Image analysis and artificial intelligence based approach for soil-water and nitrogen status estimation

McCarthy, Alison ORCID: https://orcid.org/0000-0003-4595-6447 and Nguyen, Tai and Raine, Steven (2015) Image analysis and artificial intelligence based approach for soil-water and nitrogen status estimation. In: 2nd Australian Cotton Research Conference 2015: Science Securing Cotton's Future, 8-10 Sept 2015, Toowoomba, Australia.

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Abstract

Optimal crop yields require optimisation of both water and nitrogen application. Industry standard soil-water sensors require contact with the soil and provide information for a single point in the field although there is often spatial variability in soil type and crop growth. Nitrogen content is typically determined using destructive manual soil coring followed by laboratory testing. It is often not practical to install multiple soil-water sensors in a commercial field situation or to conduct multiple soil cores throughout the cotton season.

A non-contact soil-water and nitrogen estimation system offers growers potential savings by optimising water and fertiliser management and efficiency and crop productivity. Existing non-contact approaches typically have low spatial resolution and cannot discriminate plants from soil. An alternative approach is a camera-based sensing system that estimates soil-water and plant nitrogen status. This project has developed a proof-of concept infield camera-based plant sensing system and model that estimates soil-water, plant nitrogen status and fruit growth for cotton.

A data fusion algorithm was developed that can determine current and predict future soil-water, nitrogen and fruit load of cotton plants based on day of the season, weather data and visual plant response captured using cameras. These models have potential to be used instead of industry-standard models APSIM and OZCOT to predict crop production throughout the season as part of automated control systems to optimise irrigation and fertiliser application. The procedure used to develop the model could be applied to any crop.

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Item Type:	Conference or Workshop Item (Commonwealth Reporting Category E) (Speech)
Refereed:	No
Item Status:	Live Archive
Additional Information:	Abstract only published in Proceedings. Copyright 2015 author. This publication is copyright. It may be reproduced in whole or in part for the purposes of study, research, or review, but is subject to the inclusion of an acknowledgment of the source.
Faculty/School / Institute/Centre:	Historic - Institute for Agriculture and the Environment
Faculty/School / Institute/Centre:	Historic - Institute for Agriculture and the Environment
Date Deposited:	20 Jun 2016 00:03
Last Modified:	05 Aug 2020 04:16
Uncontrolled Keywords:	irrigation; fertiliser; non-contact; machine learning; automation
Fields of Research (2008):	07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070104 Agricultural Spatial Analysis and Modelling 09 Engineering > 0906 Electrical and Electronic Engineering > 090602 Control Systems, Robotics and Automation 07 Agricultural and Veterinary Sciences > 0703 Crop and Pasture Production > 070302 Agronomy
Fields of Research (2020):	30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300206 Agricultural spatial analysis and modelling 40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400799 Control engineering, mechatronics and robotics not elsewhere classified 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3004 Crop and pasture production > 300403 Agronomy
Socio-Economic Objectives (2008):	D Environment > 96 Environment > 9609 Land and Water Management > 960905 Farmland, Arable Cropland and Permanent Cropland Water Management
URI:	http://eprints.usq.edu.au/id/eprint/29212

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