During the last decade,there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant.High ...During the last decade,there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant.High throughput phenotyping(HTP)involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing.Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain.More specifically,several root phenotyping tools are discussed to study the plant’s hidden half and an area long neglected.However,the use of these HTP technologies produces big data sets that impede the inference from those datasets.Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions.These are interdisciplinary approaches for data analysis using probability,statistics,classification,regression,decision theory,data visualization,and neural networks to relate information extracted with the phenotypes obtained.These techniques use feature extraction,identification,classification,and prediction criteria to identify pertinent data for use in plant breeding and pathology activities.This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms.We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls.Overall,this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions.Finally,we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.展开更多
High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine ...High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine max)using unmanned aerial vehicle(UAV)remote sensing and deep learning models.In 2018,a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions:drought and control.We used a convolutional neural network(CNN)as a model to estimate the phenotypic values of 5 conventional biomass-related traits:dry weight,main stem length,numbers of nodes and branches,and plant height.We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models.The accuracy of the developed models was assessed through 10-fold cross-validation,which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously.Deep learning enabled us to extract features that exhibited strong correlations with the output(i.e.,phenotypes of the target traits)and accurately estimate the values of the features from the input data.We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits.Furthermore,we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions.The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.展开更多
In addition to the negative consequences of climate change,sucking pest complexes severely limited cotton yields in the recent past.Although the damage caused by bollworms was much reduced by utilizing Bt cotton,the e...In addition to the negative consequences of climate change,sucking pest complexes severely limited cotton yields in the recent past.Although the damage caused by bollworms was much reduced by utilizing Bt cotton,the emergence of sucking pests(such as aphids,thrips,and whiteflies)poses a serious threat to cotton production,as they reduce lint yield by 40%–60%finally.Additionally,these pests also caused yield losses by spreading viral diseases.Promoting innovative and thorough control methods is necessary to counter the threat posed by these sucking pests.Such initiatives necessitate a multifaceted strategy that combines next-generation breeding technology and pest management techniques to produce novel cotton cultivars that are resistant to sucking pests.The discovery of novel genes and regulatory factors linked to cotton’s resistance to sucking pests will be possible by the combination of next-generation breeding technologies and omics approaches and employing those tools on special resistant donors.Continuous research aimed at understanding the genetic basis of insect resistance and improving integrated pest management(IPM)techniques is crucial to the sustainability and resilience of cotton cropping systems.To this end,a sustainable and viable strategy to protect cotton fields from sucking pests is outlined.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
Chlorophyll and anthocyanin contents provide a valuable indicator of the status of a plant’s physiology, but to be more widely utilized it needs to be assessed easily and non‐destructively. This is particularly evid...Chlorophyll and anthocyanin contents provide a valuable indicator of the status of a plant’s physiology, but to be more widely utilized it needs to be assessed easily and non‐destructively. This is particularly evident in terms of assessing and exploiting germplasm for plant‐breeding programs. We report, for the first time, experiments with Fragaria chiloensis（L.）Duch. and the estimation of the effects of response to salinity stress（0, 30, and 60 mmol NaCl/L） in terms of these pigments content and gas exchange. It is shown that both pigments（which interestingly, themselves show a high correlation） give a good indication of stress response. Both pigments can be accurately predicted using spectral reflectance indices（SRI）;however, the accuracy of the predictions was slightly improved using multilinear regression analysis models and genetic algorithm analysis. Specifically for chlorophyll content, unlike other species, the use of published SRI gave better indications ofstress response than Normalized Difference Vegetation Index.The effect of salt on gas exchange is only evident at the highest concentration and some SRI gave better prediction performance than the known Photochemical Reflectance Index. This information will therefore be useful for identifying tolerant genotypes to salt stress for incorporation in breeding programs.展开更多
文摘During the last decade,there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant.High throughput phenotyping(HTP)involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing.Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain.More specifically,several root phenotyping tools are discussed to study the plant’s hidden half and an area long neglected.However,the use of these HTP technologies produces big data sets that impede the inference from those datasets.Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions.These are interdisciplinary approaches for data analysis using probability,statistics,classification,regression,decision theory,data visualization,and neural networks to relate information extracted with the phenotypes obtained.These techniques use feature extraction,identification,classification,and prediction criteria to identify pertinent data for use in plant breeding and pathology activities.This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms.We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls.Overall,this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions.Finally,we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
基金supported by the JST CREST[grant number:JPMJCR16O2]and MEXT KAKENHI[grant number:JP22H02306].The funders had no role in the study design,data collection and analysis,decision to publish,or manuscript preparation.
