SPECTRAL DISCRIMINATION OF IMPORTANT ORCHARD SPECIES USING HYPERSPECTRAL INDICES AND ARTIFICIAL INTELLIGENCE APPROACHES

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

MIRZAEI M.; ABBASI M.; MAROFI S.; SOLGI E.; KARIMI R.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of RS and GIS for Natural Resources Year:2018 | Volume:9 | Issue:2 (31) Page(s): 76-92

Download Full-Text

Persian Verion

View:

1,030

Download:

Cites:

Information Journal Paper

Title

SPECTRAL DISCRIMINATION OF IMPORTANT ORCHARD SPECIES USING HYPERSPECTRAL INDICES AND ARTIFICIAL INTELLIGENCE APPROACHES

Author(s)

Keywords

FIELD SPECTROSCOPYQ1

PLANT SPECIES DISCRIMINATIONQ1

SPECTRAL INDICESQ1

ARTIFICIAL NEURAL NETWORK (ANN)Q2

SUPPORT VECTOR MACHINE (SVM)Q1

Abstract

Study spectral reflectance through SPECTRAL INDICES allows the optimal use of the wide range of spectral wavelengths in hyperspectral data. The purpose of this study was to introduce and evaluate the performance of SPECTRAL INDICES to discriminate dominant orchard species in Chaharmahal Bakhtiari province. In this study, 150 spectral curves were measured in the range of 350 to 2500 mm, from grapes, walnuts and almond trees.After the initial correction, 30 of the most important SPECTRAL INDICES were extracted.Analysis of variance and comparisons of meanings was applied to identify the optimal indices for species discrimination at a 99% confidence level. Then, an artificial neural network (ANN) and support vector machine (SVM) approaches were used to evaluate the performance of indices in species discrimination. ANOVA results indicated that the Moisture Stress Index (MSI), Band ratio at 1, 200 nm, normalized phaepophytiniz index (NPQI) and cellulose absorption index (CAI) indices are optimal for discrimination of the studied species. The performance evaluation of the introduced indicators in some of the ANN and SVM enhancement structures has been associated with 100% accuracy in both education and testing, which shows the effectiveness of these studies in distinguishing orchard species. The performance evaluation of the introduced indicators has been validated at 100% in both training and testing stages. This result emphasizes the necessity of performing spectroscopic studies to separate the orchard species beforeanalyzing the hyperspectral images due to their large data volume, high cost and huge data analysis.

Cites

No record.

References

No record.

Cite

APA: Copy

MIRZAEI, M., ABBASI, M., MAROFI, S., SOLGI, E., & KARIMI, R.. (2018). SPECTRAL DISCRIMINATION OF IMPORTANT ORCHARD SPECIES USING HYPERSPECTRAL INDICES AND ARTIFICIAL INTELLIGENCE APPROACHES. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), 9(2 (31) ), 76-92. SID. https://sid.ir/paper/189427/en

Vancouver: Copy

MIRZAEI M., ABBASI M., MAROFI S., SOLGI E., KARIMI R.. SPECTRAL DISCRIMINATION OF IMPORTANT ORCHARD SPECIES USING HYPERSPECTRAL INDICES AND ARTIFICIAL INTELLIGENCE APPROACHES. JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE)[Internet]. 2018;9(2 (31) ):76-92. Available from: https://sid.ir/paper/189427/en

IEEE: Copy

M. MIRZAEI, M. ABBASI, S. MAROFI, E. SOLGI, and R. KARIMI, “SPECTRAL DISCRIMINATION OF IMPORTANT ORCHARD SPECIES USING HYPERSPECTRAL INDICES AND ARTIFICIAL INTELLIGENCE APPROACHES,” JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE), vol. 9, no. 2 (31) , pp. 76–92, 2018, [Online]. Available: https://sid.ir/paper/189427/en

Related Journal Papers