Resumen
The cultivation of kimchi cabbage in South Korea has always faced significant challenges due to the looming presence of Alternaria leaf spot (ALS), which is a fungal disease mainly caused by Alternaria alternata. The emergence of black spots resulting from Alternaria infection lowers the quality of the plant, rendering it inedible and unmarketable. The timely identification of this disease is crucial, as it provides essential data enabling swift intervention, thereby localizing the infection throughout the field. Hyperspectral imaging technologies excel in detecting subtle shifts in reflectance values induced by chemical differences within leaf tissues. However, research on the spectral correlation between Alternaria and kimchi cabbage remains relatively scarce. Therefore, this study aims to identify the spectral signature of Alternaria infection on kimchi cabbage and develop an automatic classifier for detecting Alternaria disease symptoms. Alternaria alternata was inoculated on various sizes of kimchi cabbage leaves and observed daily using a hyperspectral imaging system. Datasets were created based on captured hyperspectral images to train four classifier models, including support vector machine (SVM), random forest (RF), one-dimensional convolutional neural network (1D-CNN), and one-dimensional residual network (1D-ResNet). The results suggest that 1D-ResNet outperforms the other models with an overall accuracy of 0.91, whereas SVM, RF, and 1D-CNN achieved 0.80, 0.88, and 0.86, respectively. This study may lay the foundation for future research on high-throughput disease detection, frequently incorporating drones and aerial imagery.