scholarly journals Improving the accuracy of indirect methods in estimating leaf area index using three correc-tion schemes in a Larix gmelinii plantation

2016 ◽  
Vol 40 (6) ◽  
pp. 574-584
Author(s):  
ZHOU Ming ◽  
◽  
LIU Zhi-Li ◽  
JIN Guang-Ze
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Larix Gmelinii ◽  
Indirect Methods ◽  
Area Index

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.

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

Comparative Analysis of Two Indirect Methods of Measuring Leaf Area Index as Applied to Minimal and Spur Pruned Grape Vines

1994 ◽  
Vol 21 (2) ◽  
pp. 197 ◽  
Author(s):  
KJ Sommer ◽  
ARG Lang
Keyword(s):  
Comparative Analysis ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Light Transmission ◽  
Accurate Estimate ◽  
Indirect Methods ◽  
Area Index ◽  
Leaf Sampling ◽  
Transmission Measurements

Leaf area index of spur and minimal pruned vines was measured directly by destructive leaf sampling and indirectly from light transmission measurements using the LAI-2000 and the DEMON instruments. Both instruments provided good estimates of plant and leaf area index. The LAI-2000 had a tendency to underestimate leaf area index. The DEMON instrument provided the most accurate estimate of plant and leaf area index. With both instruments it is important to validate indirect with direct estimates of vine leaf area.

scholarly journals An Empirical Protocol for Indirect Measurement of Leaf Area Index in Grape (Vitis vinifera L.)

HortScience ◽  
1993 ◽  
Vol 28 (8) ◽  
pp. 777-779 ◽  
Author(s):  
David A. Grantz ◽  
Larry E. Williams
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Canopy Structure ◽  
Indirect Measurement ◽  
Vitis Vinifera L ◽  
Indirect Methods ◽  
Area Index ◽  
Wide Row Spacing ◽  
Radiation Sensors

Leaf area development and canopy structure are important characteristics affecting yield and fruit quality of grapevines. Trellising systems and wide row spacing are common viticultural practices that violate key assumptions of currently available indirect methods of leaf area determination. We have developed a protocol for using a commercially available instrument to determine leaf area index (LAI) indirectly in a trellised vineyard. From knowledge of plant spacing, leaf area per vine can be calculated as required. A derived calibration equation resulted in a near 1:1 relationship (y = 0.00 + 1.00 X; r2 = 0.998) between actual and indirectly determined LAI over a range of LAI induced by irrigation treatments. The protocol involved covering 75% of the sensor with a manufacturer-supplied field of view delimiter and masking data from the outer three (of five) concentric radiation sensors. The protocol could form the basis for a general measurement technique, but may require local calibration.

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