文摘High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine max)using unmanned aerial vehicle(UAV)remote sensing and deep learning models.In 2018,a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions:drought and control.We used a convolutional neural network(CNN)as a model to estimate the phenotypic values of 5 conventional biomass-related traits:dry weight,main stem length,numbers of nodes and branches,and plant height.We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models.The accuracy of the developed models was assessed through 10-fold cross-validation,which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously.Deep learning enabled us to extract features that exhibited strong correlations with the output(i.e.,phenotypes of the target traits)and accurately estimate the values of the features from the input data.We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits.Furthermore,we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions.The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.
基金M/s.RASI Seeds Pvt.Ltd.,Attur,Tamil Nadu,India for their generous financial assistance in setting up a MAS study in cotton for genetic improvement of sucking pest resistance.
文摘In addition to the negative consequences of climate change,sucking pest complexes severely limited cotton yields in the recent past.Although the damage caused by bollworms was much reduced by utilizing Bt cotton,the emergence of sucking pests(such as aphids,thrips,and whiteflies)poses a serious threat to cotton production,as they reduce lint yield by 40%–60%finally.Additionally,these pests also caused yield losses by spreading viral diseases.Promoting innovative and thorough control methods is necessary to counter the threat posed by these sucking pests.Such initiatives necessitate a multifaceted strategy that combines next-generation breeding technology and pest management techniques to produce novel cotton cultivars that are resistant to sucking pests.The discovery of novel genes and regulatory factors linked to cotton’s resistance to sucking pests will be possible by the combination of next-generation breeding technologies and omics approaches and employing those tools on special resistant donors.Continuous research aimed at understanding the genetic basis of insect resistance and improving integrated pest management(IPM)techniques is crucial to the sustainability and resilience of cotton cropping systems.To this end,a sustainable and viable strategy to protect cotton fields from sucking pests is outlined.
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.
基金supported by the equipment grant(FONDEQUIP‐IQM 130073)from CONICYT‐Chilethe Doctoral research grantthe research program"Adaptation of Agriculture to Climate Change(A2C2),"both from Universidad de Talca‐Chile
文摘Chlorophyll and anthocyanin contents provide a valuable indicator of the status of a plant’s physiology, but to be more widely utilized it needs to be assessed easily and non‐destructively. This is particularly evident in terms of assessing and exploiting germplasm for plant‐breeding programs. We report, for the first time, experiments with Fragaria chiloensis（L.）Duch. and the estimation of the effects of response to salinity stress（0, 30, and 60 mmol NaCl/L） in terms of these pigments content and gas exchange. It is shown that both pigments（which interestingly, themselves show a high correlation） give a good indication of stress response. Both pigments can be accurately predicted using spectral reflectance indices（SRI）;however, the accuracy of the predictions was slightly improved using multilinear regression analysis models and genetic algorithm analysis. Specifically for chlorophyll content, unlike other species, the use of published SRI gave better indications ofstress response than Normalized Difference Vegetation Index.The effect of salt on gas exchange is only evident at the highest concentration and some SRI gave better prediction performance than the known Photochemical Reflectance Index. This information will therefore be useful for identifying tolerant genotypes to salt stress for incorporation in breeding programs.
