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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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Plant hormones—or phytohormones—are chemical molecules that modulate one or more physiological processes of a plant. In animals, hormones are often produced in specific glands and circulated via the circulatory system. However, plants lack hormone-producing glands.
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Related Experiment Video

Updated: Jan 16, 2026

Robotic Sensing and Stimuli Provision for Guided Plant Growth
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AgroLD: a knowledge graph for the plant sciences.

Larmande Pierre1,2, Pittolat Bertrand3,4, Tando Ndomassi5,4

  • 1DIADE, IRD, CIRAD, Univ. Montpellier, Ave Agropolis, Montpellier, France. pierre.larmande@ird.fr.

BMC Genomic Data
|October 3, 2025
PubMed
Summary
This summary is machine-generated.

AgroLD integrates diverse plant science data using Semantic Web technologies to improve crop yields. This knowledge graph aids researchers in understanding genotype-phenotype relationships for climate change adaptation and disease resistance.

Keywords:
BioinformaticsFAIRKnowledge graphsLinked dataPlant sciences

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Area of Science:

  • Plant Science
  • Bioinformatics
  • Genomics

Background:

  • Growing global food demand necessitates improved crop yields.
  • Climate change presents significant challenges to agriculture.
  • Understanding genotype-phenotype relationships is key to crop improvement.

Purpose of the Study:

  • To develop a unified knowledge platform for plant science data.
  • To facilitate hypothesis generation and validation in plant research.
  • To address complex biological questions related to plant adaptation and disease resistance.

Main Methods:

  • Utilized Semantic Web technologies to build a knowledge graph.
  • Integrated and annotated data from multiple diverse sources.
  • Developed AgroLD, a large-scale plant science knowledge graph.

Main Results:

  • AgroLD integrates approximately 1.08 billion triples.
  • Data from over 151 datasets across 19 sources are included.
  • The knowledge graph facilitates seamless data integration.

Conclusions:

  • AgroLD provides a specialized platform for plant science research.
  • It supports investigations into gene function in disease resistance.
  • The platform aids in understanding plant adaptive responses to climate change.