Please use this identifier to cite or link to this item:
Title: Hardware and embedded algorithms for real time variable rate fertiliser applications
Contributor(s): Stover, Joshua  (author); Falzon, Greg  (author)orcid ; Jensen, Troy (author); Schroeder, Bernard (author); Lamb, David W  (author)
Publication Date: 2017-10-16
Open Access: Yes
DOI: 10.5281/zenodo.895528Open Access Link
Handle Link:
Abstract: Efficient use of fertilisers, in particular the use of Nitrogen (N), is one of the rate-limiting factors in meeting global food production requirements. While N is a key driver in increasing crop yields, overuse can also lead to negative environmental and health impacts. It has been suggested that Variable Rate Fertiliser (VRF) techniques may help to reduce excessive N applications. VRF seeks to spatially vary fertiliser input based on estimated crop requirements, however a major challenge in the operational deployment of VRF systems is the automated processing of large amounts of sensor data in real-time. Machine learning techniques have shown promise in their ability to process these large, high-velocity data streams, and to produce accurate predictions. This paper will use a simulation testing methodology on real hardware to compare two existing machine learning algorithms and a prototype implementation of a newly developed algorithm for their applicability to VRF application.
Publication Type: Conference Publication
Conference Name: 7th Asian-Australasian Conference on Precision Agriculture
Conference Details: The International Tri-Conference for Precision Agriculture in 2017, Hamilton, New Zealand, 16-18 October, 2017
Source of Publication: Proceedings of the 7th Asian-Australasian Conference on Precision Agriculture, p. 1-8
Publisher: The New Zealand Institute for Plant & Food Research Ltd
Place of Publication: Hamilton, New Zealand
Field of Research (FOR): 070106 Farm Management, Rural Management and Agribusiness
079902 Fertilisers and Agrochemicals (incl. Application)
170203 Knowledge Representation and Machine Learning
Socio-Economic Outcome Codes: 960904 Farmland, Arable Cropland and Permanent Cropland Land Management
890201 Application Software Packages (excl. Computer Games)
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Other Links:
Appears in Collections:Conference Publication
School of Science and Technology

Files in This Item:
2 files
File Description SizeFormat 
Show full item record
Google Media

Google ScholarTM



This item is licensed under a Creative Commons License Creative Commons