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Multi-Input Deep Learning Model with RGB and Hyperspectral Imaging for Banana Grading

Author

Listed:
  • Armacheska Rivero Mesa

    (Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan
    Department of Mathematics, Physics, and Computer Science, University of the Philippines Mindanao, Davao City 8000, Philippines)

  • John Y. Chiang

    (Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan
    Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 807, Taiwan
    Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung 807, Taiwan)

Abstract

Grading is a vital process during the postharvest of horticultural products as it dramatically affects consumer preference and satisfaction when goods reach the market. Manual grading is time-consuming, uneconomical, and potentially destructive. A non-invasive automated system for export-quality banana tiers was developed, which utilized RGB, hyperspectral imaging, and deep learning techniques. A real dataset of pre-classified banana tiers based on quality and size (Class 1 for export quality bananas, Class 2 for the local market, and Class 3 for defective fruits) was utilized using international standards. The multi-input model achieved an excellent overall accuracy of 98.45% using only a minimal number of samples compared to other methods in the literature. The model was able to incorporate both external and internal properties of the fruit. The size of the banana was used as a feature for grade classification as well as other morphological features using RGB imaging, while reflectance values that offer valuable information and have shown a high correlation with the internal features of fruits were obtained through hyperspectral imaging. This study highlighted the combined strengths of RGB and hyperspectral imaging in grading bananas, and this can serve as a paradigm for grading other horticultural crops. The fast-processing time of the multi-input model developed can be advantageous when it comes to actual farm postharvest processes.

Suggested Citation

  • Armacheska Rivero Mesa & John Y. Chiang, 2021. "Multi-Input Deep Learning Model with RGB and Hyperspectral Imaging for Banana Grading," Agriculture, MDPI, vol. 11(8), pages 1-18, July.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:8:p:687-:d:598542
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    References listed on IDEAS

    as
    1. Osama Elsherbiny & Yangyang Fan & Lei Zhou & Zhengjun Qiu, 2021. "Fusion of Feature Selection Methods and Regression Algorithms for Predicting the Canopy Water Content of Rice Based on Hyperspectral Data," Agriculture, MDPI, vol. 11(1), pages 1-21, January.
    2. Briones, Roehlano M., 2013. "Market Structure and Distribution of Benefits from Agricultural Exports: the Case of the Philippine Mango Industry," Discussion Papers DP 2013-16, Philippine Institute for Development Studies.
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    Cited by:

    1. Meftah Salem M. Alfatni & Siti Khairunniza-Bejo & Mohammad Hamiruce B. Marhaban & Osama M. Ben Saaed & Aouache Mustapha & Abdul Rashid Mohamed Shariff, 2022. "Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis," Agriculture, MDPI, vol. 12(9), pages 1-28, September.
    2. Xuan Chu & Pu Miao & Kun Zhang & Hongyu Wei & Han Fu & Hongli Liu & Hongzhe Jiang & Zhiyu Ma, 2022. "Green Banana Maturity Classification and Quality Evaluation Using Hyperspectral Imaging," Agriculture, MDPI, vol. 12(4), pages 1-18, April.
    3. Junchi Zhou & Wenwu Hu & Airu Zou & Shike Zhai & Tianyu Liu & Wenhan Yang & Ping Jiang, 2022. "Lightweight Detection Algorithm of Kiwifruit Based on Improved YOLOX-S," Agriculture, MDPI, vol. 12(7), pages 1-14, July.

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