Author

B. Ganapathysubramanian

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

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Plant Biology· 5

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Organism:plant (other)· 5 Assay:imaging· 4 Research Focus:cell type annotation· 3 Research Focus:clustering· 3 Approach:deep learning· 2 Research Focus:differential expression· 2 Research Focus:spatial domain detection· 2 Research Focus:benchmarking· 1

Frequent Co-Authors

T. Z. Jubery· 3 shared A. K. Singh· 2 shared S. Sarkar· 2 shared A. Singh· 1 shared A. V. Nishimwe· 1 shared B. A. Miller· 1 shared C. Carley· 1 shared C. J. Lawrence-Dill· 1 shared

Activity by Year

2018-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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3 shared papersPlant Biology

T. Z. Jubery

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5 papers

Recent·Most cited

Leveraging Soil Mapping and Machine Learning to Improve Spatial Adjustments in Plant Breeding Trials

M. E. Carroll, L. G. Riera, B. A. Miller, P. M. Dixon, B. Ganapathysubramanian, S. Sarkar, A. K. Singh

The paper compares spatial adjustment methods in plant breeding trials, finding that XGBoost improves yield estimates by incorporating soil feature modeling at different scales.

bioRxiv · Jan 3, 2024Plant Biology

Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel

M. C. Tross, M. W. Grzybowski, T. Z. Jubery, R. J. Grove, A. V. Nishimwe, J. V. Torres-Rodriguez, G. Sun, B. Ganapathysubramanian, J. C. Schnable

This study uses data-driven methods to analyze hyperspectral leaf reflectance data from maize plants, identifying genetic markers correlated with reflectance patterns and chlorophyll content.

bioRxiv · Dec 15, 2023Plant Biology

Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification

D. Dong, K. Nagasubramanian, R. Wang, U. K. Frei, T. Z. Jubery, T. Lübberstedt, B. Ganapathysubramanian

This paper applies self-supervised learning to maize kernel classification and segmentation, showing improved performance and annotation efficiency compared to supervised methods.

Frontiers in Plant Science · Apr 14, 2023Peer-reviewedPlant Biology

Using Machine Learning To Develop A Fully Automated Soybean Nodule Acquisition Pipeline (SNAP)

T. Z. Jubery, C. Carley, S. Sarkar, A. Singh, B. Ganapathysubramanian, A. K. Singh

The paper introduces SNAP, a deep learning pipeline for automated soybean nodule quantification with 99% accuracy, enabling efficient analysis of nodule traits and genetic-environmental interactions.

bioRxiv · Oct 12, 2020Plant Biology

Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning

N. Zhou, Z. D. Siegel, S. Zarecor, N. Lee, D. A. Campbell, C. M. Andorf, D. Nettleton, C. J. Lawrence-Dill, B. Ganapathysubramanian, J. W. Kelly, I. Friedberg

This paper evaluates crowdsourcing for generating high-quality ground truth data for machine learning in plant phenomics, finding that Amazon MTurk workers match expert performance.

bioRxiv · May 30, 2018Plant Biology