中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
合肥物质科学研究院 [5]
采集方式
OAI收割 [5]
内容类型
期刊论文 [5]
发表日期
2024 [1]
2022 [2]
2021 [1]
2019 [1]
学科主题
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Causality-inspired crop pest recognition based on Decoupled Feature Learning
期刊论文
OAI收割
PEST MANAGEMENT SCIENCE, 2024
作者:
Hu, Tao
;
Du, Jianming
;
Yan, Keyu
;
Dong, Wei
;
Zhang, Jie
  |  
收藏
  |  
浏览/下载:71/0
  |  
提交时间:2024/11/22
pest recognition
Decoupled Feature Learning
causal inference
deep learning
AM-ResNet: Low-energy-consumption addition-multiplication hybrid ResNet for pest recognition
期刊论文
OAI收割
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 卷号: 202
作者:
Zhang, Li
;
Du, Jianming
;
Dong, Shifeng
;
Wang, Fenmei
;
Xie, Chengjun
  |  
收藏
  |  
浏览/下载:38/0
  |  
提交时间:2022/12/23
Pest recognition
Convolutional neural networks
Addernet
Energy consumption
An effective pest detection method with automatic data augmentation strategy in the agricultural field
期刊论文
OAI收割
SIGNAL IMAGE AND VIDEO PROCESSING, 2022
作者:
Qian, Shaowei
;
Du, Jianming
;
Zhou, Jianan
;
Xie, Chengjun
;
Jiao, Lin
  |  
收藏
  |  
浏览/下载:26/0
  |  
提交时间:2022/12/23
Deep learning
Pest detection and recognition
Data augmentation
Computer vision
Deep Learning Based Automatic Multiclass Wild Pest Monitoring Approach Using Hybrid Global and Local Activated Features
期刊论文
OAI收割
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2021, 卷号: 17
作者:
Liu, Liu
;
Xie, Chengjun
;
Wang, Rujing
;
Yang, Po
;
Sudirman, Sud
  |  
收藏
  |  
浏览/下载:69/0
  |  
提交时间:2021/09/06
Feature extraction
Monitoring
Machine learning
Object detection
Agriculture
Proposals
Image recognition
Convolutional neural network (CNN)
global activated feature pyramid network
local activated region proposal network
pest monitoring
An Effective Data Augmentation Strategy for CNN-Based Pest Localization and Recognition in the Field
期刊论文
OAI收割
IEEE ACCESS, 2019, 卷号: 7
作者:
Li, Rui
;
Wang, Rujing
;
Zhang, Jie
;
Xie, Chengjun
;
Liu, Liu
  |  
收藏
  |  
浏览/下载:39/0
  |  
提交时间:2020/10/28
Pest localization
pest recognition
convolutional neural network
multi-scale
data augmentation