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Machine Learning-Based Spectral Library for Crop Classification and Status Monitoring
- Source :
- Agronomy, Vol 9, Iss 9, p 496 (2019), Agronomy, Volume 9, Issue 9
- Publication Year :
- 2019
- Publisher :
- MDPI AG, 2019.
-
Abstract
- The establishment and application of a spectral library is a critical step in the standardization and automation of remote sensing interpretation and mapping. Currently, most spectral libraries are designed to support the classification of land cover types, whereas few are dedicated to agricultural remote sensing monitoring. Here, we gathered spectral observation data on plants in multiple experimental scenarios into a spectral database to investigate methods for crop classification (16 crop species) and status monitoring (tea plant and rice growth). We proposed a set of screening methods for spectral features related to plant classification and status monitoring (band reflectance, vegetation index, spectral differentiation, spectral continuum characteristics) that are based on ISODATA and JM distance. Next, we investigated the performance of different machine learning classifiers in the spectral library application, including K-nearest neighbor (KNN), Random Forest (RF), and a genetic algorithm coupled with a support vector machine (GA-SVM). The optimal combination of spectral features and the classifier with the highest classification accuracy were selected for crop classification and status monitoring scenarios. The GA-SVM classifier performed the best, which produced an accuracy of OAA = 0.94, Kappa = 0.93 for crop classification in a complex scenario (crops mixed with 71 non-crop plant species), and promising accuracies for tea plant growth monitoring (OAA = 0.98, Kappa = 0.97) and rice growth stage monitoring (OAA = 0.92, Kappa = 0.90). Therefore, the establishment of a plant spectral library combined with relevant feature extraction and a classification algorithm effectively supports agricultural monitoring by remote sensing.
- Subjects :
- 010504 meteorology & atmospheric sciences
Computer science
0211 other engineering and technologies
02 engineering and technology
Land cover
Machine learning
computer.software_genre
01 natural sciences
Multispectral pattern recognition
Crop
lcsh:Agriculture
spectral library
021101 geological & geomatics engineering
0105 earth and related environmental sciences
business.industry
crop classification
lcsh:S
Hyperspectral imaging
Automation
status monitoring
Random forest
Support vector machine
hyperspectral
machine learning
Artificial intelligence
business
Agronomy and Crop Science
computer
Classifier (UML)
Subjects
Details
- Language :
- English
- ISSN :
- 20734395
- Volume :
- 9
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- Agronomy
- Accession number :
- edsair.doi.dedup.....5dcd4d29da63596d62cb65fe48e815b2