Items where Subject is "Remote sensing"

Article

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. https://doi.org/10.1016/j.compag.2022.107024

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. https://doi.org/10.3390/agronomy11091711

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. https://doi.org/10.3390/rs16183389

Beutel, T. S. and Graz, F. P. (2023) Can we benchmark annual ground cover maintenance? The Rangeland Journal, 44 (6). pp. 333-342. https://doi.org/10.1071/RJ22041

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. https://doi.org/10.1071/RJ21018

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. https://doi.org/10.1007/s11119-006-9011-z

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. https://doi.org/10.1016/j.fcr.2010.01.010

Gautam, D., Mawardi, Z., Elliott, L., Loewensteiner, D., Whiteside, T. and Brooks, S. J. (2025) Detection of Invasive Species (Siam Weed) Using Drone-Based Imaging and YOLO Deep Learning Model. Remote Sensing, 17 (1). p. 120. https://doi.org/10.3390/rs17010120

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. https://doi.org/10.1016/j.compag.2007.05.004

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.

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). https://doi.org/10.1071/CP23340

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. https://doi.org/10.1016/j.cosust.2009.07.007

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. https://doi.org/10.1071/AR06279

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. https://doi.org/10.1016/j.ecolind.2021.108426

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

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.

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 .

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. https://doi.org/10.1071/AR05361

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. https://doi.org/10.1016/j.fcr.2007.03.023

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). https://doi.org/10.1071/PC24031

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. https://doi.org/10.1016/j.rse.2020.111737

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. https://doi.org/10.1109/TGRS.2024.3389651

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. https://doi.org/10.34133/2021/9874650

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. https://doi.org/10.34133/2022/9768502

Book Section

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. https://doi.org/10.1016/B978-0-12-374431-9.00019-0

Monograph

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

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

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.

Conference or Workshop Item

Cronin, N.L.R., Lucas, R.M., Witte, C., Milne, A.K. and Hoffmann, M. B. (2001) Synthetic aperture radar for AGB estimation in Australia's woodlands. In: International Geoscience and Remote Sensing Symposium (IGARSS), 9-13 July 2001, Sydney, Australia.

Graz, F. P. and Beutel, T. S. (2025) Cumulative ground cover maintenance: what does it tell us about the grazing landscape and its management? In: 12th International Rangeland Congress IRC 2025, 2-6 June 2025, Adelaide, South Australia.

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.

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.

Muller, J. and Spiegel, N. B. (2025) Testing virtual fencing for the sustainable management of north Australian rangelands: Impacts on beef cattle grazing behaviour, pasture resource, and cattle production. In: 12th International Rangeland Congress IRC 2025, 2-6 June 2025, Adelaide, South Australia.

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. https://doi.org/10.1109/IGARSS52108.2023.10282230

Trevithick, R., Denham, R., Weis, G. and Beutel, T. S. (2025) Evolving VegMachine.net: enhancing a successful tool for Australian rangeland cover analysis. In: 12th International Rangeland Congress IRC 2025, 2-6 June 2025, Adelaide, South Australia.

Wilson, C., Gaschk, C., Gentle, M. N. and Marshall, D. (2023) Enhancing feral pig management through spatial research: real-world applications. In: 2nd Pest Animal and Weed Symposium, 28-31 August 2023, Dalby, Australia.

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.