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Identification of rice disease under complex background based on PSOC-DRCNet

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成果类型:
期刊论文
作者:
Liu, Zewei;Zhou, Guoxiong;Zhu, Wenke;Chai, Yi;Li, Liujun;...
通讯作者:
Zhou, GX
作者机构:
[Dai, Weisi; Sun, Lixiang; Zhou, Guoxiong; Liu, Zewei] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.
[Zhu, Wenke] Cent South Univ Forestry & Technol, Coll Bangor, Changsha 410004, Hunan, Peoples R China.
[Chai, Yi] Chongqing Univ Technol, Coll Mech Engn, Chongqing 401135, Peoples R China.
[Li, Liujun] Univ Idaho, Dept Soil & Water Syst, Moscow, ID 83844 USA.
[Wang, Yanfeng] Natl Univ Def Technol, Changsha, Hunan, Peoples R China.
通讯机构:
[Zhou, GX ] C
Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.
语种:
英文
关键词:
Rice disease identification;PSOC-DRCNet;DMA;RAB;CerLoss;PSOC
期刊:
Expert Systems with Applications
ISSN:
0957-4174
年:
2024
卷:
249
页码:
123643
基金类别:
National Natural Science Foundation of China [61902436]; Hunan Key Labo-ratory of Intelligent Logistics Technology [2019TP1015]
机构署名:
本校为第一且通讯机构
院系归属:
计算机与信息工程学院
摘要:
Rice is a crucial agricultural crop, yet it frequently suffers from various diseases, leading to decreased yields and, in severe cases, crop failure. Diseases significantly affect rice growth and yield, resulting in economic losses and food security challenges. The role of image recognition in identifying rice diseases is critical in agricultural production. It enables automated and efficient detection of rice diseases, which is essential for effective management, ensuring food security and sustainable agriculture. To address issues like background noise and edge blurring in rice disease image...

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