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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 10, 2025

High-Throughput Identification of Resistance to Pseudomonas syringae pv. Tomato in Tomato using Seedling Flood Assay

High-Throughput Identification of Resistance to Pseudomonas syringae pv. Tomato in Tomato using Seedling Flood Assay

Published on: March 10, 2020

Detection Method for Tomato Leaf Mildew Based on Hyperspectral Fusion Terahertz Technology.

Xiaodong Zhang¹, Yafei Wang¹, Zhankun Zhou¹

¹College of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.

Foods (Basel, Switzerland)

|February 11, 2023

Summary

This study introduces a new multi-source detection method for tomato leaf mildew using THz hyperspectral imaging, combining internal and external leaf features for accurate disease diagnosis and improved yield.

Keywords:

leaf mildew multi-source information fusion near infrared hyperspectral technology terahertz time-domain spectroscopy tomato

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Last Updated: Aug 10, 2025

High-Throughput Identification of Resistance to Pseudomonas syringae pv. Tomato in Tomato using Seedling Flood Assay

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Published on: March 10, 2020

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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
Spectroscopy

Background:

Tomato leaf mildew significantly impacts crop yield and quality.
Early and accurate detection of plant diseases is crucial for effective management.
Current detection methods may lack comprehensive analysis of disease indicators.

Purpose of the Study:

To develop a novel multi-source detection method for tomato leaf mildew.
To integrate near-infrared hyperspectral imaging and THz time-domain spectroscopy for enhanced disease detection.
To establish a fusion diagnosis and health evaluation model for improved accuracy.

Main Methods:

Utilized near-infrared hyperspectral imaging and THz time-domain spectroscopy for multi-source data acquisition.
Applied Savitzky Golay (SG) smoothing, genetic algorithm (GA), and principal component analysis (PCA) for data preprocessing and feature optimization.
Developed recognition models using backpropagation neural network (BPNN) and a Bayesian network model for data fusion.

Main Results:

Individual models achieved high recognition rates (95%–96.67%) for different mildew grades.
The fused hyperspectral THz model achieved a superior recognition rate of 97.12%.
The fusion method demonstrated a significant improvement in accuracy compared to single detection methods.

Conclusions:

The integrated THz hyperspectral imaging approach offers a robust and accurate method for detecting tomato leaf mildew.
Combining internal and external leaf features enhances diagnostic capabilities.
This fusion model provides a valuable tool for precision agriculture and disease management.