User profiles for B. Ganapathysubramanian
Baskar GanapathysubramanianNSF/USDA AI Institute, Iowa State University Verified email at iastate.edu Cited by 9658 |
[HTML][HTML] Machine learning for high-throughput stress phenotyping in plants
Advances in automated and high-throughput imaging technologies have resulted in a deluge
of high-resolution images and sensor data of plants. However, extracting patterns and …
of high-resolution images and sensor data of plants. However, extracting patterns and …
[HTML][HTML] Deep learning for plant stress phenotyping: trends and future perspectives
… Stage B: this feature set is then used in subsequent classification/regression analysis. In
contrast to this two-stage approach, a DL pipeline is typically fed the raw image data and, during …
contrast to this two-stage approach, a DL pipeline is typically fed the raw image data and, during …
[HTML][HTML] Challenges and opportunities in machine-augmented plant stress phenotyping
Plant stress phenotyping is essential to select stress-resistant varieties and develop better
stress-management strategies. Standardization of visual assessments and deployment of …
stress-management strategies. Standardization of visual assessments and deployment of …
Sparse grid collocation schemes for stochastic natural convection problems
B Ganapathysubramanian, N Zabaras - Journal of Computational Physics, 2007 - Elsevier
… -dimensional basis Φ i :(12) B ∑ i = 0 P u i Φ i , ∑ i = 0 P p i Φ i , ∑ i = 0 P θ i Φ i : x , t , ξ ,
Φ j = 0 , where < a , b > is the inner product of the functions a and b over the ensemble ( < a , …
Φ j = 0 , where < a , b > is the inner product of the functions a and b over the ensemble ( < a , …
An explainable deep machine vision framework for plant stress phenotyping
…, AK Singh, B Ganapathysubramanian… - Proceedings of the …, 2018 - National Acad Sciences
… The first convolutional layer maps the three channels ( R G B ) in the input image to 128
feature maps by using a 3 × 3 kernel function. Subsequent max-pooling decreases the …
feature maps by using a 3 × 3 kernel function. Subsequent max-pooling decreases the …
Nanoscale control of internal inhomogeneity enhances water transport in desalination membranes
…, SD Jons, A Roy, M Paul, B Ganapathysubramanian… - Science, 2021 - science.org
… (B and C) Selected areas from a zero-loss (B) and composite thickness map image (C) of
the PA4 membrane. Scale bars, 500 nm. (D and E) Water permeance as a function of average …
the PA4 membrane. Scale bars, 500 nm. (D and E) Water permeance as a function of average …
[HTML][HTML] Plant disease identification using explainable 3D deep learning on hyperspectral images
… 2b shows the RGB image of the disease progression comparison between interior and …
b RGB image of the disease progression comparison between interior and exterior region of …
b RGB image of the disease progression comparison between interior and exterior region of …
[HTML][HTML] A real-time phenotyping framework using machine learning for plant stress severity rating in soybean
… sampling of the Y and B% range. This information is used to construct a 2D plot that depicts
decision boundaries that separate various IDC classes (as a function of Y and B%), which …
decision boundaries that separate various IDC classes (as a function of Y and B%), which …
[HTML][HTML] Engineering fluid flow using sequenced microstructures
… (b) The four modes of operation achieved experimentally are shown with confocal cross-sections
of the asymmetric quadrant of the flow (same scale bar of 20 μm is used for all four …
of the asymmetric quadrant of the flow (same scale bar of 20 μm is used for all four …
[HTML][HTML] A weakly supervised deep learning framework for sorghum head detection and counting
…, S Ninomiya, B Ganapathysubramanian… - Plant …, 2019 - spj.science.org
The yield of cereal crops such as sorghum (Sorghum bicolor L. Moench) depends on the
distribution of crop-heads in varying branching arrangements. Therefore, counting the head …
distribution of crop-heads in varying branching arrangements. Therefore, counting the head …