Items where Subject is "Remote sensing"

2024

Bai, S. H., Tootoonchy, M., Kämper, W., Tahmasbian, I., Farrar, M. B., Boldingh, H., Pereira, T., Jonson, H., Nichols, J., Wallace, H. M. and Trueman, S. J. (2024) Predicting Carbohydrate Concentrations in Avocado and Macadamia Leaves Using Hyperspectral Imaging with Partial Least Squares Regressions and Artificial Neural Networks. Remote Sensing, 16 (18). p. 3389. ISSN 2072-4292

McKenna, P. B., Ufer, N., Glenn, V., Dale, N., Carins, T., Nguyen, T. h., Thomson, M. B., Young, A. J., Buck, S. R., Jones, P. and Erskine, P. D. (2024) Mapping pasture dieback impact and recovery using an aerial imagery time series: a central Queensland case study. Crop and Pasture Science, 75 (9).

Wilson, C., Gentle, M. N. and Fancourt, B. A. (2024) Advancing spatial analysis of invasive species movement data to improve monitoring, control programs and decision making: feral cat home range as a case study. Pacific Conservation Biology, 30 (5).

Yang, J. X., Zhou, J., Wang, J., Tian, H. and Liew, A. W.-C. (2024) LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 62 . pp. 1-15. ISSN 1558-0644

2023

Beutel, T. S. and Graz, F. P. (2023) Can we benchmark annual ground cover maintenance? The Rangeland Journal, 44 (6). pp. 333-342.

Department of Agriculture and Fisheries, Queensland, (2023) Queensland AgTech Roadmap 2023–2028. Technical Report. State of Queensland.

Potgieter, A., Camino, C., Poblete, T., George-Jaeggli, B. and et, a. (2023) Advances in the Study of Biochemical, Morphological and Physiological Traits of Wheat and Sorghum Crops in Australia Using Hyperspectral Data and Machine Learning. In: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 16-21 July 2023, Pasadena, Calif. USA.

2022

Akbarian, S., Xu, C.-Y., Wang, W., Ginns, S. P. and Lim, S. (2022) Sugarcane yields prediction at the row level using a novel cross-validation approach to multi-year multispectral images. Computers and Electronics in Agriculture, 190 , 107024.

Han, L., Cao, J., Ibell, P. and Diczbalis, Y. (2022) DigiHort: Digital Twins for Innovation of Future Orchard Systems. In: TropAg 2022 International Agriculture Conference, 31 October - 2 November 2022, Brisbane, Australia.

Zhi, X., Massey-Reed, S. R., Wu, A., Potgieter, A., Borrell, A., Hunt, C. H., Jordan, D., Zhao, Y., Chapman, S., Hammer, G. and George-Jaeggli, B. (2022) Estimating Photosynthetic Attributes from High-Throughput Canopy Hyperspectral Sensing in Sorghum. Plant phenomics (Washington, D.C.), 2022 . p. 9768502. ISSN 2643-6515

2021

Anderson, N. T., Walsh, K. B., Koirala, A., Wang, Z., Amaral, M. H., Dickinson, G. R., Sinha, P. and Robson, A. J. (2021) Estimation of Fruit Load in Australian Mango Orchards Using Machine Vision. Agronomy, 11 (9). p. 1711. ISSN 2073-4395

Beutel, T. S., Shepherd, R., Karfs, R. A., Abbott, B. N., Eyre, T., Hall, T. J. and Barbi, E. (2021) Is ground cover a useful indicator of grazing land condition? The Rangeland Journal, 43 (1). pp. 55-64.

Pringle, M. J., O'Reagain, P. J., Stone, G. S., Carter, J. O., Orton, T. G. and Bushell, J. J. (2021) Using remote sensing to forecast forage quality for cattle in the dry savannas of northeast Australia. Ecological Indicators, 133 . p. 108426. ISSN 1470-160X

Zhao, Y., Zheng, B., Chapman, S. C., Laws, K., George-Jaeggli, B., Hammer, G. L., Jordan, D. R. and Potgieter, A. B. (2021) Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery. Plant Phenomics, 2021 . p. 9874650. ISSN null

2020

Atzeni, M., Muehlebach, J., Fielder, D. and Mayer, D. G. (2020) Detect-alert-deter system for enhanced biosecurity and risk assessment. Project Report. AgriFutures.

