2023
DOI: 10.3389/frai.2023.1203546
|View full text |Cite
|
Sign up to set email alerts
|

Explainable deep learning in plant phenotyping

Sakib Mostafa,
Debajyoti Mondal,
Karim Panjvani
et al.

Abstract: The increasing human population and variable weather conditions, due to climate change, pose a threat to the world's food security. To improve global food security, we need to provide breeders with tools to develop crop cultivars that are more resilient to extreme weather conditions and provide growers with tools to more effectively manage biotic and abiotic stresses in their crops. Plant phenotyping, the measurement of a plant's structural and functional characteristics, has the potential to inform, improve a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

1
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
5
1
1

Relationship

0
7

Authors

Journals

citations
Cited by 10 publications
(2 citation statements)
references
References 192 publications
1
1
0
Order By: Relevance
“…Notably, VGG-16 exhibited the most reliable localisation of affected regions based on its heatmap quality. Similar to the finding in [51], these visual explanations enhanced our understanding of how models identify nutrient deficiencies by pinpointing the highlighted areas.…”
Section: Discussionsupporting
confidence: 58%
“…Notably, VGG-16 exhibited the most reliable localisation of affected regions based on its heatmap quality. Similar to the finding in [51], these visual explanations enhanced our understanding of how models identify nutrient deficiencies by pinpointing the highlighted areas.…”
Section: Discussionsupporting
confidence: 58%
“…The adoption of deep learning has drastically improved the accuracy of root segmentation [ 16 , 21 25 ]. Despite these advances, two issues remain: (a) annotation for segmentation is intrinsically laborious as pixels must be carefully annotated and (b) downstream skeletonization and instance segmentation have a very low tolerance to errors.…”
Section: Introductionmentioning
confidence: 99%