DC2Net:An Asian Soybean Rust Detection Model Based on Hyperspectral Imaging and Deep Learning

二维码有效期 120s

摘要Asian soybean rust (ASR) is one of the major diseases that causes serious yield loss worldwide,even up to 80％.Early and accurate detection of ASR is critical to reduce economic losses.Hyperspectral imaging,combined with deep learning,has already been proved as a powerful tool to detect crop diseases.However,current deep learning models are limited to extract both spatial and spectral features in hyperspectral images due to the use of fixed geometric structure of the convolutional kernels,leading to the fact that the detection accuracy of current models remains further improvement.

作者 Jiarui Feng ^[1] Shenghui Zhang ^[2] Zhaoyu Zhai ^[2] Hongfeng Yu ^[3] Huanliang Xu ^[2] 学术成果认领

作者单位 College of Artificial Intelligence,Nanjing Agricultural University,Nanjing,210095,China;College of Engineering,Nanjing Agricultural University,Nanjing,210095,China ^[1] College of Artificial Intelligence,Nanjing Agricultural University,Nanjing,210095,China ^[2] College of Engineering,Nanjing Agricultural University,Nanjing,210095,China ^[3]

栏目名称 RESEARCH ARTICLES

DOI 10.34133/plantphenomics.0163

发布时间 2024-12-11

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