The biotrophic fungus Puccinia striiformis f. sp. tritici is the causal agent of the yellow rust in wheat. Between the years 2010–2013 a new strain of this pathogen(Warrior/Ambition),against which the present cultiva...The biotrophic fungus Puccinia striiformis f. sp. tritici is the causal agent of the yellow rust in wheat. Between the years 2010–2013 a new strain of this pathogen(Warrior/Ambition),against which the present cultivated wheat varieties have no resistance, appeared and spread rapidly. It threatens cereal production in most of Europe. The search for sources of resistance to this strain is proposed as the most efficient and safe solution to ensure high grain production. This will be helped by the development of high performance and low cost techniques for field phenotyping. In this study we analyzed vegetation indices in the Red,Green, Blue(RGB) images of crop canopies under field conditions. We evaluated their accuracy in predicting grain yield and assessing disease severity in comparison to other field measurements including the Normalized Difference Vegetation Index(NDVI), leaf chlorophyll content, stomatal conductance, and canopy temperature. We also discuss yield components and agronomic parameters in relation to grain yield and disease severity.RGB-based indices proved to be accurate predictors of grain yield and grain yield losses associated with yellow rust(R2= 0.581 and R2= 0.536, respectively), far surpassing the predictive ability of NDVI(R2= 0.118 and R2= 0.128, respectively). In comparison to potential yield, we found the presence of disease to be correlated with reductions in the number of grains per spike, grains per square meter, kernel weight and harvest index. Grain yield losses in the presence of yellow rust were also greater in later heading varieties. The combination of RGB-based indices and days to heading together explained 70.9% of the variability in grain yield and 62.7% of the yield losses.展开更多
An open-source software for field-based plant phenotyping,Precision Plots Analyzer(PREPs),was developed using Window.NET.The software runs on 64-bit Windows computers.This software allows the extraction of phenotypic ...An open-source software for field-based plant phenotyping,Precision Plots Analyzer(PREPs),was developed using Window.NET.The software runs on 64-bit Windows computers.This software allows the extraction of phenotypic traits on a per-microplot basis from orthomosaic and digital surface model(DSM)images generated by Structure-from-Motion/Multi-View-Stereo(SfM-MVS)tools.Moreover,there is no need to acquire skills in geographical information system(GIS)or programming languages for image analysis.Three use cases illustrated the software's functionality.The first involved monitoring the growth of sugar beet varieties in an experimental field using an unmanned aerial vehicle(UAV),where differences among varieties were detected through estimates of crop height,coverage,and volume index.Second,mixed varieties of potato crops were estimated using a UAV and varietal differences were observed from the estimated phenotypic traits.A strong correlation was observed between the manually measured crop height and UAV-estimated crop height.Finally,using a multicamera array attached to a tractor,the height,coverage,and volume index of the 3 potato varieties were precisely estimated.PREPs software is poised to be a useful tool that allows anyone without prior knowledge of programming to extract crop traits for phenotyping.展开更多
High-throughput phenotyping is the bottleneck for advancing field trait characterization and yield improvement in major field crops.Specifically for sorghum(Sorghum bicolor L.),rapid plant-level yield estimation is hi...High-throughput phenotyping is the bottleneck for advancing field trait characterization and yield improvement in major field crops.Specifically for sorghum(Sorghum bicolor L.),rapid plant-level yield estimation is highly dependent on characterizing the number of grains within a panicle.In this context,the integration of computer vision and artificial intelligence algorithms with traditional field phenotyping can be a critical solution to reduce labor costs and time.Therefore,this study aims to improve sorghum panicle detection and grain number estimation from smartphone-capture images under field conditions.A preharvest benchmark dataset was collected at field scale(2023 season,Kansas,USA),with 648 images of sorghum panicles retrieved via smartphone device,and grain number counted.Each sorghum panicle image was manually labeled,and the images were augmented.Two models were trained using the Detectron2 and Yolov8 frameworks for detection and segmentation,with an average precision of 75%and 89%,respectively.For the grain number,3 models were trained:MCNN(multiscale convolutional neural network),TCNN-Seed(two-column CNN-Seed),and Sorghum-Net(developed in this study).The Sorghum-Net model showed a mean absolute percentage error of 17%,surpassing the other models.Lastly,a simple equation was presented to relate the count from the model(using images from only one side of the panicle)to the field-derived observed number of grains per sorghum panicle.The resulting framework obtained an estimation of grain number with a 17%error.The proposed framework lays the foundation for the development of a more robust application to estimate sorghum yield using images from a smartphone at the plant level.展开更多
