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Light Acquisition

Light Acquisition

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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Related Experiment Video

Updated: Sep 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Method for Generating Real-Time Indoor Detailed Illuminance Maps Based on Deep Learning with a Single Sensor.

Seung-Taek Oh¹, You-Bin Lee², Jae-Hyun Lim²

¹Smart Natural Space Research Center, Kongju National University, Cheonan 31080, Republic of Korea.

Sensors (Basel, Switzerland)

|August 28, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning method to create detailed indoor illuminance maps using just one sensor. This approach reduces sensor needs for smart lighting systems integrating natural light.

Keywords:

deep learning illuminance map indoor detailed illuminance single sensor

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Published on: August 15, 2014

Area of Science:

Building Science
Computer Vision
Artificial Intelligence

Background:

Smart lighting systems require accurate indoor illuminance data for energy efficiency and user comfort.
Current methods for mapping indoor light rely on numerous sensors, leading to data processing challenges and user inconvenience.
Deep learning has been explored for natural light prediction but less so for detailed indoor illuminance analysis and sensor reduction.

Purpose of the Study:

To develop a deep learning-based method for generating detailed indoor illuminance maps using a single sensor.
To address the limitations of multi-sensor systems in dynamic natural light environments.
To reduce the number of sensors required for accurate indoor lighting analysis.

Main Methods:

A dataset was created with dynamic indoor illuminance and sun position data.
A deep neural network (DNN) model was trained to predict illuminance across an entire indoor space.
The model utilized input from a single illuminance sensor and the sun's position to generate an illuminance map.

Main Results:

The proposed method successfully generated detailed indoor illuminance maps.
The system achieved an average Mean Absolute Error (MAE) of 2.0 Lux and a Mean Absolute Percentage Error (MAPE) of 2.5% on clear days.
Demonstrated the feasibility of calculating illuminance levels for entire indoor areas with minimal sensing.

Conclusions:

A single-sensor deep learning approach is effective for creating comprehensive indoor illuminance maps.
This method significantly reduces hardware requirements and data processing load for smart lighting.
The findings support the commercialization of natural light-integrated lighting technologies by simplifying sensor infrastructure.