<scp>AngleCam</scp> : Predicting the temporal variation of leaf angle distributions from image series with deep learning-Reference-Cited by-同舟云学术

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AngleCam : Predicting the temporal variation of leaf angle distributions from image series with deep learning

Published:2022-10-06 Issue:11 Volume:13 Page:2531-2545
ISSN:2041-210X
Container-title:Methods in Ecology and Evolution
language:en
Short-container-title:Methods Ecol Evol

Author:

Kattenborn Teja¹²^ORCID,Richter Ronny²³^ORCID,Guimarães‐Steinicke Claudia¹²^ORCID,Feilhauer Hannes¹²^ORCID,Wirth Christian²³^ORCID

Affiliation:

1. Remote Sensing Centre for Earth System Research (RSC4Earth) Leipzig University Leipzig Germany

2. German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Germany

3. Systematic Botany and Functional Biodiversity Institute of Biology Leipzig University Leipzig Germany

Publisher

Wiley

Subject

Ecological Modeling,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13968

Reference82 articles.

1. PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits

2. Albawi S. Mohammed T. A. &Al‐Zawi S.(2017).Understanding of a convolutional neural network.2017 International Conference on Engineering and Technology (ICET) 1–6.

3. Circadian, Carbon, and Light Control of Expansion Growth and Leaf Movement

4. Rapid measurement of the three-dimensional distribution of leaf orientation and the leaf angle probability density function using terrestrial LiDAR scanning

5. Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

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