Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis
Yield prediction for crops is essential information for food security. A high-throughput phenotyping platform (HTPP) generates the data of the complete life cycle of a plant. However, the data are rarely used for yield prediction because of the lack of quality image analysis methods, yield data asso...
Enregistré dans:
Auteurs principaux: | Sungyul Chang, Unseok Lee, Min Jeong Hong, Yeong Deuk Jo, Jin-Baek Kim |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Frontiers Media S.A.
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/100486e6f4ed4036bd348f4e7ee2be2c |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation
par: Jonathon A. Gibbs, et autres
Publié: (2021) -
PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping
par: Lingbo Liu, et autres
Publié: (2021) -
Identification of High Nitrogen Use Efficiency Phenotype in Rice (Oryza sativa L.) Through Entire Growth Duration by Unmanned Aerial Vehicle Multispectral Imagery
par: Ting Liang, et autres
Publié: (2021) -
Study on the Micro-Phenotype of Different Types of Maize Kernels Based on Micro-CT
par: ZHAO Huan, et autres
Publié: (2021) -
Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data
par: Behrokh Nazeri, et autres
Publié: (2021)