Grape sugar content prediction with multispectral alignment and improved residual network.

Yiming Chen, Jizhou Deng, Zhijie Liu, Junchao Chen, Wang Wei, Yuanping Xiang, Xinghui Zhu, Changyun Li

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Yiming ChenHunan Agricultural University, Changsha, 410000, China.

Jizhou DengHunan Agricultural University, Changsha, 410000, China.

Zhijie LiuHunan Agricultural University, Changsha, 410000, China.

Junchao ChenTencent Music Entertainment, Shenzhen, 518000, China.

Wang WeiHunan Agricultural University, Changsha, 410000, China.

Yuanping XiangHunan Agricultural University, Changsha, 410000, China.

Xinghui ZhuHunan Agricultural University, Changsha, 410000, China.

Changyun LiHunan Agricultural University, Changsha, 410000, China. chenym@hunau.edu.cn.

Funding

The Key Research and Development Project of the Science and Technology Plan of Hunan Province, China 2020NK2033The Natural Science Foundation Project of Changsha City, Hunan Province, China kq2402123

6 · The paper itself

Abstract

Sugar content is a crucial indicator of grape ripeness and grading, and developing non-contact and non-destructive sugar content detection devices is essential for grape-picking robots and sorting platforms. Spectroscopy, which can detect the chemical composition of grapes, has become a key technology for developing non-destructive testing devices. In this paper, we collected 2,880 randomly labeled multispectral images of Sunshine Rose grapes with a Changguang Yuchen MS600 PRO multispectral camera and measured the sugar content (in Brix values) of the labeled grapes with a handheld refractometer, using data exclusively from this grape variety. To address noise and misalignment issues in the multispectral images, we proposed preprocessing methods including Gaussian denoising and ECC (Enhanced Correlation Coefficient) algorithm registration. Based on a ResNet-50 residual network, we constructed a grape sugar content prediction regression model Improved-Res with SE (Squeeze-and-Excitation) attention modules, DSC (Depthwise Separable Convolutions), and Inception modules. The model's performance was evaluated by MSE (Mean Squared Error), MAE (Mean Absolute Error), and R

Indexed as

CarbohydratesSugarsVitisAlgorithmsFruitNeural Networks, ComputerCarbohydratesSugarsDeep learningGrape sugar contentMultispectral imagingNondestructive testing

Identifiers

PMID41125696

DOI10.1038/s41598-025-20848-3

PMCPMC12546725

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