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Related Concept Videos

Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&#8211;Environment Interactions
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Forest-Scale Phenotyping: Productivity Characterisation Through Machine Learning.

Maxime Bombrun1, Jonathan P Dash1, David Pont1

  • 1Forest Informatics, Scion, Rotorua, New Zealand.

Frontiers in Plant Science
|March 27, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a whole-of-forest phenotyping system using remote sensing and data science to model forest productivity. Genetics, environment, and site factors are key drivers, enabling better tree breeding and site selection for increased forest output.

Keywords:
GPU-accelerationLIDAR. forestrydecision treesgradient boostingphenotyping

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Area of Science:

  • Forestry science
  • Data science
  • Remote sensing

Background:

  • Improving plantation forest productivity is challenging due to complex biotic/abiotic interactions and long breeding cycles.
  • Understanding forest phenotypic expression influenced by environment and management is incomplete.
  • Sophisticated data analytics and remote sensing offer new approaches to model biological systems.

Purpose of the Study:

  • To develop and implement a whole-of-forest phenotyping system for landscape-level productivity estimation.
  • To identify key drivers of forest productivity using a data-driven approach.
  • To enhance tree breeding and genetic deployment strategies in plantation forestry.

Main Methods:

  • Collected 2.7 million observations across 62 variables (climate, management, genetics, terrain).
  • Utilized environmental surfaces, management records, and remotely sensed data.
  • Applied three machine learning methods to model forest productivity and interpret variable influence (Shapley values).

Main Results:

  • The most accurate model identified genetics, environmental conditions, leaf area index, topology, and soil properties as key productivity drivers.
  • Machine learning models successfully predicted forest productivity at a landscape level.
  • The study demonstrated the power of integrated remote sensing and data science for understanding forest dynamics.

Conclusions:

  • The developed phenotyping system enables landscape-level understanding of forest productivity.
  • This approach can identify superior/inferior genotypes and estimate site-specific productivity indices.
  • It facilitates improved tree breeding and genetic deployment for increased plantation forest productivity.