An assessment study of three indirect methods for estimating leaf area density and leaf area index of individual trees

2020 ◽  
Vol 292-293 ◽  
pp. 108101 ◽  
Author(s):  
Shanshan Wei ◽  
Tiangang Yin ◽  
Maria Angela Dissegna ◽  
Andrew J. Whittle ◽  
Genevieve Lai Fern Ow ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Indirect Methods ◽  
Area Index ◽  
Area Density ◽  
Leaf Area Density ◽  
Assessment Study ◽  
Individual Trees

scholarly journals Estimation of maize plant height and leaf area index dynamics using an unmanned aerial vehicle with oblique and nadir photography

2020 ◽  
Vol 126 (4) ◽  
pp. 765-773 ◽  
Author(s):  
Yingpu Che ◽  
Qing Wang ◽  
Ziwen Xie ◽  
Long Zhou ◽  
Shuangwei Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Height ◽  
Large Scale ◽  
High Efficiency ◽  
Point Clouds ◽  
Phenotypic Traits ◽  
Area Index ◽  
Area Density ◽  
Leaf Area Density

Abstract Background and Aims High-throughput phenotyping is a limitation in plant genetics and breeding due to large-scale experiments in the field. Unmanned aerial vehicles (UAVs) can help to extract plant phenotypic traits rapidly and non-destructively with high efficiency. The general aim of this study is to estimate the dynamic plant height and leaf area index (LAI) by nadir and oblique photography with a UAV, and to compare the integrity of the established three-dimensional (3-D) canopy by these two methods. Methods Images were captured by a high-resolution digital RGB camera mounted on a UAV at five stages with nadir and oblique photography, and processed by Agisoft Metashape to generate point clouds, orthomosaic maps and digital surface models. Individual plots were segmented according to their positions in the experimental design layout. The plant height of each inbred line was calculated automatically by a reference ground method. The LAI was calculated by the 3-D voxel method. The reconstructed canopy was sliced into different layers to compare leaf area density obtained from oblique and nadir photography. Key Results Good agreements were found for plant height between nadir photography, oblique photography and manual measurement during the whole growing season. The estimated LAI by oblique photography correlated better with measured LAI (slope = 0.87, R2 = 0.67), compared with that of nadir photography (slope = 0.74, R2 = 0.56). The total number of point clouds obtained by oblique photography was about 2.7–3.1 times than those by nadir photography. Leaf area density calculated by nadir photography was much less than that obtained by oblique photography, especially near the plant base. Conclusions Plant height and LAI can be extracted automatically and efficiently by both photography methods. Oblique photography can provide intensive point clouds and relatively complete canopy information at low cost. The reconstructed 3-D profile of the plant canopy can be easily recognized by oblique photography.

scholarly journals Ground-based estimation of leaf area index and vertical distribution of leaf area density in a Betula ermanii forest

Silva Fennica ◽  
2009 ◽  
Vol 43 (5) ◽  
Author(s):  
Akihiro Sumida ◽  
Taro Nakai ◽  
Masahito Yamada ◽  
Kiyomi Ono ◽  
Shigeru Uemura ◽  
...  
Keyword(s):  
Vertical Distribution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Area Density ◽  
Betula Ermanii ◽  
Leaf Area Density ◽  
Betula Ermanii Forest

scholarly journals A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 175 ◽  
Author(s):  
Orly Enrique Apolo-Apolo ◽  
Manuel Pérez-Ruiz ◽  
Jorge Martínez-Guanter ◽  
Gregorio Egea
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
High Throughput ◽  
Model Performance ◽  
Wheat Breeding ◽  
Plant Phenotyping ◽  
Indirect Methods ◽  
Area Index ◽  
Model Based ◽  
Recent Emergence

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model.

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