Prof. Hassan Dahi Profile

Prof. Hassan Dahi

at Agriculture Research Ctr

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Proceedings Article | 21 October 2019 Presentation + Paper

Early detection of pink bollworm Pectinophora gossypiella (Saunders) using remote sensing technologies

M. Yones, Ghada Khdery, H. Dahi, E. Farg, S. Arafat, Walaa Gamil

Proceedings Volume 11149, 111491C (2019) https://doi.org/10.1117/12.2528300

KEYWORDS: Reflectivity, Remote sensing, Photosynthesis, Near infrared, Short wave infrared radiation, Statistical analysis, Agriculture, Spectroscopy, Visible radiation, Infrared radiation

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Pink bollworm (Pectinophora gossypiella (Saunders), PBW) is a major pest of cotton worldwide. The system of cotton pest management adopted in Egypt is described in relation to the economic thresholds of infestation (infested green boll3%). The best way to minimize impacts of a pest outbreak is to regularly monitor the crop to detect the onset of pest and to take timely action if the pest is present. Monitoring should be done on the basis of local knowledge and up-to-date and reliable global information. Reduction in losses caused by pests by timely and effective control measures will considerably add to food production in the country. Monitoring of this pest is generally undertaken through regular field surveys, which is labour intensive, time consuming and error prone. Alternately, radiometry is a reliable technique for rapid and non-destructive assessment of plant health. The purpose of this research was to develop a new method to detect infested cotton plant with PBW without any losses to boll. Thus, a study was conducted to characterize reflectance spectra of cotton plants with known PBW infestation, and seek to identify specific narrow wavelengths sensitive to PBW damage. Reflectance measurements were made in the spectral range of 350–2500 nm using a hyperspectral radiometer. Reflectance sensitivity analysis of the hyperspectral data to PBW damage also determined. Results of this study could suggest potential usage of remote sensing in monitoring spatial distribution of the PBW, and thereby enable effective planning and implementation of site-specific pest management practices. The study shows that it is feasible to detect PBW infestation using the hyperspectral data and recognize its level, which could be utilized to monitor trade and predictions.

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