Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops.The objective of this study is to develop a machine learning(ML)approach adept ...Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops.The objective of this study is to develop a machine learning(ML)approach adept at soybean(Glycine max L.(Merr.))pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot.To meet this goal,we developed a multiview image-based yield estimation framework utilizing deep learning architectures.Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions.We used data from controlled imaging environment in field,as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation.Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars.展开更多
The microstructure determines the photovoltaic performance of a thin film organic semiconductor film.The relationship between microstructure and performance is usually highly non-linear and expensive to evaluate,thus ...The microstructure determines the photovoltaic performance of a thin film organic semiconductor film.The relationship between microstructure and performance is usually highly non-linear and expensive to evaluate,thus making microstructure optimization challenging.Here,we show a data-driven approach for mapping the microstructure to photovoltaic performance using deep convolutional neural networks.We characterize this approach in terms of two critical metrics,its generalizability(has it learnt a reasonable map?),and its intepretability(can it produce meaningful microstructure characteristics that influence its prediction?).A surrogate model that exhibits these two features of generalizability and intepretability is particularly useful for subsequent design exploration.We illustrate this by using the surrogate model for both manual exploration(that verifies known domain insight)as well as automated microstructure optimization.We envision such approaches to be widely applicable to a wide variety of microstructure-sensitive design problems.展开更多
基金the funding support from the Iowa Soybean Association(A.K.S.)USDA-NIFA Grants#2017-67007-26151(S.S.,A.K.S.,B.G.,A.S.),2017-67021-25965(S.B.,S.S.,B.G.,A.S.,A.K.S.),and 2019-67021-29938(A.S.,B.G.,S.S,A.K.S)+5 种基金NSF S&CC Grant#1952045NSF Grant#CNS-1954556Raymond F.Baker Center for Plant Breeding(A.K.S.)Bayer Chair in Soybean Breeding(A.K.S.)Plant Sciences Institute(S.S.,A.K.S.,B.G.)and USDA CRIS Project IOW04714(A.K.S.,A.S).M.E.C.was partly supported by a graduate assistantship through NSF NRT Predictive Plant Phenomics Project.
文摘Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops.The objective of this study is to develop a machine learning(ML)approach adept at soybean(Glycine max L.(Merr.))pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot.To meet this goal,we developed a multiview image-based yield estimation framework utilizing deep learning architectures.Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions.We used data from controlled imaging environment in field,as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation.Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars.
基金S.G.,A.K.,and S.S.were funded by AFOSR YIP FA9550-17-1-0220 and DARPA HR00111990031B.S.S.P.and B.G.were funded by NSF DMREF 1435587 and DARPA HR00111990031.
文摘The microstructure determines the photovoltaic performance of a thin film organic semiconductor film.The relationship between microstructure and performance is usually highly non-linear and expensive to evaluate,thus making microstructure optimization challenging.Here,we show a data-driven approach for mapping the microstructure to photovoltaic performance using deep convolutional neural networks.We characterize this approach in terms of two critical metrics,its generalizability(has it learnt a reasonable map?),and its intepretability(can it produce meaningful microstructure characteristics that influence its prediction?).A surrogate model that exhibits these two features of generalizability and intepretability is particularly useful for subsequent design exploration.We illustrate this by using the surrogate model for both manual exploration(that verifies known domain insight)as well as automated microstructure optimization.We envision such approaches to be widely applicable to a wide variety of microstructure-sensitive design problems.
