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Irwin, M.E
Mora, H
Trindall, J
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Authors
Yule, I.J
Pullanagari, R.R
Kereszturi, G
Irwin, M.E
McVeagh, P.J
Cushnahan, T
White, M
Trindall, J
Rainbow, R
Celades, J.A
Caicedo, J.H
García, C.E
Mora, H
Topics
Remote Sensing Applications in Precision Agriculture
Big Data, Data Mining and Deep Learning
Profitability and Success Stories in Precision Agriculture
Type
Oral
Poster
Year
2016
2018
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1. Hyperspectral Imaging to Measure Pasture Nutrient Concentration and Other Quality Parameters

Managing pasture nutrient requirements on large hill country sheep and beef properties based on information from soil sampling is expensive because of the time and labor involved. High levels of error are also expected as these properties are often greatly variable and it is therefore extremely difficult to sample intensively enough to capture this variation. Extensive sampling was also not considered viable as there was no effective means of spreading fertilizer with a variable rate capability... I.J. Yule, R.R. Pullanagari, G. Kereszturi, M.E. Irwin, P.J. Mcveagh, T. Cushnahan, M. White

2. Accelerating Precision Agriculture to Decision Agriculture: Enabling Digital Agriculture in Australia

For more than two decades, the success of Australia’s agricultural and rural sectors has been supported by the work of the Rural Research and Development Corporations (RDCs). The RDCs are funded by industry and government. For the first time, all fifteen of Australia’s RDC’s have joined forces with the Australian government to design a solution for the use of big data in Australian agriculture. This is the first known example of a nationwide approach for the digital transformation... J. Trindall, R. Rainbow

3. Toward a Precision Agricultural Implementation for Sugar Cane Plantations in Southwestern Region of Colombia, South America

The Colombian Sugar Cane Research Center, CENICAÑA, has initiated an ambitious project for the implementation of Precision Agriculture (PA) technologies in the Cauca river valley region, where one of its main objectives is to have the ability to collect large volumes of geospatial data. The main sugarcane growers in the country perform their work in the selected work area, which covers an area of ​​approximately 242,000 ha, characterized by diverse topographic and edaphic conditions.... J.A. Celades, J.H. Caicedo, C.E. García, H. Mora