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An improved U-Net network-based quantitative analysis of melon fruit phenotypic characteristics

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Abstract

Melon fruit phenotype has rich genetic variation and is an important target trait for breeding and commerciality. Existing measurement techniques have problems such as low detection accuracy, low efficiency and vague quantification methods. The study uses a deep learning approach to build an improved U-Net network for accurate segmentation of melon pericarp and seed cavity characteristics, as well as quantitative computation of phenotypic characteristics. The improved network uses VGG16 to replace the encoder in the original U-Net network, migrates the pre-training weights under the PASCAL VOC dataset, and adds a Repeated Criss-Cross Attention to the U-Net network skip connection to improve the network training efficiency and enhance the acquisition of image contextual information to solve the problem of few melon samples and poor fruit skin cavity segmentation. The segmentation results were further quantified to obtain precise parameters of melon fruit phenotypic characteristics. The experiments showed that the segmentation accuracy indexes of the improved U-Net network were 98.83% for Mean Pixel Accuracy and 96.67% for Mean Intersection over Union, which were higher than those of other comparative algorithms. The relative error of each quantitative measurement parameter is less than 5%, and the repeat accuracy is less than 0.1%, which has high accuracy and robustness. It meets the requirements of rapid detection of melon fruit characteristics and provides a powerful help for the accurate acquisition of its germplasm resource information.

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Acknowledgements

This work was supported by Key Laboratory of Storage of Agricultural Products, Ministry of Agriculture and Rural Affairs [Project Number: Kf2021003]; Natural Science Foundation of Hebei Province [Project Number: 2021202136]; Tianjin Science and Technology Plan Project [Project Number: 21ZYCGSN00320].

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Authors and Affiliations

  1. School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300130, China

    Chunyang Qian, Haolin Liu, Taihang Du & Shuguang Sun

  2. Institute of Information, Tianjin Academy of Agricultural Sciences, Tianjin, 300192, China

    Chunyang Qian & Wei Liu

  3. Vegetable Research Institute, Tianjin Kerun Agricultural Technology Co., Ltd., Tianjin, 300381, China

    Ruowei Zhang

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  1. Chunyang Qian
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  2. Haolin Liu
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  3. Taihang Du
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Correspondence to Shuguang Sun.

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Qian, C., Liu, H., Du, T. et al. An improved U-Net network-based quantitative analysis of melon fruit phenotypic characteristics. Food Measure 16, 4198–4207 (2022). https://doi.org/10.1007/s11694-022-01519-7

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