Akira Yamazaki, Ao Takezawa, Kyoka Nagasaka, Ko Motoki, Kazusa Nishimura, Ryohei Nakano, Tetsuya Nakazaki
{"title":"一种使用机器学习测量花粉发芽率的简单方法。","authors":"Akira Yamazaki, Ao Takezawa, Kyoka Nagasaka, Ko Motoki, Kazusa Nishimura, Ryohei Nakano, Tetsuya Nakazaki","doi":"10.1007/s00497-023-00472-9","DOIUrl":null,"url":null,"abstract":"<p><p>The pollen germination rate decreases under various abiotic stresses, such as high-temperature stress, and it is one of the causes of inhibition of plant reproduction. Thus, measuring pollen germination rate is vital for understanding the reproductive ability of plants. However, measuring the pollen germination rate requires much labor when counting pollen. Therefore, we used the Yolov5 machine learning package in order to perform transfer learning and constructed a model that can detect germinated and non-germinated pollen separately. Pollen images of the chili pepper, Capsicum annuum, were used to create this model. Using images with a width of 640 pixels for training constructed a more accurate model than using images with a width of 320 pixels. This model could estimate the pollen germination rate of the F<sub>2</sub> population of C. chinense previously studied with high accuracy. In addition, significantly associated gene regions previously detected in genome-wide association studies in this F<sub>2</sub> population could again be detected using the pollen germination rate predicted by this model as a trait. Moreover, the model detected rose, tomato, radish, and strawberry pollen grains with similar accuracy to chili pepper. The pollen germination rate could be estimated even for plants other than chili pepper, probably because pollen images were similar among different plant species. We obtained a model that can identify genes related to pollen germination rate through genetic analyses in many plants.</p>","PeriodicalId":51297,"journal":{"name":"Plant Reproduction","volume":" ","pages":"355-364"},"PeriodicalIF":2.9000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A simple method for measuring pollen germination rate using machine learning.\",\"authors\":\"Akira Yamazaki, Ao Takezawa, Kyoka Nagasaka, Ko Motoki, Kazusa Nishimura, Ryohei Nakano, Tetsuya Nakazaki\",\"doi\":\"10.1007/s00497-023-00472-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The pollen germination rate decreases under various abiotic stresses, such as high-temperature stress, and it is one of the causes of inhibition of plant reproduction. Thus, measuring pollen germination rate is vital for understanding the reproductive ability of plants. However, measuring the pollen germination rate requires much labor when counting pollen. Therefore, we used the Yolov5 machine learning package in order to perform transfer learning and constructed a model that can detect germinated and non-germinated pollen separately. Pollen images of the chili pepper, Capsicum annuum, were used to create this model. Using images with a width of 640 pixels for training constructed a more accurate model than using images with a width of 320 pixels. This model could estimate the pollen germination rate of the F<sub>2</sub> population of C. chinense previously studied with high accuracy. In addition, significantly associated gene regions previously detected in genome-wide association studies in this F<sub>2</sub> population could again be detected using the pollen germination rate predicted by this model as a trait. Moreover, the model detected rose, tomato, radish, and strawberry pollen grains with similar accuracy to chili pepper. The pollen germination rate could be estimated even for plants other than chili pepper, probably because pollen images were similar among different plant species. We obtained a model that can identify genes related to pollen germination rate through genetic analyses in many plants.</p>\",\"PeriodicalId\":51297,\"journal\":{\"name\":\"Plant Reproduction\",\"volume\":\" \",\"pages\":\"355-364\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2023-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Plant Reproduction\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1007/s00497-023-00472-9\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/6/6 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"PLANT SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plant Reproduction","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1007/s00497-023-00472-9","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/6/6 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
A simple method for measuring pollen germination rate using machine learning.
The pollen germination rate decreases under various abiotic stresses, such as high-temperature stress, and it is one of the causes of inhibition of plant reproduction. Thus, measuring pollen germination rate is vital for understanding the reproductive ability of plants. However, measuring the pollen germination rate requires much labor when counting pollen. Therefore, we used the Yolov5 machine learning package in order to perform transfer learning and constructed a model that can detect germinated and non-germinated pollen separately. Pollen images of the chili pepper, Capsicum annuum, were used to create this model. Using images with a width of 640 pixels for training constructed a more accurate model than using images with a width of 320 pixels. This model could estimate the pollen germination rate of the F2 population of C. chinense previously studied with high accuracy. In addition, significantly associated gene regions previously detected in genome-wide association studies in this F2 population could again be detected using the pollen germination rate predicted by this model as a trait. Moreover, the model detected rose, tomato, radish, and strawberry pollen grains with similar accuracy to chili pepper. The pollen germination rate could be estimated even for plants other than chili pepper, probably because pollen images were similar among different plant species. We obtained a model that can identify genes related to pollen germination rate through genetic analyses in many plants.
期刊介绍:
Plant Reproduction (formerly known as Sexual Plant Reproduction) is a journal devoted to publishing high-quality research in the field of reproductive processes in plants. Article formats include original research papers, expert reviews, methods reports and opinion papers. Articles are selected based on significance for the field of plant reproduction, spanning from the induction of flowering to fruit development. Topics incl … show all