Plant diseases cause considerable economic loss in the global agricultural industry. A current challenge in the agricultural industry is the development of reliable methods for detecting plant diseases and plant stress. Existing disease detection methods mainly involve manually and visually assessing crops for visible disease indicators. The rapid development of unmanned aerial vehicles (UAVs) and hyperspectral imaging technology has created a vast potential for plant disease detection. UAV-borne hyperspectral remote sensing (HRS) systems with high spectral, spatial, and temporal resolutions have replaced conventional manual inspection methods because they allow for more accurate cost-effective crop analyses and vegetation characteristics. This paper aims to provide an overview of the literature on HRS for disease detection based on deep learning algorithms. Prior articles were collected using the keywords “hyperspectral”, “deep learning”, “UAV”, and “plant disease”. This paper presents basic knowledge of hyperspectral imaging, using UAVs for aerial surveys, and deep learning-based classifiers. Generalizations about workflow and methods were derived from existing studies to explore the feasibility of conducting such research. Results from existing studies demonstrate that deep learning models are more accurate than traditional machine learning algorithms. Finally, further challenges and limitations regarding this topic are addressed.

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.

Downy mildew poses a significant threat to kimchi cabbage, a vital agricultural product in Korea, adversely affecting its yield and quality. Traditional disease detection methods based on visual inspection are labor intensive and time consuming. This study proposes a non-destructive, field-scale disease detection approach using unmanned aerial vehicle (UAV)-based hyperspectral imaging. Hyperspectral images of the kimchi cabbage field were preprocessed, segmented at the pixel level, and classified into four categories: background, healthy, early-stage disease, and late-stage disease. Spectral analysis of the late and early stages of downy mildew infection revealed notable differences in the red-edge band, with infected plants exhibiting increased red-edge reflectance. To automate disease detection, various machine learning models, including Random Forest (RF), 1D Convolutional Neural Network (1D-CNN), 1D Residual Network (1D-ResNet), and 1D Inception Network (1D-InceptionNet), were developed. These models were trained based on a 0.2 sampling dataset, achieving overall accuracy scores of 0.907, 0.901, 0.909, and 0.914, along with F1 scores of 0.876, 0.845, 0.897, and 0.899, respectively. Overall, the results of this study revealed that the red-edge band reliably signaled the presence of downy mildew, and the 1D-InceptionNet model demonstrated the most effective performance for automatic disease detection.