A
Arti Singh
Researcher at Iowa State University
Publications - 92
Citations - 3115
Arti Singh is an academic researcher from Iowa State University. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 20, co-authored 53 publications receiving 1827 citations. Previous affiliations of Arti Singh include Agriculture and Agri-Food Canada.
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Journal ArticleDOI
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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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.
Journal ArticleDOI
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.
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A real-time phenotyping framework using machine learning for plant stress severity rating in soybean.
Hsiang Sing Naik,Jiaoping Zhang,Alec Lofquist,Teshale Assefa,Soumik Sarkar,David M. Ackerman,Arti Singh,Asheesh K. Singh,Baskar Ganapathysubramanian +8 more
TL;DR: This work constructs a phenotypically meaningful ‘population canopy graph’, connecting the automatically extracted canopy trait features with plant stress severity rating, and incorporated this image capture →-image processing → classification workflow into a smartphone app that enables automated real-time evaluation of IDC scores using digital images of the canopy.