Abstract
With the breakthrough of deep learning techniques, many leaf-based automated plant diagnosis methodologies have been proposed.To the best of our knowledge, most conventional methodologies only accept narrow range images, typically one or quite a limited number of targets are in their input.This is because the appearance of leaves is diverse and leaves usually heavily overlap each other in practical situations.In this paper, we propose a basic and practical end-to-end plant disease diagnosis system for wideangle images.Our system is principally composed of two specially designed types of convolutional neural networks.The system achieves leaf detection performance of 73.9% in F1score, overall (detection and diagnosis) performance of 68.1% in recall and 65.8% in precision at around 3 seconds/image on 500 wide-angle on-site images which have 6,860 healthy and 6,741 infected leaves (13,601 in total).
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The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 4
Claim · model-written summary
The system detected leaves in wide-angle on-site cucumber images with an F1 score of 73.9%.
- Taxon
- Cucumber
Cucumis sativust0 - Outcome
- leaf detection performancee0
- Value
- 73.9%e0
- Setting
- on-site imagese1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Cucumber Images”
e0“The system achieves leaf detection performance of 73.9% in F1score”
e1“on 500 wide-angle on-site images”
Claim · model-written summary
Overall detection and diagnosis recall was 68.1% on wide-angle on-site cucumber images.
- Taxon
- CucumberCucumis sativust0
- Outcome
- recalle0
- Value
- 68.1%e0
- Setting
- on-site imagese1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Cucumber Images”
e0“overall (detection and diagnosis) performance of 68.1% in recall”
e1“on 500 wide-angle on-site images”
Claim · model-written summary
Overall detection and diagnosis precision was 65.8% on wide-angle on-site cucumber images.
- Taxon
- CucumberCucumis sativust0
- Outcome
- precisione0
- Value
- 65.8%e0
- Setting
- on-site imagese1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Cucumber Images”
e0“65.8% in precision”
e1“on 500 wide-angle on-site images”
Claim · model-written summary
The detection and diagnosis system processed images at around 3 seconds per image.
- Taxon
- CucumberCucumis sativust0
- Outcome
- seconds/imagee0
- Value
- around 3 seconds/imagee0
- Setting
- on-site imagese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Cucumber Images”
e0“at around 3 seconds/image on 500 wide-angle on-site images”