B
Baskar Ganapathysubramanian
Researcher at Iowa State University
Publications - 262
Citations - 6876
Baskar Ganapathysubramanian is an academic researcher from Iowa State University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 34, co-authored 221 publications receiving 4808 citations. Previous affiliations of Baskar Ganapathysubramanian include Cornell University.
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Machine Learning for High-Throughput Stress Phenotyping in Plants
TL;DR: This work provides a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.
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Sparse grid collocation schemes for stochastic natural convection problems
TL;DR: The sparse grid collocation method based on the Smolyak algorithm offers a viable alternate method for solving high-dimensional stochastic partial differential equations and an extension of the collocation approach to include adaptive refinement in important stochastically dimensions is utilized to further reduce the numerical effort necessary for simulation.
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Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives.
TL;DR: A comparative assessment of DL tools against other existing techniques, with respect to decision accuracy, data size requirement, and applicability in various scenarios is provided.
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An explainable deep machine vision framework for plant stress phenotyping.
Sambuddha Ghosal,David Blystone,Asheesh K. Singh,Baskar Ganapathysubramanian,Arti Singh,Soumik Sarkar +5 more
TL;DR: A machine learning framework’s ability to identify and classify a diverse set of foliar stresses in soybean with remarkable accuracy is demonstrated, and the learned model appears to be agnostic to species, seemingly demonstrating an ability of transfer learning.
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Plant disease identification using explainable 3D deep learning on hyperspectral images
Koushik Nagasubramanian,Sarah Jones,Asheesh K. Singh,Soumik Sarkar,Arti Singh,Baskar Ganapathysubramanian +5 more
TL;DR: A novel 3D deep convolutional neural network (DCNN) is deployed that directly assimilates the hyperspectral data and provides physiological insight into model predictions, thus generating confidence in model predictions.