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Kalra, A
Kereszturi, G
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Authors
Yule, I.J
Pullanagari, R.R
Kereszturi, G
Irwin, M.E
McVeagh, P.J
Cushnahan, T
White, M
Kalra, A
Pitla, S
Luck, J.D
Topics
Remote Sensing Applications in Precision Agriculture
Artificial Intelligence (AI) in Agriculture
Type
Oral
Poster
Year
2016
2024
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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. AIR-N: AI-Enabled Robotic Precision Nitrogen Management Platform

The AI-Enabled Robotic Nitrogen Management (AIR-N) system is a versatile, cloud-based platform designed for precision nitrogen management in agriculture, targeting the reduction of nitrous oxide emissions as emphasized by the EPA. This end-to-end integrated system is adaptable to various cloud services, enhancing its applicability across different farming environments. AIR-N's framework consists of three primary components: a sensing layer for gathering data, a cloud layer where AI and machine... A. Kalra, S. Pitla, J.D. Luck