Semantic Segmentation of Sorghum Using Hyperspectral Data Identifies Genetic Associations
This study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle). While many current methods for segmentation focus on separat...
Principais autores: | Chenyong Miao, Alejandro Pages, Zheng Xu, Eric Rodene, Jinliang Yang, James C. Schnable |
---|---|
Formato: | Artigo |
Idioma: | English |
Publicado em: |
American Association for the Advancement of Science (AAAS)
2020-01-01
|
coleção: | Plant Phenomics |
Acesso em linha: | http://dx.doi.org/10.34133/2020/4216373 |
Registros relacionados
-
3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves
por: Michael C. Tross, et al.
Publicado em: (2021-12-01) -
A UAV‐based high‐throughput phenotyping approach to assess time‐series nitrogen responses and identify trait‐associated genetic components in maize
por: Eric Rodene, et al.
Publicado em: (2022-01-01) -
Fully‐connected semantic segmentation of hyperspectral and LiDAR data
por: Hakan Aytaylan, et al.
Publicado em: (2019-04-01) -
FUSION OF LIDAR AND HYPERSPECTRAL DATA FOR SEMANTIC SEGMENTATION OF FOREST TREE SPECIES
por: E. Tusa, et al.
Publicado em: (2020-08-01) -
Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms
por: Eric Rodene, et al.
Publicado em: (2024-03-01)