Xie, Z., Phinn, S. R., Game, E. T., Pannell, D. J., Hobbs, R. J., Briggs, P. R., Beutel, T. S., Holloway, C. H. and McDonald-Madden, E. (2020) Corrigendum to “Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands - A first step towards identifying degraded lands for conservation” [Remote Sens. Environ. 232 (2019), 111317]. Remote Sensing of Environment, 241 . p. 111737. ISSN 0034-4257

2014

Johansen, K., Robson, A., Samson, P., Sallam, N., Chandler, K., Eaton, A., Derby, L. and Jennings, J. (2014) Mapping canegrub damage from high spatial resolution satellite imagery. Proceedings of the Australian Society of Sugar Cane Technology, 36 . pp. 62-70. ISSN 0726-0822

2013

Robson, A., Abbott, C., Bramley, R. and Lamb, D. (2013) Remote Sensing - based precision agriculture tool for the sugar industry. Project Report. Sugar Research Australia.

2011

Robson, A., Abbott, C., Lamb, D. and Bramley, R. (2011) Paddock and regional scale yield prediction of cane using satellite imagery. Proceedings of the Australian Society of Sugar Cane Technology, 33 . ISSN 0726-0822

2010

Fitzgerald, G.J., Rodriguez, D. and O'Leary, G. (2010) Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI). Field Crops Research, 116 (3). pp. 318-324.

Robson, A., Hughes, J.R. and Coventry, R.J. (2010) Using spatial mapping layers to understand variability in precision agricultural systems for sugarcane production. Proceedings of the Australian Society of Sugar Cane Technology, 32 . p. 713. ISSN 0726-0822

2009

Meinke, H., Howden, S. M., Struick, P. C., Nelson, R., Rodriguez, D. and Chapman, S. C. (2009) Adaptation science for agriculture and natural resource management - urgency and theoretical basis. Current Opinion in Environmental Sustainability, 1 (1). pp. 69-76. ISSN 1877-3435

Roboson, A. and Medway, J. (2009) Remote sensing applications for cotton. Australian Cottongrower, 30 (4). pp. 40-43.

Rodriguez, D., Robson, A. J. and Belford, R. (2009) Dynamic and Functional Monitoring Technologies for Applications in Crop Management. In: Crop Physiology: Applications for Genetic Improvement and Agronomy. Elsevier. ISBN 978-0-12-374431-9

2008

Holloway, C. T., O'Reagain, P. J. and Tomkins, N. (2008) Patch selection by cattle can be quantified using satellite imagery and GPS in extensive, semi-arid savannas. In: Multifunctional grasslands in a changing world, Volume 1: XXI International Grassland Congress and VIII International Rangeland Congress., 29th June - 5th July 2008, Hohhot, China.

2007

Jensen, T., Apan, A., Young, F. and Zeller, L. (2007) Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform. Computers and Electronics in Agriculture, 59 (1-2). pp. 66-77.

Potgieter, A.B., Apan, A., Dunn, P. and Hammer, G. (2007) Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery. Australian Journal of Agricultural Research, 58 (4). pp. 316-325.

Tilling, A.K., O'Leary, G.J., Ferwerda, J.G., Jones, S.D., Fitzgerald, G.J., Rodriguez, D. and Belford, R. (2007) Remote sensing of nitrogen and water stress in wheat. Field Crops Research, 104 (1-3). pp. 77-85.

2006

Fitzgerald, G.J., Rodriguez, D., Christensen, L.K., Belford, R., Sadras, V.O. and Clarke, T.R. (2006) Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments. Precision Agriculture, 7 (4). pp. 233-248.

Rodriguez, D., Fitzgerald, G.J., Belford, R. and Christensen, L.K. (2006) Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts. Australian Journal of Agricultural Research, 57 (7). pp. 781-789.

2004

Wright, G. C., Robson, A. and Mills, G. (2004) Application of remote sensing technologies to improve yield and water-use efficiency in irrigated peanuts. In: New Directions for a Diverse Planet: 4th International Crop Science Congress, October 2004, Brisbane, Australia.