A low-resolution image restoration classifier network to identify stored-grain insects from images of sticky boards-Reference-Cited by-同舟云学术

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A low-resolution image restoration classifier network to identify stored-grain insects from images of sticky boards

Published:2019-07 Issue: Volume:162 Page:593-601
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Zhou Huiling,Miao Haiwei,Li Jiangtao,Jian Fuji^ORCID,Jayas Digvir S.

Funder

2015 China Special Fund for Grain-Scientific Research in Public Interest

Program of Introducing Talents of Discipline to Universities of China

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference18 articles.

1. Jointly optimized regressors for image super-resolution;Dai;Comput. Graphics Forum,2015

2. Auto-encoding variational bayes;Diederik;Statistics,2013

3. Generative adversarial nets;Goodfellow,2014

4. Deep residual learning for image recognition;He,2015

5. Simultaneous super-resolution and feature extraction for recognition of low-resolution faces;Hennings-Yeomans,2008

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1. Automatic detection method for tobacco beetles combining multi-scale global residual feature pyramid network and dual-path deformable attention;Scientific Reports;2024-02-28

2. Real-time stored product insect detection and identification using deep learning: System integration and extensibility to mobile platforms;Journal of Stored Products Research;2023-12

3. Development of an automatic pest monitoring system using a deep learning model of DPeNet;Measurement;2022-11

4. Evaluation of Effective Class-Balancing Techniques for CNN-Based Assessment of Aphanomyces Root Rot Resistance in Pea (Pisum sativum L.);Sensors;2022-09-24

5. A Method to Establish a Synthetic Image Dataset of Stored-Product Insects for Insect Detection;IEEE Access;2022

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