Biotechnology Bulletin ›› 2024, Vol. 40 ›› Issue (9): 123-130.doi: 10.13560/j.cnki.biotech.bull.1985.2024-0511
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XIA Shi-xuan1,2(), GENG Ze-dong1, ZHU Guang-tao3, ZHANG Chun-zhi2, LI Da-wei2()
Received:
2024-05-29
Online:
2024-09-26
Published:
2024-10-12
Contact:
LI Da-wei
E-mail:xiaxuan172@163.com;lidawei@caas.cn
XIA Shi-xuan, GENG Ze-dong, ZHU Guang-tao, ZHANG Chun-zhi, LI Da-wei. Quick Detection of Potato Pollen Viability Based on Deep Learning[J]. Biotechnology Bulletin, 2024, 40(9): 123-130.
数据集 Dataset | 模型 Model | 交并比 IoU | 精确率 Precision | 召回率 Recall | F1分数 F1 score |
---|---|---|---|---|---|
有活力花粉 | SegFormer | 35.25 | 85.95 | 38.26 | 51.11 |
U-Net | 34.34 | 78.62 | 38.28 | 49.92 | |
DeepLabV3 | 31.34 | 78.19 | 35.04 | 47.35 | |
所有花粉 | SegFormer | 75.65 | 82.67 | 88.64 | 84.78 |
U-Net | 58.50 | 74.56 | 72.58 | 73.08 | |
DeepLabV3 | 59.08 | 78.26 | 69.97 | 73.70 |
Table 1 Comparative results of different algorithms
数据集 Dataset | 模型 Model | 交并比 IoU | 精确率 Precision | 召回率 Recall | F1分数 F1 score |
---|---|---|---|---|---|
有活力花粉 | SegFormer | 35.25 | 85.95 | 38.26 | 51.11 |
U-Net | 34.34 | 78.62 | 38.28 | 49.92 | |
DeepLabV3 | 31.34 | 78.19 | 35.04 | 47.35 | |
所有花粉 | SegFormer | 75.65 | 82.67 | 88.64 | 84.78 |
U-Net | 58.50 | 74.56 | 72.58 | 73.08 | |
DeepLabV3 | 59.08 | 78.26 | 69.97 | 73.70 |
Fig. 2 Segmentation results of different-scale pictures The original image is the pollen stained by TTC; the pseudo-color image is the pollen segmentation image output by the model; the mask image is the result of masking the pseudo-color image and the original image for the convenience of observing the segmentation effect; 200 μm(left); 400 μm(right)
Fig. 3 Recognition effect by OpenCV program The original image is the pollen stained by TTC; the mask image is the result of masking the pseudo-color image output by the model with the original image; the recognition image is the output by the OpenCV program after labeling the recognized pollens; bar = 200 μm
编号 No. | 有活力花粉数The number of viable pollens | 总花粉数The number of total pollens | 花粉活力Pollen viability | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
程序计算值Calculated value by program | 人工计算值Manually calculated value | 误差 Error | 程序计算值Calculated value by program | 人工计算值Manually calculated value | 误差 Error | 程序计算值Calculated value by program/% | 人工计算值Manually calculated value/% | 误差 Error/% | ||||
2×-1 | 569 | 535 | -34 | 1 074 | 1 021 | -53 | 53 | 52 | -1 | |||
2×-2 | 241 | 240 | -1 | 442 | 457 | 15 | 55 | 53 | -2 | |||
2×-3 | 297 | 296 | -1 | 390 | 403 | 13 | 76 | 73 | -3 | |||
2×-4 | 447 | 445 | -2 | 528 | 553 | 25 | 84 | 80 | -4 | |||
2×-5 | 355 | 357 | 2 | 483 | 511 | 28 | 73 | 70 | -3 | |||
2×-6 | 158 | 154 | -4 | 315 | 303 | -12 | 50 | 51 | 1 | |||
