Detection of phenoxy herbicide dosage in cotton crops through the analysis of hyperspectral data

ABSTRACT

Although herbicide drifts are known worldwide and recognized as one of the major risks for crop security in the agriculture sector, the traditional assessment of damage in cotton crops caused by herbicide drifts has several limitations. The aim of this study was to assess proximal sensor and modelling techniques in the detection of phenoxy herbicide dosage in cotton crops. In situ hyperspectral data (400–900 nm) were collected at four different times after ground-based spraying of cotton crops in a factorial randomized complete block experimental design with dose and timing of exposure as factors. Three chemical doses: nil, 5% and 50% of the recommended label rate of the herbicide 2,4-D were applied to cotton plants at specific growth stages (i.e. 4–5 nodes, 7–8 nodes and 11–12 nodes). Results have shown that yield had a significant correlation (p-values <0.05) to the green peak (~550 nm) and NIR range, as the pigment and cell internal structure of the plants are key for the assessment of damage. Prediction models integrating raw spectral data for the prediction of dose have performed well with classification accuracy higher than 80% in most cases. Visible and NIR range were significant in the classification. However, the inclusion of the green band (around 550 nm) increased the classification accuracy by more than 25%. This study shows that hyperspectral sensing has the potential to improve the traditional methods of assessing herbicide drift damage.

Acknowledgements

This study is part of a major project funded by the Cotton Research and Development Corporation – CRDC Australia (Project USQ1404) and by the Australian Commonwealth Government through the Research Training Program (RTP). Special thanks to Michelle Keenan from the Queensland Department of Agriculture and Fisheries at Toowoomba (QDAF) and Rachel King from the statistical consulting unit of USQ for all the knowledge and support in this study. Also thanks to Kristian Hovde Liland for the provision of the statistical code implemented in part of this study (sMC algorithm) and to Fernando Mosquera, Allan Castillo, and Dr Wan Nor Zanariah Zainol Abdullah for their support during the field work.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study is part of a major project funded by the Cotton Research and Development Corporation – CRDC Australia (Project USQ1404) and by the Australian Commonwealth Government through the Research Training Program (RTP).