Wheat is one of the major crops in the world,with a global demand expected to reach 850 million tons by 2050 that is clearly outpacing current supply.The continual pressure to sustain wheat yield due to the world’s g...Wheat is one of the major crops in the world,with a global demand expected to reach 850 million tons by 2050 that is clearly outpacing current supply.The continual pressure to sustain wheat yield due to the world’s growing population under fluctuating climate conditions requires breeders to increase yield and yield stability across environments.We are working to integrate deep learning into field-based phenotypic analysis to assist breeders in this endeavour.We have utilised wheat images collected by distributed CropQuant phenotyping workstations deployed for multiyear field experiments of UK bread wheat varieties.Based on these image series,we have developed a deep-learning based analysis pipeline to segment spike regions from complicated backgrounds.As a first step towards robust measurement of key yield traits in the field,we present a promising approach that employ Fully Convolutional Network(FCN)to performsemantic segmentation of images to segment wheat spike regions.We also demonstrate the benefits of transfer learning through the use of parameters obtained from other image datasets.We found that the FCN architecture had achieved a Mean classification Accuracy(MA)>82%on validation data and>76%on test data and Mean Intersection over Union value(MIoU)>73%on validation data and and>64%on test datasets.Through this phenomics research,we trust our attempt is likely to form a sound foundation for extracting key yield-related traits such as spikes per unit area and spikelet number per spike,which can be used to assist yield-focused wheat breeding objectives in near future.展开更多
基金Tahani Alkhudaydi was funded by University of Tabuk,scholarship program(37/052/75278)Ji Zhou,Daniel Reynolds,and Simon Griffiths were partially funded by UKRI Biotechnology+4 种基金Biological Sciences Research Council's(BBSRC)Designing Future Wheat Cross-Institute Strategic Programme(BB/P016855/1)to Prof.Graham MooreBBS/E/J/00OPR9781 to Simon GriffithsBBS/E/T/00OPR9785 to Ji ZhouDaniel Reynolds was partially supported by the Core Strategic Programme Grant(BB/CSP17270/l)at the Earlham InstituteBeatriz de la Iglesiawas supported by ES/LO11859/1,from the Business and LocalGovernment Data Research Centre,funded by the Economicand Social Research Council.
文摘Wheat is one of the major crops in the world,with a global demand expected to reach 850 million tons by 2050 that is clearly outpacing current supply.The continual pressure to sustain wheat yield due to the world’s growing population under fluctuating climate conditions requires breeders to increase yield and yield stability across environments.We are working to integrate deep learning into field-based phenotypic analysis to assist breeders in this endeavour.We have utilised wheat images collected by distributed CropQuant phenotyping workstations deployed for multiyear field experiments of UK bread wheat varieties.Based on these image series,we have developed a deep-learning based analysis pipeline to segment spike regions from complicated backgrounds.As a first step towards robust measurement of key yield traits in the field,we present a promising approach that employ Fully Convolutional Network(FCN)to performsemantic segmentation of images to segment wheat spike regions.We also demonstrate the benefits of transfer learning through the use of parameters obtained from other image datasets.We found that the FCN architecture had achieved a Mean classification Accuracy(MA)>82%on validation data and>76%on test data and Mean Intersection over Union value(MIoU)>73%on validation data and and>64%on test datasets.Through this phenomics research,we trust our attempt is likely to form a sound foundation for extracting key yield-related traits such as spikes per unit area and spikelet number per spike,which can be used to assist yield-focused wheat breeding objectives in near future.