2×-7 | 99 | 103 | 4 | 138 | 153 | 15 | 72 | 68 | -4 | |||
2×-8 | 167 | 161 | -6 | 235 | 238 | 3 | 71 | 68 | -3 | |||
2×-9 | 142 | 139 | -3 | 177 | 183 | 6 | 80 | 76 | -4 | |||
2×-10 | 314 | 314 | 0 | 553 | 537 | -16 | 57 | 58 | 1 | |||
4×-1 | 117 | 125 | 8 | 249 | 270 | 21 | 47 | 46 | -1 | |||
4×-2 | 118 | 124 | 6 | 244 | 263 | 19 | 48 | 47 | -1 | |||
4×-3 | 80 | 82 | 2 | 174 | 180 | 6 | 46 | 46 | 0 | |||
4×-4 | 71 | 70 | -1 | 187 | 199 | 12 | 38 | 35 | -3 | |||
4×-5 | 72 | 79 | 7 | 240 | 246 | 6 | 30 | 32 | 2 | |||
4×-6 | 73 | 81 | 8 | 245 | 257 | 12 | 30 | 32 | 2 | |||
4×-7 | 70 | 74 | 4 | 204 | 218 | 14 | 34 | 34 | 0 | |||
4×-8 | 75 | 73 | -2 | 188 | 206 | 18 | 40 | 35 | -4 | |||
4×-9 | 69 | 75 | 6 | 232 | 251 | 19 | 30 | 30 | 0 | |||
4×-10 | 56 | 60 | 4 | 186 | 198 | 12 | 30 | 30 | 0 |
Table 2 Comparison of different identification methods
编号 No. | 有活力花粉数The number of viable pollens | 总花粉数The number of total pollens | 花粉活力Pollen viability | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
程序计算值Calculated value by program | 人工计算值Manually calculated value | 误差 Error | 程序计算值Calculated value by program | 人工计算值Manually calculated value | 误差 Error | 程序计算值Calculated value by program/% | 人工计算值Manually calculated value/% | 误差 Error/% | ||||
2×-1 | 569 | 535 | -34 | 1 074 | 1 021 | -53 | 53 | 52 | -1 | |||
2×-2 | 241 | 240 | -1 | 442 | 457 | 15 | 55 | 53 | -2 | |||
2×-3 | 297 | 296 | -1 | 390 | 403 | 13 | 76 | 73 | -3 | |||
2×-4 | 447 | 445 | -2 | 528 | 553 | 25 | 84 | 80 | -4 | |||
2×-5 | 355 | 357 | 2 | 483 | 511 | 28 | 73 | 70 | -3 | |||
2×-6 | 158 | 154 | -4 | 315 | 303 | -12 | 50 | 51 | 1 | |||
2×-7 | 99 | 103 | 4 | 138 | 153 | 15 | 72 | 68 | -4 | |||
2×-8 | 167 | 161 | -6 | 235 | 238 | 3 | 71 | 68 | -3 | |||
2×-9 | 142 | 139 | -3 | 177 | 183 | 6 | 80 | 76 | -4 | |||
2×-10 | 314 | 314 | 0 | 553 | 537 | -16 | 57 | 58 | 1 | |||
4×-1 | 117 | 125 | 8 | 249 | 270 | 21 | 47 | 46 | -1 | |||
4×-2 | 118 | 124 | 6 | 244 | 263 | 19 | 48 | 47 | -1 | |||
4×-3 | 80 | 82 | 2 | 174 | 180 | 6 | 46 | 46 | 0 | |||
4×-4 | 71 | 70 | -1 | 187 | 199 | 12 | 38 | 35 | -3 | |||
4×-5 | 72 | 79 | 7 | 240 | 246 | 6 | 30 | 32 | 2 | |||
4×-6 | 73 | 81 | 8 | 245 | 257 | 12 | 30 | 32 | 2 | |||
4×-7 | 70 | 74 | 4 | 204 | 218 | 14 | 34 | 34 | 0 | |||
4×-8 | 75 | 73 | -2 | 188 | 206 | 18 | 40 | 35 | -4 | |||
4×-9 | 69 | 75 | 6 | 232 | 251 | 19 | 30 | 30 | 0 | |||
4×-10 | 56 | 60 | 4 | 186 | 198 | 12 | 30 | 30 | 0 |
[1] | Hardigan MA, Laimbeer FPE, Newton L, et al. Genome diversity of tuber-bearing Solanum uncovers complex evolutionary history and targets of domestication in the cultivated potato[J]. Proc Natl Acad Sci USA, 2017, 114(46): E9999-E10008. |
[2] |
Camire ME, Kubow S, Donnelly DJ. Potatoes and human health[J]. Crit Rev Food Sci Nutr, 2009, 49(10): 823-840.
