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Open AccessArticle

Finite Difference Analysis and Bivariate Correlation of Hyperspectral Data for Detecting Laurel Wilt Disease and Nutritional Deficiency in Avocado

1
Bioengineering Department, Florida Gulf Coast University, 10501 FGCU Blvd., Fort Myers, FL 33965, USA
2
Physics Department, Ave Maria University, 5050 Ave Maria Blvd., Ave Maria, FL 34142, USA
3
Agricultural and Biological Engineering Department, Southwest Florida Research and Education Center, University of Florida, 2685 FL-29, Immokalee, FL 34142, USA
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(15), 1748; https://doi.org/10.3390/rs11151748
Received: 31 May 2019 / Revised: 19 July 2019 / Accepted: 23 July 2019 / Published: 25 July 2019
(This article belongs to the Special Issue Remote Sensing for Sustainable Agriculture and Smart Farming)
Laurel wilt (Lw) is a very destructive disease and poses a serious threat to the commercial production of avocado in Florida, USA. External symptoms of Lw are similar to those that are caused by other diseases and disorders. A rapid technique to distinguish Lw infected avocado from healthy trees and trees with other abiotic stressors is presented in this paper. A novel method was developed to analyze data from hyperspectral data using finite difference approximation (FDA) and bivariate correlation (BC) to discriminate Lw, Nitrogen (N), and Iron (Fe) deficiencies from healthy avocado plants. Several combinatorial methods were used in preprocessing the data, such as standard normal transformation of data, smoothing of the data, and polynomial fit. The FDA technique was derived using a Taylor Polynomial finite difference approximation. This FDA accentuates inflection points in the spectrum. These, in turn, reveal variance in the data that can be used to identify spectral signature associated with healthy and diseased states. By statistical correlation using the bivariate correlation coefficient of these enhanced spectral patterns, an algorithm (FDA-BC) for distinguishing Lw avocado leaves from all other categories of healthy or mineral deficient avocado leaves is achieved with an overall accuracy of 100%. View Full-Text
Keywords: Laurel wilt; hyperspectral data analysis; bivariate correlation; spectral signature; disease detection Laurel wilt; hyperspectral data analysis; bivariate correlation; spectral signature; disease detection
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MDPI and ACS Style

Hariharan, J.; Fuller, J.; Ampatzidis, Y.; Abdulridha, J.; Lerwill, A. Finite Difference Analysis and Bivariate Correlation of Hyperspectral Data for Detecting Laurel Wilt Disease and Nutritional Deficiency in Avocado. Remote Sens. 2019, 11, 1748. https://doi.org/10.3390/rs11151748

AMA Style

Hariharan J, Fuller J, Ampatzidis Y, Abdulridha J, Lerwill A. Finite Difference Analysis and Bivariate Correlation of Hyperspectral Data for Detecting Laurel Wilt Disease and Nutritional Deficiency in Avocado. Remote Sensing. 2019; 11(15):1748. https://doi.org/10.3390/rs11151748

Chicago/Turabian Style

Hariharan, Jeanette; Fuller, John; Ampatzidis, Yiannis; Abdulridha, Jaafar; Lerwill, Andrew. 2019. "Finite Difference Analysis and Bivariate Correlation of Hyperspectral Data for Detecting Laurel Wilt Disease and Nutritional Deficiency in Avocado" Remote Sens. 11, no. 15: 1748. https://doi.org/10.3390/rs11151748

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