Detection of Lesions in Lettuce Caused by Pectobacterium carotovorum Subsp. carotovorum by Supervised Classification Using Multispectral Images

This study aimed to detect soft rot caused by Pectobacterium carotovorum subsp. carotovorum in 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. carotovorum subsp. carotovorum in 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.

Automated summary

This study successfully detected soft rot in lettuce canopy at the pre-symptomatic stage using multispectral sensors, with the best supervised classification results achieved at 4 and 8 days after inoculation, especially with subsets derived from the Mapir Survey3W camera's RGN sensor.