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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: Jun 13, 2026

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

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

Published on: August 8, 2017

An object-based framework for identifying moldy corn using hyperspectral images.

Yuhang Niu¹, Zhen Yang¹, Wenrui Tian¹

¹Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, Zhengzhou 450001, PR China; Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making, Henan University of Technology, Zhengzhou 450001, PR China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy

|June 11, 2026

Summary

This summary is machine-generated.

A new object-based framework using hyperspectral imaging (HSI) accurately detects moldy corn. This method significantly improves upon existing pixel-based and kernel-based approaches for food quality monitoring.

Keywords:

Hyperspectral imaging Moldy corn NCMI Object-based

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Area of Science:

Agricultural Science
Food Science and Technology
Spectroscopy

Background:

Hyperspectral imaging (HSI) is crucial for non-destructive mold detection in grains.
Current pixel-based (PB) and kernel-based (KB) methods for mold detection have limitations, including ignoring spatial features or overlooking fine-grained kernel characteristics.

Purpose of the Study:

To develop an innovative object-based (OB) framework to enhance the accuracy of moldy corn detection.
To address the limitations of existing PB and KB methods in capturing mold features, especially when mold presence is scarce.

Main Methods:

Proposed a Normalized Corn Mold Index (NCMI) for near-infrared (NIR) hyperspectral images to improve healthy and moldy corn kernel separability.
Employed multiresolution segmentation to partition corn kernels into homogeneous objects for structured feature extraction.
Designed a multi-scale convolutional network (MSCNN) for hierarchical feature extraction from objects, followed by support vector machine (SVM) classification (MSCNN-SVM model).

Main Results:

The proposed OB framework achieved a high accuracy of 97.01% in detecting moldy corn.
The OB framework significantly outperformed traditional PB and KB methods.

Conclusions:

The novel OB framework effectively integrates multi-scale feature extraction and discriminative classification for accurate mold detection.
This framework provides robust technical support for intelligent food quality and safety monitoring, showing significant application potential.