1 January 2011 Oil palm pest infestation monitoring and evaluation by helicopter-mounted, low altitude remote sensing platform
Author Affiliations +
J. of Applied Remote Sensing, 5(1), 053540 (2011). doi:10.1117/1.3609843
Timely detection of pest or disease infections is extremely important for controlling the spread of disease and preventing crop productivity losses. A specifically designed radio-controlled helicopter mounted low altitude remote sensing (LARS) platform can offer near-real-time results upon user demand. The acquired LARS images were processed to estimate vegetative-indices and thereby detecting upper stem rot (Phellinus Noxius) disease in both young and mature oil palm plants. The indices helped discriminate healthy and infested plants by visualization, analysis and presentation of digital imagery software, which were validated with ground truth data. Good correlations and clear data clusters were obtained in characteristic plots of normalized difference vegetation index (NDVI)LARS and green normalized difference vegetation indexLARS against NDVISpectro and chlorophyll content, by which infested plants were discriminated from healthy plants in both young and mature crops. The chlorophyll content values (μmol m−2) showed notable differences among clusters for healthy young (972 to 1100), for infested young (253 to 400), for healthy mature (1210 to 1500), and for infested mature (440 to 550) oil palm. The correlation coefficients (R2) were in a reasonably acceptable range (0.62 to 0.88). The vegetation indices based on LARS images, provided satisfactory results when compared to other approaches. The developed technology showed promising scope for medium and large plantations.
Grianggai Samseemoung, Hemantha P.W. Jayasuriya, Peeyush Soni, "Oil palm pest infestation monitoring and evaluation by helicopter-mounted, low altitude remote sensing platform," Journal of Applied Remote Sensing 5(1), 053540 (1 January 2011). https://doi.org/10.1117/1.3609843



Digital photography

Near infrared

Image processing

Remote sensing


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