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

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Suarez, L. A., Apan, A. and Werth, J. (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

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Article Link: http://dx.doi.org/10.1016/j.isprsjprs.2016.08.004

Publisher URL: http://www.sciencedirect.com/science/article/pii/S0924271616302635


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 (PLS-R) 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.

Item Type:Article
Business groups:Crop and Food Science
Keywords:Cotton PLS-R Hyperspectral data Herbicide drift Yield
Subjects:Plant culture > Field crops > Textile and fibre plants
Plant pests and diseases
Live Archive:13 Sep 2016 05:36
Last Modified:03 Sep 2021 16:50

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