Detection of Cotton Verticillium Wilt Disease Severity Based on Hyperspectrum and GWO-SVM
نویسندگان
چکیده
In order to address the challenge of early detection cotton verticillium wilt disease, naturally infected plants in field, which were divided into five categories based on degree disease severity, have been investigated this study. Canopies analyzed with spectral data measured, and various preprocessing techniques, including multiplicative scatter correction (MSC) MSC-continuous wavelet analysis algorithms, used predict severity. With a combination support vector machine (SVM) models such optimization algorithms as genetic algorithm (GA), grid search (GS), particle swarm (PSO), grey wolf optimizer (GWO), grading model was established The study results show that MSC-PSO-SVM outperforms other three terms classification accuracy, macro precision, recall, F1-score are 80%, 81.26%, 79.57%, respectively. Among those eight constructed basis continuous analyses using mexh db3, MSC-db3(23)-PSO-SVM MSC-db3(23)-GWO-SVM perform best, latter having shorter running time. An overall evaluation shows is an optimal model, values its indicators being 91.2%, 92.02%, 91.16%, Moreover, under prediction accuracy levels 1 5 has achieved highest rate 100%, 88% level 2 lowest 84% both 3 4. These demonstrate it effective use technology classifying satisfying needs field grading. This provides new approach for offered theoretical prevention, precise drug application, instrument development disease.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2023
ISSN: ['2072-4292']
DOI: https://doi.org/10.3390/rs15133373