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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: Aug 20, 2025

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Published on: March 17, 2023

Classification of rice leaf blast severity using hyperspectral imaging.

Guosheng Zhang¹, Tongyu Xu², Youwen Tian¹

¹Shenyang Agricultural University, Shenyang, China.

Scientific Reports

|November 17, 2022

Summary

Standard deviation (STD) of spectral reflectance in rice leaves effectively detects rice leaf blast severity. This hyperspectral imaging method using support vector machine (SVM) models shows higher accuracy than raw data.

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Last Updated: Aug 20, 2025

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RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

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Published on: August 8, 2017

Area of Science:

Agricultural Science
Plant Pathology
Remote Sensing

Background:

Rice leaf blast is a major global threat to rice production, impacting yield and quality.
Hyperspectral imaging offers a promising non-destructive approach for plant disease detection and research.

Purpose of the Study:

To evaluate the efficacy of spectral reflectance standard deviation (STD) in classifying rice leaf blast severity.
To compare the performance of Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) models using full-spectrum and STD data.

Main Methods:

Calculation of the standard deviation (STD) of spectral reflectance from whole rice leaves.
Development and comparison of SVM and PNN models for disease classification at different rice growth stages (jointing, booting, heading).

Main Results:

STD-based SVM models achieved higher average accuracies (97.78% at jointing, 92.63% at booting, 92.20% at heading) compared to full-spectrum SVM models.
STD-based PNN models also demonstrated improved average accuracies (88.89% at jointing, 91.58% at booting, 92.20% at heading) over full-spectrum PNN models.
STD analysis revealed significant differences in spectral reflectance within and among different rice leaf blast severity levels.

Conclusions:

The standard deviation of spectral reflectance is a more effective indicator for assessing rice leaf blast severity than raw spectral data.
Hyperspectral imaging combined with STD analysis and machine learning models (especially SVM) provides a robust method for early and accurate detection of rice leaf blast.