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Results of disease detection on apple images:

Chang Hee Han (10515788) ; Eal Kim (11604129) ; Tan Nhu Nhat Doan (11604132)...
https://figshare.com/articles/figure/Results_of_disease_detection_on_apple_images_/16868274. , 2021

Link: https://doi.org/10.1371/journal.pone.0258880.g004

RT Journal
T1 <Title>Results of disease detection on apple images</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_16868274&Exemplar=1&LAN=DE
				
A1 Chang Hee Han (10515788)
A1 Eal Kim (11604129)
A1 Tan Nhu Nhat Doan (11604132)
A1 Dongil Han (11604135)
A1 Seong Joon Yoo (11072028)
A1 Jin Tae Kwak (2123362)
YR 2021
K1 Neuroscience
K1 Biotechnology
K1 Cancer
K1 Information Systems not elsewhere classified
K1 infected apple fruits
K1 improving crop yield
K1 experimental results suggest
K1 proposed network outperformed
K1 aggregated attention cnn
K1 aggregated feature maps
K1 disease detection system
K1 xlink "> diseases
K1 xlink ">
K1 proposed network
K1 feature maps
K1 feature pyramids
K1 disease detection
K1 accurate detection
K1 yearly harvest
K1 thus holding
K1 stage framework
K1 resultant loss
K1 profound effect
K1 mask r
K1 improved design
K1 high accuracy
K1 fully exploit
K1 fold cross
K1 excitation block
K1 early diagnosis
K1 competing networks
K1 comparative experiments
K1 candidate regions
K1 also demonstrated
K1 additional level
K1 75 threshold
K1 5 threshold
JF https://figshare.com/articles/figure/Results_of_disease_detection_on_apple_images_/16868274
LK http://dx.doi.org/https://doi.org/10.1371/journal.pone.0258880.g004
DO https://doi.org/10.1371/journal.pone.0258880.g004
SF ELIB - SuUB Bremen

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