doi: 10.1080/10408390903041996 pmid: 19960391 |
[3] | Monro J, Mishra S, Blandford E, et al. Potato genotype differences in nutritionally distinct starch fractions after cooking, and cooking plus storing cool[J]. J Food Compos Anal, 2009, 22(6): 539-545. |
[4] | Aliche EB, Theeuwen TPJM, Oortwijn M, et al. Carbon partitioning mechanisms in potato under drought stress[J]. Plant Physiol Biochem, 2020, 146: 211-219. |
[5] | Simakov EA, Anisimov BV, Yashina IM, et al. Potato breeding and seed production system development in Russia[J]. Potato Res, 2008, 51(3): 313-326. |
[6] | Celis C, Scurrah M, Cowgill S, et al. Environmental biosafety and transgenic potato in a centre of diversity for this crop[J]. Nature, 2004, 432(7014): 222-225. |
[7] |
Gebhardt C, Valkonen JP. Organization of genes controlling disease resistance in the potato genome[J]. Annu Rev Phytopathol, 2001, 39: 79-102.
pmid: 11701860 |
[8] | Lindhout P, Meijer D, Schotte T, et al. Towards F1 hybrid seed potato breeding[J]. Potato Res, 2011, 54(4): 301-312. |
[9] | Boavida LC, Vieira AM, Becker JD, et al. Gametophyte interaction and sexual reproduction: how plants make a zygote[J]. Int J Dev Biol, 2005, 49(5/6): 615-632. |
[10] | Dafni A, Firmage D. Pollen viability and longevity: practical, ecological and evolutionary implications[J]. Plant Syst Evol, 2000, 222(1): 113-132. |
[11] | Chen DJ, Neumann K, Friedel S, et al. Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis[J]. Plant Cell, 2014, 26(12): 4636-4655. |
[12] |
Walter A, Liebisch F, Hund A. Plant phenotyping: from bean weighing to image analysis[J]. Plant Methods, 2015, 11: 14.
doi: 10.1186/s13007-015-0056-8 pmid: 25767559 |
[13] |
Song P, Wang JL, Guo XY, et al. High-throughput phenotyping: breaking through the bottleneck in future crop breeding[J]. Crop J, 2021, 9(3): 633-645.
doi: 10.1016/j.cj.2021.03.015 |
[14] | García-Fortea E, García-Pérez A, Gimeno-Páez E, et al. A deep learning-based system(microscan)for the identification of pollen development stages and its application to obtaining doubled haploid lines in eggplant[J]. Biology, 2020, 9(9): 272. |
[15] | 周成全, 叶宏宝, 俞国红, 等. 基于机器视觉与深度学习的西兰花表型快速提取方法研究[J]. 智慧农业, 2020, 2(1): 121-132. |
Zhou CQ, Ye HB, Yu GH, et al. A fast extraction method of broccoli phenotype based on machine vision and deep learning[J]. Smart Agric, 2020, 2(1): 121-132.
