Leaf area index estimated by direct, semi-direct, and indirect methods in European beech and sycamore maple stands

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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Litterfall production and leaf area index in a virgin European beech (Fagus sylvatica L.) – Silver fir (Abies alba Mill.) forest

Dendrobiology ◽

10.12657/denbio.083.008 ◽

2020 ◽

Vol 83 ◽

pp. 75-84 ◽

Cited By ~ 1

Author(s):

Ion Catalin Petritan ◽

Victor-Vasile Mihăilă ◽

Cosmin Ion Bragă ◽

Marlène Boura ◽

Diana Vasile ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Fagus Sylvatica ◽

Abies Alba ◽

European Beech ◽

Silver Fir ◽

Area Index ◽

Litterfall Production ◽

Fagus Sylvatica L

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

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2020.108101 ◽

2020 ◽

Vol 292-293 ◽

pp. 108101 ◽

Cited By ~ 1

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

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

Functional Plant Biology ◽

10.1071/pp9940197 ◽

1994 ◽

Vol 21 (2) ◽

pp. 197 ◽

Cited By ~ 22

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.

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