Binglin Zhu, Fusang Liu, Yingpu Che, Fang Hui, Yuntao Ma
{"title":"利用地面和航空摄影技术对大田间作作物进行三维定量化","authors":"Binglin Zhu, Fusang Liu, Yingpu Che, Fang Hui, Yuntao Ma","doi":"10.1109/pma.2018.8747359","DOIUrl":null,"url":null,"abstract":"High-throughput phenotyping of plant threedimensional (3D) architecture is critical for determining plant phenotypic characteristics. The acquisition of 3D architecture of plant phenotypic traits based on multi-view photographing has been widely applied in greenhouse research. Growth process of the plants can be dynamically monitored. However, the application of this method in the field is more difficult and less due to the complex environment. In this study, maize/soybean intercropping plant populations in the field were selected as the research objects. We combined ground and aerial photography to obtain the image sequences. at the stage of seedling, jointing, tasseling and grain filling. The targeted plants were photographed with fixed point from multi-view hemispherical directions on ground photography before tasseling stage. Then, Unmanned Aerial Vehicle was used to take photos in the way of concentric circles with different radius. We preprocessed the image sequences by Support Vector Machine (SVM) method, and pixel information only containing targeted plants were achieved. We evaluated the accuracy of calculated individual height, blade length and maximum width with the measured data. Image sensitivity analysis was also done at 25 and 79 days after emergence by reducing the image numbers. Canopy coverage and plant height were compared between different scenarios. The results showed that there was a good agreement between measured and calculated plant height, blade length and blade maximum width with R2>0.90. Then the dynamic changes of plant height, crown surface and organ growth were extracted based on reconstructed 3D architecture. Sensitivity analysis showed that at the early growth stage, 50 images are enough for 3D reconstruction of the plant. However, all 300 images need to be included at the late growth stage of plants. The results can provide a basis for high-throughput phenotypic analysis related to genotypes and help to evaluate the plant architecture and canopy radiation interception.","PeriodicalId":268842,"journal":{"name":"2018 6th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Three-dimensional quantification of intercropping crops in field by ground and aerial photography\",\"authors\":\"Binglin Zhu, Fusang Liu, Yingpu Che, Fang Hui, Yuntao Ma\",\"doi\":\"10.1109/pma.2018.8747359\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"High-throughput phenotyping of plant threedimensional (3D) architecture is critical for determining plant phenotypic characteristics. The acquisition of 3D architecture of plant phenotypic traits based on multi-view photographing has been widely applied in greenhouse research. Growth process of the plants can be dynamically monitored. However, the application of this method in the field is more difficult and less due to the complex environment. In this study, maize/soybean intercropping plant populations in the field were selected as the research objects. We combined ground and aerial photography to obtain the image sequences. at the stage of seedling, jointing, tasseling and grain filling. The targeted plants were photographed with fixed point from multi-view hemispherical directions on ground photography before tasseling stage. Then, Unmanned Aerial Vehicle was used to take photos in the way of concentric circles with different radius. We preprocessed the image sequences by Support Vector Machine (SVM) method, and pixel information only containing targeted plants were achieved. We evaluated the accuracy of calculated individual height, blade length and maximum width with the measured data. Image sensitivity analysis was also done at 25 and 79 days after emergence by reducing the image numbers. Canopy coverage and plant height were compared between different scenarios. The results showed that there was a good agreement between measured and calculated plant height, blade length and blade maximum width with R2>0.90. Then the dynamic changes of plant height, crown surface and organ growth were extracted based on reconstructed 3D architecture. Sensitivity analysis showed that at the early growth stage, 50 images are enough for 3D reconstruction of the plant. However, all 300 images need to be included at the late growth stage of plants. The results can provide a basis for high-throughput phenotypic analysis related to genotypes and help to evaluate the plant architecture and canopy radiation interception.\",\"PeriodicalId\":268842,\"journal\":{\"name\":\"2018 6th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 6th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/pma.2018.8747359\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 6th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/pma.2018.8747359","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Three-dimensional quantification of intercropping crops in field by ground and aerial photography
High-throughput phenotyping of plant threedimensional (3D) architecture is critical for determining plant phenotypic characteristics. The acquisition of 3D architecture of plant phenotypic traits based on multi-view photographing has been widely applied in greenhouse research. Growth process of the plants can be dynamically monitored. However, the application of this method in the field is more difficult and less due to the complex environment. In this study, maize/soybean intercropping plant populations in the field were selected as the research objects. We combined ground and aerial photography to obtain the image sequences. at the stage of seedling, jointing, tasseling and grain filling. The targeted plants were photographed with fixed point from multi-view hemispherical directions on ground photography before tasseling stage. Then, Unmanned Aerial Vehicle was used to take photos in the way of concentric circles with different radius. We preprocessed the image sequences by Support Vector Machine (SVM) method, and pixel information only containing targeted plants were achieved. We evaluated the accuracy of calculated individual height, blade length and maximum width with the measured data. Image sensitivity analysis was also done at 25 and 79 days after emergence by reducing the image numbers. Canopy coverage and plant height were compared between different scenarios. The results showed that there was a good agreement between measured and calculated plant height, blade length and blade maximum width with R2>0.90. Then the dynamic changes of plant height, crown surface and organ growth were extracted based on reconstructed 3D architecture. Sensitivity analysis showed that at the early growth stage, 50 images are enough for 3D reconstruction of the plant. However, all 300 images need to be included at the late growth stage of plants. The results can provide a basis for high-throughput phenotypic analysis related to genotypes and help to evaluate the plant architecture and canopy radiation interception.