| Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close-range sensing H Liu, B Bruning, T Garnett, B Berger Computers and Electronics in Agriculture 175, 105621, 2020 | 115 | 2020 |
| A review of recent sensing technologies to detect invertebrates on crops H Liu, S Lee, JS Chahl Precision Agriculture 17 (4), DOI:10.1007/s11119-016-9473-6, 2016 | 81 | 2016 |
| The Development of Hyperspectral Distribution Maps to Predict the Content and Distribution of Nitrogen and Water in Wheat (Triticum aestivum) B Bruning, H Liu, C Brien, B Berger, M Lewis, T Garnett Frontiers in plant science 10, 1380, 2019 | 79 | 2019 |
| A multispectral machine vision system for invertebrate detection on green leaves H Liu, JS Chahl Computers and Electronics in Agriculture 150, 279-288, 2018 | 56 | 2018 |
| Development of a machine vision system for weed detection during both off-season and in-season in broadacre no-tillage cropping lands H Liu, SH Lee, JS Chahl American Journal of Agricultural and Biological Sciences 9 (2), 174-193, 2014 | 44 | 2014 |
| A multispectral 3-D vision system for invertebrate detection on crops H Liu, SH Lee, JS Chahl IEEE Sensors Journal 17 (22), 7502-7515, 2017 | 40 | 2017 |
| Approaches, applications, and future directions for hyperspectral vegetation studies: an emphasis on yield‐limiting factors in wheat B Bruning, B Berger, M Lewis, H Liu, T Garnett The Plant Phenome Journal 3 (1), e20007, 2020 | 39 | 2020 |
| Proximal detecting invertebrate pests on crops using a deep residual convolutional neural network trained by virtual images H Liu, JS Chahl Artificial intelligence in agriculture 5, 13-23, 2021 | 36 | 2021 |
| The performances of hyperspectral sensors for proximal sensing of nitrogen levels in wheat H Liu, B Bruning, T Garnett, B Berger Sensors 20 (16), 4550, 2020 | 30 | 2020 |
| Registration of multispectral 3D points for plant inspection H Liu, SH Lee, JS Chahl Precision agriculture 19, 513-536, 2018 | 29 | 2018 |
| Transformation of a high-dimensional color space for material classification H Liu, SH Lee, JS Chahl Journal of the Optical Society of America A 34 (4), 523-532, 2017 | 22 | 2017 |
| Development of a proximal machine vision system for off-season weed mapping in broadacre no-tillage fallows H Liu, SH Lee, JS Chahl Journal of Computer Science 9 (12), 1803-1821, 213 | 17* | 213 |
| Hyperspectral imaging detects biological stress of wheat for early diagnosis of crown rot disease Y Xie, D Plett, M Evans, T Garrard, M Butt, K Clarke, H Liu Computers and Electronics in Agriculture 217, 108571, 2024 | 15 | 2024 |
| The promise of hyperspectral imaging for the early detection of crown rot in wheat Y Xie, D Plett, H Liu AgriEngineering 3 (4), 924-941, 2021 | 15 | 2021 |
| Hyperspectral imaging predicts yield and nitrogen content in grass–legume polycultures KR Ball, H Liu, C Brien, B Berger, SA Power, E Pendall Precision Agriculture 23 (6), 2270-2288, 2022 | 12 | 2022 |
| Detecting crown rot disease in wheat in controlled environment conditions using digital color imaging and machine learning Y Xie, D Plett, H Liu AgriEngineering 4 (1), 141-155, 2022 | 12 | 2022 |
| An evaluation of the contribution of ultraviolet in fused multispectral images for invertebrate detection on green leaves H Liu, SH Lee, JS Chahl Precision Agriculture 17 (4), DOI:10.1007/s11119-016-9472-7, 2016 | 12 | 2016 |
| Stitching of video sequences for weed mapping H Liu, SH Lee, JS Chahl International Conference on Intelligent Information Hiding and Multimedia …, 2015 | 7 | 2015 |
| Development of a green plant image segmentation method of machine vision system for no-tillage fallow weed detection H Liu, SH Lee, JS Chahl Society for Engineering in Agriculture Conference : innovative agricultural …, 2013 | 6 | 2013 |
| Bioinspired invertebrate pest detection on standing crops JS Chahl, H Liu Bioinspiration, Biomimetics, and Bioreplication VIII 10593, 68-81, 2018 | 4 | 2018 |
Sang-Heon Lee (이상헌)The University of South AustraliaPotvrđena adresa e-pošte na unisa.edu.au
