Peer review report 2 On “Comparison of direct and indirect methods for assessing leaf area index across a tropical rain forest landscape”

2015 ◽  
Vol 201 ◽  
pp. 168
Author(s):  
Anonymous
Keyword(s):  
Leaf Area Index ◽  
Peer Review ◽  
Leaf Area ◽  
Rain Forest ◽  
Tropical Rain Forest ◽  
Forest Landscape ◽  
Indirect Methods ◽  
Area Index ◽  
Direct And Indirect Methods

Comparison of direct and indirect methods for assessing leaf area index across a tropical rain forest landscape

2013 ◽  
Vol 177 ◽  
pp. 110-116 ◽  
Author(s):  
Paulo C. Olivas ◽  
Steven F. Oberbauer ◽  
David B. Clark ◽  
Deborah A. Clark ◽  
Michael G. Ryan ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Rain Forest ◽  
Tropical Rain Forest ◽  
Forest Landscape ◽  
Indirect Methods ◽  
Area Index ◽  
Direct And Indirect Methods

First direct landscape-scale measurement of tropical rain forest Leaf Area Index, a key driver of global primary productivity

2007 ◽  
Vol 0 (0) ◽  
pp. 071121035930001-??? ◽  
Author(s):  
David B. Clark ◽  
Paulo C. Olivas ◽  
Steven F. Oberbauer ◽  
Deborah A. Clark ◽  
Michael G. Ryan
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Primary Productivity ◽  
Rain Forest ◽  
Tropical Rain Forest ◽  
Landscape Scale ◽  
Area Index ◽  
Key Driver

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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