Spectral Signature for Detecting Pest Infestation of Some Cultivated Plants in the Northern West Coast of Egypt. | ||
| Egyptian Academic Journal of Biological Sciences. A, Entomology | ||
| Article 6, Volume 12, Issue 3, June 2019, Pages 73-83 PDF (1.59 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/eajbsa.2019.33963 | ||
| Authors | ||
| Yones M. S.1; Aboelghar M.1; Khdery G. A.1; Dahi H. F.2; Sowilem M.1 | ||
| 1National Authority for Remote sensing and Space Sciences (NARSS), 23, Josef Proztito St. Elnozha Elgedida - P.O. Box 1564 Alf maskan Cairo, Egypt | ||
| 2Plant Protection Research Institute, (ARC), Giza, Egypt | ||
| Abstract | ||
| Early detection of pest infestation is essential for determining the optimal time for control application. Many studies indicated the effectiveness of remote sensing technology as a tool to identify plants stressed by pest infestation. In the current study, we observed using hyper spectral remotely sensed data for discrimination between healthy and pest infested plants. Three arid-land plants were selected in the current study: sweet almond, citrus lemon trees and olives. As the first step of the analysis, spectral reflectance pattern for the three plants healthy and infected was identified. The optimal waveband and wavelength/s to differentiate between healthy and infected plants were identified. Different vegetation indices: Modified Chlorophyll Absorption in Reflectance Index (MCARI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), and Normalized Pigment Chlorophyll Index (NPCI) were calculated and compared between the values of these indices under infestation stress was examined. The results showed that healthy plants give higher reflectance values in visible spectral bands than infected plants with olives, however, healthy plants showed higher reflectance than infected plants throughout the whole spectrum with the other two plants. Vegetation indices values showed less value with healthy plants with sweet almond and citrus lemon but an opposite trend was found with olives. Blue and red spectral zones were optimal to differentiate between healthy and infected sweet almond and citrus lemon trees when all spectral zones except SWIRI were effective to differentiate between healthy and infected olives. | ||
| Keywords | ||
| Remote Sensing; hyperspectral data; Monitoring; predictions | ||
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