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Interpretable deep learning for guided microstructure-property explorations in photovoltaics

npj Computational Materials 2019年第1期5卷 302-312页

作者： Balaji Sesha Sarath Pokuri sambuddha ghosal Apurva Kokate Soumik Sarkar Baskar Ganapathysubramanian Department of Mechanical Engineering Iowa State UniversityAmesIAUSA Department of Computer Science Iowa State UniversityAmesIAUSA

The microstructure determines the photovoltaic performance of a thin film organic semiconductor *** relationship between microstructure and performance is usually highly non-linear and expensive to evaluate,thus making microstructure optimization ***,we show a data-driven approach for mapping the microstructure to photovoltaic performance using deep convolutional neural *** 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 *** illustrate this by using the surrogate model for both manual exploration(that verifies known domain insight)as well as automated microstructure *** envision such approaches to be widely applicable to a wide variety of microstructure-sensitive design problems.

关键词： microstructure property meaningful

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Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications

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Plant Phenomics 2021年第1期3卷 286-297页

作者： Luis GRiera Matthew ECarroll Zhisheng Zhang Johnathon MShook sambuddha ghosal Tianshuang Gao Arti Singh Sourabh Bhattacharya Baskar Ganapathysubramanian Asheesh K.Singh Soumik Sarkar Department of Mechanical Engineering Iowa State UniversityAmesIowaUSA Department of Agronomy Iowa State UniversityAmesIowaUSA Department of Computer Science Iowa State UniversityAmesIowaUSA

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row *** 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 *** meet this goal,we developed a multiview image-based yield estimation framework utilizing deep learning *** images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding *** 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 *** 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.

关键词： breeding soybean cultivar

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