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

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Multispectral imaging-based detection of apple bruises using segmentation network and classification model.

Yanru Fang¹, Hongyi Bai^1,2, Laijun Sun^1,2

¹College of Electronics and Engineering, Heilongjiang University, Harbin, China.

Journal of Food Science

|January 20, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning method using multispectral imaging to accurately detect apple bruise levels and timing. The approach significantly improves bruise detection and classification, offering a new tool for the fruit industry.

Keywords:

bruise levels and time of apples bruised regions extraction improved DeepLabV3+improved DenseNet121 multispectral imaging technology

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Bruises negatively impact apple appearance, nutritional value, and marketability, leading to economic losses.
Accurate and timely detection of bruising is essential for quality control in the apple industry.

Purpose of the Study:

To develop and validate a novel method for precise detection of apple bruise levels and timing.
To enhance the accuracy of bruise segmentation and classification using deep learning and multispectral imaging.

Main Methods:

A self-designed multispectral imaging system was combined with an improved DeepLabV3+ model for bruise segmentation.
Depthwise separable convolution, efficient channel attention, and focal loss were employed to enhance segmentation accuracy.
Spectral data from bruised regions were analyzed using improved DenseNet121 for bruise level and time identification, incorporating cosine annealing and attention mechanisms.

Main Results:

The improved DeepLabV3+ achieved high intersection over union (IoU) scores (up to 95.5%) and F1-scores (up to 97.5%) for bruise segmentation.
The enhanced DenseNet121 model demonstrated superior performance in identifying bruise levels (up to 99.5% accuracy) and bruise time (up to 99.3% accuracy).

Conclusions:

The proposed deep learning-based multispectral imaging method offers a highly accurate and effective solution for detecting apple bruise levels and timing.
This technology has the potential to significantly reduce economic losses and improve quality control in apple production and supply chains.