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13 June 2023 Early detection of Chinese cabbage downy mildew disease on aerial hyperspectral images using deep learning (Conference Presentation)
Xiongzhe Han, Lukas Wiku Kuswidiyanto, Hee-Young Jung, Hyun Ho Noh, Yongho Shin
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Proceedings Volume PC12539, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII; PC1253904 (2023) https://doi.org/10.1117/12.2663725
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States
Abstract
As one of the primary agricultural commodities in Korea, Chinese cabbage is susceptible to disease infections. The plants which exposed to a high moisture are easily infected by downy mildew disease. The disease is identified by irregular yellow-tan spots appearing on the upper leaf surface, leading to cell damage thus degrading the product quality. An early detection system to identify and treat the disease would be essential to prevent disease occurrence and reduce the plant damage caused by the disease. Hyperspectral imaging, as one of the non-destructive evaluation methods, has recently become more popular due to its capability to capture a wide range of light spectrum. It is sensitive enough to detect slight chemical difference within the plant. UAV-based hyperspectral system offers high-throughput plant phenotyping with abundant resources of data. A preliminary experiment has shown spectral differences between diseased and healthy cabbage leaves. Based on hyperspectral image data, the detection system employs a convolutional neural network (CNN) that extracts spectral and spatial features to detect the disease and its location. A 3D CNN architecture will be used in this study to further exploit the spectral variance and accurately detect the disease.
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Xiongzhe Han, Lukas Wiku Kuswidiyanto, Hee-Young Jung, Hyun Ho Noh, and Yongho Shin "Early detection of Chinese cabbage downy mildew disease on aerial hyperspectral images using deep learning (Conference Presentation)", Proc. SPIE PC12539, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII, PC1253904 (13 June 2023); https://doi.org/10.1117/12.2663725
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KEYWORDS
Hyperspectral imaging
Agriculture
Convolutional neural networks
Hyperspectral systems
Nondestructive evaluation
System identification
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