ESTIMATION OF CHLOROPHYLL IN PISTACHIO TREES USING HYPERSPECTRAL DATA PRINT

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PANAHI D.; ESMAILI A.; DARVISHZADEH R.; NASERI F.

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Journal: JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY Year:2014 | Volume:4 | Issue:2 Page(s): 167-177

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Title

ESTIMATION OF CHLOROPHYLL IN PISTACHIO TREES USING HYPERSPECTRAL DATA PRINT

Author(s)

PANAHI D. | ESMAILI A. | DARVISHZADEH R. | NASERI F. | Issue Writer Certificate

Keywords

HYPERSPECTRALQ2

PISTACHIO LEAVESQ2

CHLOROPHYLLQ2

PARTIAL LEAST SQUARES REGRESSIONQ2

STEPWISE MULTIPLE LINEAR REGRESSIONQ2

NARROW BAND VEGETATION INDEXQ2

Abstract

Accurate quantitative estimation of vegetation biochemical and biophysical characteristics is necessary for a large variety of agricultural, ecological and meteorological applications. Among agricultural products in Iran, strategic and economical importance of Pistachio highlights the essential planning for improvement and increase of its production. The aim of this study is to compare the utility of statistical multivariate calibration techniques, including STEPWISE MULTIPLE LINEAR REGRESSION (SMLR) and PARTIAL LEAST SQUARES REGRESSION (PLSR) with univariate techniques such as narrow band vegetation indices using HYPERSPECTRAL data for estimating CHLOROPHYLL content of Pistachio trees. PISTACHIO LEAVES were collected in different growth stage. Spectral and chemical measurements were obtained from the collected samples at the laboratory, using ASD Field Spectrometer III, SPAD measurements and wet chemical analysis. Narrow band indices derived from all possible two-band combinations of reflectance and first derivative data were assessed and the best band combinations with the highest R2 values were selected and used in linear regressions to predict the studied parameters. Among studied indices, the narrow band RVI index with wavelengths 670 nm and 734 nm using first derivative were recognized as the best index for predicting CHLOROPHYLL (R2cv=0.72, RRMSEcv=0.25). The results of multivariate analysis showed that PLSR and SMLR techniques using first derivative data are better than narrow band indices in CHLOROPHYLL prediction, respectively (R2cv=0.79 and RRMSEcv=0.21). In a nutshell this study showed that multivariate calibration techniques increase the accuracy of predicting CHLOROPHYLL content in PISTACHIO LEAVES comparing to univariate techniques. Also first derivative transformation would increase the accuracy of predicted parameters compared to reflectance values. The results highlight the benefits of using HYPERSPECTRAL measurements in assessing the biochemical parameters of Pistachios trees and therefore are recommended for analysis of health and growth status of agricultural products.

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APA: Copy

PANAHI, D., ESMAILI, A., DARVISHZADEH, R., & NASERI, F.. (2014). ESTIMATION OF CHLOROPHYLL IN PISTACHIO TREES USING HYPERSPECTRAL DATA PRINT. JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY, 4(2), 167-177. SID. https://sid.ir/paper/249270/en

Vancouver: Copy

PANAHI D., ESMAILI A., DARVISHZADEH R., NASERI F.. ESTIMATION OF CHLOROPHYLL IN PISTACHIO TREES USING HYPERSPECTRAL DATA PRINT. JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY[Internet]. 2014;4(2):167-177. Available from: https://sid.ir/paper/249270/en

IEEE: Copy

D. PANAHI, A. ESMAILI, R. DARVISHZADEH, and F. NASERI, “ESTIMATION OF CHLOROPHYLL IN PISTACHIO TREES USING HYPERSPECTRAL DATA PRINT,” JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY, vol. 4, no. 2, pp. 167–177, 2014, [Online]. Available: https://sid.ir/paper/249270/en

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