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Charcoal rot is a fungal disease that thrives in warm dry conditions andaffects the yield of soybeans and other important agronomic crops worldwide.There is a need for robust, automatic and consistent early detection andquantification of disease symptoms which are important in breeding programs forthe development of improved cultivars and in crop production for theimplementation of disease control measures for yield protection. Currentmethods of plant disease phenotyping are predominantly visual and hence areslow and prone to human error and variation. There has been increasing interestin hyperspectral imaging applications for early detection of disease symptoms.However, the high dimensionality of hyperspectral data makes it very importantto have an efficient analysis pipeline in place for the identification ofdisease so that effective crop management decisions can be made. The focus ofthis work is to determine the minimal number of most effective hyperspectralbands that can distinguish between healthy and diseased specimens early in thegrowing season. Healthy and diseased hyperspectral data cubes were captured at3, 6, 9, 12, and 15 days after inoculation. We utilized inoculated and controlspecimens from 4 different genotypes. Each hyperspectral image was captured at240 different wavelengths in the range of 383 to 1032 nm. We used a combinationof genetic algorithm as an optimizer and support vector machines as aclassifier for identification of maximally effective band combinations. Abinary classification between healthy and infected samples using six selectedband combinations obtained a classification accuracy of 97% and a F1 score of0.97 for the infected class. The results demonstrated that these carefullychosen bands are more informative than RGB images, and could be used in amultispectral camera for remote identification of charcoal rot infection insoybean.

Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean