Ph.D. 2021
Now: Deep Learning Scientist, Bayer Crop Science
- Ph.D., Electrical Engineering, Iowa State University, 2021
- B.Tech., Electronics and Communication Engineering, RNSIT, Bangalore, India, 2015
Koushik was a Ph.D. student jointly supervised by Prof. Baskar Ganapathysubramanian and Prof. Soumik Sarkar. After receiving his undergraduate degree in 2015, he worked at Nubesol Technologies, Bangalore, for about seven months. His research interests included machine learning, deep learning, and computer vision, and he worked on deep learning for plant disease prediction.
He graduated from the SCS Lab in June 2021 and is currently a Deep Learning Scientist at Bayer Crop Science.
Research interests: Machine learning, deep learning, computer vision
Publications with the lab (20)
- Cyber-agricultural systems for crop breeding and sustainable production — Trends in Plant Science 29 (2) (2024)
- Self‐supervised learning improves classification of agriculturally important insect pests in plants — The Plant Phenome Journal (2023)
- Plant phenotyping with limited annotation: Doing more with less — The Plant Phenome Journal (2022)
Show all 20
- Privacy-preserving deep models for plant stress phenotyping — AI for Agriculture and Food Systems (2022)
- Self-supervised agricultural insect pest classification — Authorea Preprints, Authorea (2022)
- Exploring the use of 3d point cloud data for improved plant stress rating — AI for agriculture and food systems (2021)
- Self-supervised learning improves agricultural pest classification — AI for agriculture and food systems (2021)
- How useful is active learning for image‐based plant phenotyping? — The Plant Phenome Journal (2021)
- High-throughput phenotyping in soybean — High-throughput crop phenotyping (2021)
- Challenges and opportunities in machine-augmented plant stress phenotyping — Trends in Plant Science 26 (1) (2021)
- Distributed deep learning for persistent monitoring of agricultural fields — NeurIPS 2021 AI for science workshop (2021)
- Usefulness of interpretability methods to explain deep learning based plant stress phenotyping — arXiv preprint arXiv:2007.05729 (2020)
- Automated trichome counting in soybean using advanced image‐processing techniques — Applications in plant sciences (2020)
- Soybean Root Phenomics — Iowa State University Research and Demonstration Farms Progress Reports (2019)
- Plant disease identification using explainable 3D deep learning on hyperspectral images — Plant Methods (2019)
- Development of optimized phenomic predictors for efficient plant breeding decisions using phenomic-assisted selection in soybean — Plant Phenomics, AAAS (2019)
- Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps — arXiv preprint arXiv:1804.08831 (2018)
- PIRM2018 challenge on spectral image super-resolution: methods and results — Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)
- NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images — (2018)
- Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems — Plant methods (2018)
koushikn (@) iastate.eduGoogle Scholar