| Autor: |
Carmo, Glecia Júnia dos Santos, Castoldi, Renata, Martins, George Deroco, Jacinto, Ana Carolina Pires, Tebaldi, Nilvanira Donizete, Charlo, Hamilton César de Oliveira, Zampiroli, Renan |
| Zdroj: |
Canadian Journal of Remote Sensing; March 2022, Vol. 48 Issue: 2 p144-157, 14p |
| Abstrakt: |
AbstractThis study aimed to detect soft rot caused by Pectobacterium carotovorumsubsp. carotovorumin lettuce using images obtained by multispectral sensors mounted on an unmanned aerial vehicle (UAV). A secondary objective was to identify the best sensor and determine the optimal stage after inoculation to detect infected plants. In the field, soft rot lesions and the agronomic traits of lettuce plants inoculated or not with the bacteria were assessed on different days after inoculation (DAI). Classifications were made using the Support Vector Machine (SVM) and Naive Bayes (NB) algorithms to analyze data groups consisting of spectral bands, vegetation indices and a combination of bands and indices obtained from a conventional visible camera and Mapir Survey3W multispectral camera, as well as agronomic parameters. The results confirmed the possibility of pre-symptomatic detection of P. carotovorumsubsp. carotovorumin lettuce at the canopy level. With respect to identifying healthy and infected lettuce plants by supervised classification, the best results were obtained at 4 and 8 DAI, especially when using the subsets derived from the Mapir Survey3W camera (RGN sensor), for both classifiers. The subsets obtained with the conventional visible sensor (RGB sensor) produced the best results at 20 and 24 DAI. |
| Databáze: |
Supplemental Index |
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