Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

Suarez, L. A. and Apan, Armando and Werth, Jeff (2016) Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield. ISPRS Journal of Photogrammetry and Remote Sensing, 120. pp. 65-76. ISSN 0924-2716

Abstract

Yield loss in crops is often associated with plant disease or external factors such as environment, water
supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation.
Herbicide drift can be a combination of improper practices and environmental conditions which
can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time
consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations
in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused
by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLSR)
models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed
with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality,
photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA)
were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables
were highly affected by the chemical. Four PLS-R models for predicting yield were developed
according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated
by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes
(RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the
results of this study show that it is possible to accurately predict yield after a simulated herbicide drift
of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective
and non-destructive alternative based on the internal response of the cotton leaves.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying
Date Deposited: 10 Oct 2016 05:08
Last Modified: 17 Jan 2018 02:03
Uncontrolled Keywords: cotton, hyperspectral data, PLS-R, herbicide drift, yield
Fields of Research : 09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing
07 Agricultural and Veterinary Sciences > 0703 Crop and Pasture Production > 070308 Crop and Pasture Protection (Pests, Diseases and Weeds)
Funding Details:
Identification Number or DOI: 10.1016/j.isprsjprs.2016.08.004
URI: http://eprints.usq.edu.au/id/eprint/29743

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