activity
20162020
most citedUsefulness of interpretability methods to explain deep learning based plant stress phenotyping

6 citations · 9 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2020

Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications

Luis G Riera, Matthew E. Carroll, Zhisheng Zhang +8

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…

cs.CV20206 cited

Usefulness of interpretability methods to explain deep learning based plant stress phenotyping

Koushik Nagasubramanian, Asheesh K. Singh, Arti Singh +2

Deep learning techniques have been successfully deployed for automating plant stress identification and quantification. In recent years, there is a growing push towards training mo…

cs.CV2020

How useful is Active Learning for Image-based Plant Phenotyping?

Koushik Nagasubramanian, Talukder Z. Jubery, Fateme Fotouhi Ardakani +5

Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, succ…

cs.CV2018

Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps

Koushik Nagasubramanian, Sarah Jones, Asheesh K. Singh +3

Our overarching goal is to develop an accurate and explainable model for plant disease identification using hyperspectral data. Charcoal rot is a soil borne fungal disease that aff…

stat.ML20171 cited

Interpretable Deep Learning applied to Plant Stress Phenotyping

Sambuddha Ghosal, David Blystone, Asheesh K. Singh +3

Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and…

cs.CV20172 cited

Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean

Koushik Nagasubramanian, Sarah Jones, Soumik Sarkar +3

Charcoal rot is a fungal disease that thrives in warm dry conditions and affects the yield of soybeans and other important agronomic crops worldwide. There is a need for robust, au…