Soybean leaf estimation based on RGB images and machine learning methods
Abstract Background RGB photographs are a powerful tool for dynamically estimating crop growth. Leaves are related to crop photosynthesis, transpiration, and nutrient uptake. Traditional blade parameter measurements were labor-intensive and time-consuming. Therefore, based on the phenotypic features...
Main Authors: | Xiuni Li, Xiangyao Xu, Shuai Xiang, Menggen Chen, Shuyuan He, Wenyan Wang, Mei Xu, Chunyan Liu, Liang Yu, Weiguo Liu, Wenyu Yang |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2023-06-01
|
Series: | Plant Methods |
Subjects: | |
Online Access: | https://doi.org/10.1186/s13007-023-01023-z |
Similar Items
-
Skewed distribution of leaf color RGB model and application of skewed parameters in leaf color description model
by: Zhengmeng Chen, et al.
Published: (2020-02-01) -
Modeling Dynamics of Leaf Color Based on RGB Value in Rice
by: Yong-hui ZHANG, et al.
Published: (2014-04-01) -
Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV
by: Predrag Ranđelović, et al.
Published: (2020-07-01) -
YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting
by: Shuai Xiang, et al.
Published: (2023-01-01) -
The field phenotyping platform's next darling: Dicotyledons
by: Xiuni Li, et al.
Published: (2022-08-01)