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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: Oct 17, 2025

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms.

Shuaibing Liu^1,2, Xiuliang Jin¹, Chenwei Nie¹

¹Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100097, China.

Plant Physiology

|October 7, 2021

Summary

Unmanned aerial vehicles (UAVs) with multimodal data fusion and deep neural networks (DNNs) can accurately estimate maize leaf area index (LAI). Soil background improved accuracy, while tassels slightly reduced it, offering insights for precision agriculture.

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Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Published on: July 29, 2021

Area of Science:

Agricultural Science
Remote Sensing
Plant Phenotyping

Background:

Leaf Area Index (LAI) is crucial for crop growth and yield estimation.
High-throughput phenotyping of maize (Zea mays) relies on accurate LAI measurements.
The impact of multimodal data, tassels, and soil background on maize LAI estimation using UAVs is understudied.

Purpose of the Study:

To develop a framework for maize LAI estimation using remote-sensing data from UAVs.
To evaluate the performance of multimodal data fusion and deep neural networks (DNNs) for LAI estimation across different growth stages.
To investigate the influence of soil background and maize tasseling on LAI estimation accuracy.

Main Methods:

Collected red-green-blue, multispectral, and thermal infrared images using UAVs to create multimodal datasets.
Developed and compared Partial Least Square Regression (PLSR), Support Vector Regression, and Random Forest Regression models.
Implemented a deep learning model with three hidden layers for LAI estimation.

Main Results:

The DNN model achieved the best LAI estimation for a single growth period (R²=0.89, rRMSE=12.92%).
The PLSR model provided the best LAI estimation for the whole growth period (R²=0.70, rRMSE=12.78%).
Maize tassels decreased estimation accuracy, while the soil background enhanced it by providing additional image features.

Conclusions:

Multimodal data fusion with low-cost UAVs and DNNs enables accurate and reliable maize LAI estimation.
The findings are valuable for high-throughput phenotyping and precision farmland management.
Understanding the effects of tassels and soil background is critical for optimizing LAI estimation models.