doi: 10.12133/j.smartag.2020.2.1.201912-SA003 |
|
[16] | 赵越, 赵辉, 姜永成, 等. 基于深度学习的马铃薯叶片病害检测方法[J]. 中国农机化学报, 2022, 43(10): 183-189. |
Zhao Y, Zhao H, Jiang YC, et al. Detection method of potato leaf diseases based on deep learning[J]. J Chin Agric Mech, 2022, 43(10): 183-189. | |
[17] | 胡松涛, 翟瑞芳, 王应华, 等. 基于多源数据的马铃薯植株表型参数提取[J]. 智慧农业, 2023, 5(1):132-145. |
Hu ST, Zhai RF, Wang YH, et al. Extraction of potato plant phenotypic parameters based on multi-source data[J]. Smart Agric, 2023, 5(1): 132-145.
doi: 10.12133/j.smartag.SA202302009 |
|
[18] | Gao Y, Li YL, Jiang RB, et al. Enhancing green fraction estimation in rice and wheat crops: a self-supervised deep learning semantic segmentation approach[J]. Plant Phenomics, 2023, 5: 0064. |
[19] |
Tello J, Montemayor MI, Forneck A, et al. A new image-based tool for the high throughput phenotyping of pollen viability: evaluation of inter- and intra-cultivar diversity in grapevine[J]. Plant Methods, 2018, 14: 3.
doi: 10.1186/s13007-017-0267-2 pmid: 29339970 |
[20] | Tan ZH, Yang J, Li QY, et al. PollenDetect: an open-source pollen viability status recognition system based on deep learning neural networks[J]. Int J Mol Sci, 2022, 23(21): 13469. |
[21] | 马艳军, 于佃海, 吴甜, 等. 飞桨:源于产业实践的开源深度学习平台[J]. 数据与计算发展前沿, 2019, 1(5):105-115. |
Ma YJ, Yu DH, Wu T, et al. PaddlePaddle: an open-source deep learning platform from industrial practice[J]. Frontiers of Data and Computing, 2019, 1(5): 105-115. | |
[22] | Hao SJ, Zhou Y, Guo YR. A brief survey on semantic segmentation with deep learning[J]. Neurocomputing, 2020, 406: 302-321. |
[23] |
Zhang CZ, Yang ZM, Tang D, et al. Genome design of hybrid potato[J]. Cell, 2021, 184(15): 3873-3883.e12.
doi: 10.1016/j.cell.2021.06.006 pmid: 34171306 |
[24] | Sun C, Shrivastava A, Singh S, et al. Revisiting unreasonable effectiveness of data in deep learning era[C]// 2017 IEEE International Conference on Computer Vision(ICCV). Venice, Italy. Piscataway, NJ: IEEE, 2017: 843-852. |
[25] | Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning[J]. J Big Data, 2019, 6(1): 60. |
[26] | Su D, Kong H, Qiao YL, et al. Data augmentation for deep learning based semantic segmentation and crop-weed classification in agricultural robotics[J]. Comput Electron Agric, 2021, 190: 106418. |
[27] | Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale[J]. arXiv E Prints, 2020: arXiv: 2010.11929. |
[28] | Xie EZ, Wang WH, Yu ZD, et al. SegFormer: simple and efficient design for semantic segmentation with transformers[EB/OL]. 2021: arXiv: 2105. 15203. http://arxiv.org/abs/2105.15203. |
[29] | Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241. |
[30] | Chen LC, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. 2017: arXiv: 1706.05587. http://arxiv.org/abs/1706.05587. |
[31] |
Yang WN, Feng H, Zhang XH, et al. Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives[J]. Mol Plant, 2020, 13(2): 187-214.
doi: S1674-2052(20)30008-3 pmid: 31981735 |
[32] | 仇瑞承, 魏爽, 张漫, 等. 作物表型组学测量方法综述[J]. 中国农业文摘-农业工程, 2019, 31(1): 23-36, 55. |
Qiu RC, Wei S, Zhang M, et al. Summary of crop phenotypic omics measurement methods[J]. Agric Sci Eng China, 2019, 31(1): 23-36, 55. |
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