Identification of tea foliar diseases and pest damage under practical field conditions using a convolutional neural network-Reference-Cited by-同舟云学术

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Identification of tea foliar diseases and pest damage under practical field conditions using a convolutional neural network

Published:2020-08-18 Issue:9 Volume:69 Page:1731-1739
ISSN:0032-0862
Container-title:Plant Pathology
language:en
Short-container-title:Plant Pathol.

Author:

Lee Sheng‐Hung¹,Lin Shiou‐Ruei²,Chen Shih‐Fang¹^ORCID

Affiliation:

1. Department of Biomechatronics Engineering National Taiwan University Taipei Taiwan

2. Tea Research and Extension Station Council of Agriculture Taoyuan Taiwan

Funder

Council of Agriculture

Publisher

Wiley

Subject

Horticulture,Plant Science,Genetics,Agronomy and Crop Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ppa.13251

Reference30 articles.

1. Deng J. Dong W. Socher R. Li L.J. Li K.andLi F.F.(2009)ImageNet: a large‐scale hierarchical image database. In:IEEE Conference on Computer Vision and Pattern Recognition.Institute of Electrical and Electronics Engineers:Piscataway USA pp.248–255.

2. Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA)

3. The Pascal Visual Object Classes (VOC) Challenge

4. Deep learning models for plant disease detection and diagnosis

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