文摘The biotrophic fungus Puccinia striiformis f. sp. tritici is the causal agent of the yellow rust in wheat. Between the years 2010–2013 a new strain of this pathogen(Warrior/Ambition),against which the present cultivated wheat varieties have no resistance, appeared and spread rapidly. It threatens cereal production in most of Europe. The search for sources of resistance to this strain is proposed as the most efficient and safe solution to ensure high grain production. This will be helped by the development of high performance and low cost techniques for field phenotyping. In this study we analyzed vegetation indices in the Red,Green, Blue(RGB) images of crop canopies under field conditions. We evaluated their accuracy in predicting grain yield and assessing disease severity in comparison to other field measurements including the Normalized Difference Vegetation Index(NDVI), leaf chlorophyll content, stomatal conductance, and canopy temperature. We also discuss yield components and agronomic parameters in relation to grain yield and disease severity.RGB-based indices proved to be accurate predictors of grain yield and grain yield losses associated with yellow rust(R2= 0.581 and R2= 0.536, respectively), far surpassing the predictive ability of NDVI(R2= 0.118 and R2= 0.128, respectively). In comparison to potential yield, we found the presence of disease to be correlated with reductions in the number of grains per spike, grains per square meter, kernel weight and harvest index. Grain yield losses in the presence of yellow rust were also greater in later heading varieties. The combination of RGB-based indices and days to heading together explained 70.9% of the variability in grain yield and 62.7% of the yield losses.
基金partially supported by CREST(JPMJCR1512)AIP Acceleration Research(JPMJCR21U3)of JST.
文摘An open-source software for field-based plant phenotyping,Precision Plots Analyzer(PREPs),was developed using Window.NET.The software runs on 64-bit Windows computers.This software allows the extraction of phenotypic traits on a per-microplot basis from orthomosaic and digital surface model(DSM)images generated by Structure-from-Motion/Multi-View-Stereo(SfM-MVS)tools.Moreover,there is no need to acquire skills in geographical information system(GIS)or programming languages for image analysis.Three use cases illustrated the software's functionality.The first involved monitoring the growth of sugar beet varieties in an experimental field using an unmanned aerial vehicle(UAV),where differences among varieties were detected through estimates of crop height,coverage,and volume index.Second,mixed varieties of potato crops were estimated using a UAV and varietal differences were observed from the estimated phenotypic traits.A strong correlation was observed between the manually measured crop height and UAV-estimated crop height.Finally,using a multicamera array attached to a tractor,the height,coverage,and volume index of the 3 potato varieties were precisely estimated.PREPs software is poised to be a useful tool that allows anyone without prior knowledge of programming to extract crop traits for phenotyping.
文摘High-throughput phenotyping is the bottleneck for advancing field trait characterization and yield improvement in major field crops.Specifically for sorghum(Sorghum bicolor L.),rapid plant-level yield estimation is highly dependent on characterizing the number of grains within a panicle.In this context,the integration of computer vision and artificial intelligence algorithms with traditional field phenotyping can be a critical solution to reduce labor costs and time.Therefore,this study aims to improve sorghum panicle detection and grain number estimation from smartphone-capture images under field conditions.A preharvest benchmark dataset was collected at field scale(2023 season,Kansas,USA),with 648 images of sorghum panicles retrieved via smartphone device,and grain number counted.Each sorghum panicle image was manually labeled,and the images were augmented.Two models were trained using the Detectron2 and Yolov8 frameworks for detection and segmentation,with an average precision of 75%and 89%,respectively.For the grain number,3 models were trained:MCNN(multiscale convolutional neural network),TCNN-Seed(two-column CNN-Seed),and Sorghum-Net(developed in this study).The Sorghum-Net model showed a mean absolute percentage error of 17%,surpassing the other models.Lastly,a simple equation was presented to relate the count from the model(using images from only one side of the panicle)to the field-derived observed number of grains per sorghum panicle.The resulting framework obtained an estimation of grain number with a 17%error.The proposed framework lays the foundation for the development of a more robust application to estimate sorghum yield using images from a smartphone at the plant level.
