentration of ZnO NPs.

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YOLOv8-Pearpollen: Method for the Lightweight Identification of Pollen Germination Vigor in Pear Trees

Sun,

Chen,

Liu

et al. 2024

Agriculture

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Pear trees must be artificially pollinated to ensure yield, and the efficiency of pollination and the quality of pollen germination affect the size, shape, taste, and nutritional value of the fruit. Detecting the pollen germination vigor of pear trees is important to improve the efficiency of artificial pollination and consequently the fruiting rate of pear trees. To overcome the limitations of traditional manual detection methods, such as low efficiency and accuracy and high cost, and to meet the requirements of screening high-quality pollen to promote the yield and production of fruit trees, we proposed a detection method for pear pollen germination vigor named YOLOv8-Pearpollen, an improved version of YOLOv8-n. A pear pollen germination dataset was constructed, and the image was enhanced using Blend Alpha to improve the robustness of the data. A combination of knowledge distillation and model pruning was used to reduce the complexity of the model and the difficulty of deployment in hardware facilities while ensuring that the model achieved or approached the detection effect of a large-volume model that can adapt to the actual requirements of agricultural production. Various ablation tests on knowledge distillation and model pruning were conducted to obtain a high-quality lightweighting method suitable for this model. Test results showed that the mAP of YOLOv8-Pearpollen reached 96.7%. The Params, FLOPs, and weights were only 1.5 M, 4.0 G, and 3.1 MB, respectively, and the detection speed was 147.1 FPS. A high degree of lightweighting and superior detection accuracy were simultaneously achieved.

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Deep learning-based high-throughput detection of in vitro germination to assess pollen viability from microscopic images

Cited by 3 publications

References 53 publications

An Exploration of the Influence of ZnO NPs Treatment on Germination of Radish Seeds under Salt Stress based on the YOLOv8-R Lightweight Model

An Exploration of the Influence of ZnO NPs Treatment on Germination of Radish Seeds under Salt Stress based on the YOLOv8-R Lightweight Model

An exploration of the influence of ZnO NPs treatment on germination of radish seeds under salt stress based on the YOLOv8-R lightweight model

YOLOv8-Pearpollen: Method for the Lightweight Identification of Pollen Germination Vigor in Pear Trees

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