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

作　　者：Jiarui Feng Shenghui Zhang Zhaoyu Zhai Hongfeng Yu Huanliang Xu 

机构地区：[1]College of Artificial Intelligence,Nanjing Agricultural University,Nanjing,210095,China [2]College of Engineering,Nanjing Agricultural University,Nanjing,210095,China

出　　处：《Plant Phenomics》2024年第2期377-389,共13页植物表型组学（英文）

基　　金：supported by the Natural Science Foundation of Jiangsu Province(Grant No.BK20231004);Guidance Foundation,the Sanya Institute of Nanjing Agricultural University(Grant No.NAUSY-MS25);Fundamental Research Funds for the Central Universities(Grant No.KYCXJC2023007).

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.

关 键 词：learning IMAGING detection RUST model DEEP ASIAN based HYPERSPECTRAL SOYBEAN 

分 类 号：S435.651[农业科学—农业昆虫与害虫防治]