| Machine learning for high throughput stress phenotyping in plants A Singh, B Ganapathysubramanian, AK Singh, S Sarkar Trends in plant science 21 (2), 110–124, 2016 | 937 | 2016 |
| Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives Singh, A.K., Ganapathysubramanian, B., Sarkar, S. and Singh, A. Trends in Plant Science., 2018 | 508 | 2018 |
| An explainable deep machine vision framework for plant stress phenotyping S Ghosal, D Blystone, AK Singh, B Ganapathysubramanian, A Singh, ... Proceedings of the National Academy of Sciences 115 (18), 4613-4618, 2018 | 489 | 2018 |
| Plant disease identification using explainable 3D deep learning on hyperspectral images K Nagasubramanian, S Jones, AK Singh, S Sarkar, A Singh, ... Plant Methods 15 (1), 1-10, 2019 | 329* | 2019 |
| Ntire 2018 challenge on spectral reconstruction from rgb images B Arad, O Ben-Shahar, R Timofte, L Van Gool, L Zhang, MH Yang, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 211 | 2018 |
| A real-time phenotyping framework using machine learning for plant stress severity rating in soybean HS Naik, J Zhang, A Lofquist, T Assefa, S Sarkar, D Ackerman, A Singh, ... Plant methods 13 (1), 23, 2017 | 190 | 2017 |
| Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems K Nagasubramanian, S Jones, S Sarkar, AK Singh, A Singh, ... Plant Methods 14 (1), 86, 2018 | 164 | 2018 |
| A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting G Sambuddha, Z Bangyou, CC Scott, PB Andries, JR David, W Xuemin, ... Plant Phenomics, 2019 | 160 | 2019 |
| Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping A Singh, S Jones, B Ganapathysubramanian, S Sarkar, D Mueller, ... Trends in Plant Science, 2021 | 132 | 2021 |
| A deep learning framework to discern and count microscopic nematode eggs A Akintayo, GL Tylka, AK Singh, B Ganapathysubramanian, A Singh, ... Scientific Reports 8 (1), 9145, 2018 | 117* | 2018 |
| Computer vision and machine learning for robust phenotyping in genome-wide studies J Zhang, HS Naik, T Assefa, S Sarkar, RVC Reddy, A Singh, ... Scientific reports 7, 44048, 2017 | 117 | 2017 |
| Genome-wide association and epistasis studies unravel the genetic architecture of sudden death syndrome resistance in soybean Jiaoping Zhang , Arti Singh, Daren Shane Mueller and Asheesh Kumar Singh The Plant Journal, 2015 | 112 | 2015 |
| Computer vision and machine learning enabled soybean root phenotyping pipeline KG Falk, TZ Jubery, SV Mirnezami, KA Parmley, S Sarkar, A Singh, ... Plant Methods 16 (1), 5, 2020 | 104 | 2020 |
| Genetic architecture of Charcoal Rot (Macrophomina phaseolina) Resistance in Soybean revealed using a diverse panel SM Coser, RV Chowda Reddy, J Zhang, DS Mueller, A Mengistu, ... Frontiers in plant science 8, 1626, 2017 | 93 | 2017 |
| Allelic variation at Psy1-A1 and association with yellow pigment in durum wheat grain A Singh, S Reimer, CJ Pozniak, FR Clarke, JM Clarke, RE Knox, ... Theoretical and Applied Genetics 118 (8), 1539-1548, 2009 | 90 | 2009 |
| Raffinose family oligosaccharides: friend or foe for human and plant health? D Elango, K Rajendran, L Van der Laan, S Sebastiar, J Raigne, ... Frontiers in Plant Science, 304, 2022 | 83 | 2022 |
| UAS-Based Plant Phenotyping for Research and Breeding Applications W Guo, ME Carroll, A Singh, TL Swetnam, N Merchant, S Sarkar, ... Plant Phenomics 2021, 2021 | 83 | 2021 |
| Identification and mapping of leaf, stem and stripe rust resistance quantitative trait loci and their interactions in durum wheat A Singh, MP Pandey, AK Singh, RE Knox, K Ammar, JM Clarke, ... Molecular breeding 31 (2), 405-418, 2013 | 82 | 2013 |
| Identification and mapping in spring wheat of genetic factors controlling stem rust resistance and the study of their epistatic interactions across multiple environments A Singh, RE Knox, RM DePauw, AK Singh, RD Cuthbert, HL Campbell, ... Theoretical and Applied Genetics 126 (8), 1951-1964, 2013 | 74 | 2013 |
| Main and epistatic loci studies in soybean for Sclerotinia sclerotiorum resistance reveal multiple modes of resistance in multi-environments TC Moellers, A Singh, J Zhang, J Brungardt, M Kabbage, DS Mueller, ... Scientific reports 7 (1), 3554, 2017 | 70 | 2017 |
Baskar GanapathysubramanianNSF/USDA AI Institute, Iowa State UniversityVerified email at iastate.edu
