A quantitative evaluation method for postharvest banana characteristic browning based on the three-dimensional point cloud

XIONG Juntao; WANG Yujie; HONG Dan; LIANG Junhao; HUANG Qiyin

doi:10.7671/j.issn.1001-411X.202307014

Journal of South China Agricultural University > 2024 > 45(3): 390-396. > DOI: 10.7671/j.issn.1001-411X.202307014

XIONG Juntao, WANG Yujie, HONG Dan, et al. A quantitative evaluation method for postharvest banana characteristic browning based on the three-dimensional point cloud[J]. Journal of South China Agricultural University, 2024, 45(3): 390-396. DOI: 10.7671/j.issn.1001-411X.202307014

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A quantitative evaluation method for postharvest banana characteristic browning based on the three-dimensional point cloud

College of Mathematics and Information, South China Agricultural University, Guangzhou 510642, China

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Received Date: July 23, 2023
Available Online: February 18, 2024
Published Date: January 10, 2024

Graphical Abstract

Abstract

Abstract

Objective
To study the characteristic browning of postharvest bananas and evaluate the degree of senescence is very important for their fresh-keeping management. This study aims to solve the problems of high labor intensity and low efficiency of traditional methods for measuring the characteristic browning of bananas.
Method
A quantitative evaluation method of postharvest banana characteristic browning process based on three-dimensional point cloud was proposed. Firstly, three-dimensional point cloud model of banana was obtained by 3D scanner, and the geometric model of banana was reconstructed. Then the banana geometry model was denoised by point cloud filtering using euclidean clustering. The volume, surface area and dark spot area of banana were obtained by combining the image threshold segmentation method and Alpha Shapes. Finally, Fourier function was used to simulate the changing process of banana surface dark spots and determine the evaluation model of banana characteristic browning process. A comparison test was designed between the algorithm and the overflow method to measure the actual banana volume and the hand-drawn measurement area.
Result
By fitting the growth functions of different bananas, the fitting degree R² of the regression line to the observed values was 0.9816>0.75, indicating the effectiveness of the algorithm. The comparison results showed that the average relative error between the proposed algorithm and the actual measured value was less than 1%, which verified the accuracy and feasibility of the proposed algorithm.
Conclusion
This study can provide data and technical support for banana preservation management.
- Three-dimensional point cloud,
- Data fitting,
- Banana,
- Browning,
- Fresh-keeping

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References (29)

References

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A quantitative evaluation method for postharvest banana characteristic browning based on the three-dimensional point cloud

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