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Related Concept Videos

Sugars as Energy Storage Molecules01:10

Sugars as Energy Storage Molecules

Sugar (a simple carbohydrate) metabolism (chemical reactions) is a classic example of the many cellular processes that use and produce energy. Living things consume sugar as a major energy source because sugar molecules have considerable energy stored within their bonds. Consumed carbohydrates have their origins in photosynthesizing organisms like plants. During photosynthesis, plants use the energy of sunlight to convert carbon dioxide gas into sugar molecules, like glucose. Because this...
Sugars as Energy Storage Molecules01:10

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Related Experiment Video

Updated: Jun 24, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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Grape sugar content prediction with multispectral alignment and improved residual network.

Yiming Chen1, Jizhou Deng1, Zhijie Liu1

  • 1Hunan Agricultural University, Changsha, 410000, China.

Scientific Reports
|October 22, 2025
PubMed
Summary

This study developed an Improved-Res deep learning model for non-destructive grape sugar content detection using multispectral imaging. The model significantly improved prediction accuracy, outperforming traditional methods for grape quality assessment.

Keywords:
Deep learningGrape sugar contentMultispectral imagingNondestructive testing

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Spectroscopy

Background:

  • Grape sugar content is vital for ripeness and grading.
  • Non-contact, non-destructive detection is crucial for automated systems.
  • Spectroscopy offers a key technology for this purpose.

Purpose of the Study:

  • To develop a non-destructive method for detecting sugar content in Sunshine Rose grapes.
  • To preprocess multispectral images for noise and misalignment.
  • To construct and evaluate a deep learning model for accurate sugar content prediction.

Main Methods:

  • Collected 2,880 multispectral images of grapes.
  • Applied Gaussian denoising and Enhanced Correlation Coefficient (ECC) registration for preprocessing.
  • Developed an Improved-Res model based on ResNet-50 with SE attention, DSC, and Inception modules.

Main Results:

  • The Improved-Res model achieved Mean Squared Error (MSE) of 0.49, Mean Absolute Error (MAE) of 0.55 Brix, and R-Square (R²) of 0.92.
  • This significantly outperformed traditional machine learning (XGBoost: MSE=1.35, MAE=0.90, R²=0.78) and standard ResNet-50 (MSE=0.95, MAE=0.96, R²=0.84).
  • Ablation experiments validated the effectiveness of SE attention, depthwise separable convolutions, and Inception modules.

Conclusions:

  • The proposed Improved-Res model offers a highly accurate and robust solution for non-destructive grape sugar content detection.
  • This technology can enhance the efficiency of grape-picking robots and sorting platforms.
  • The integration of advanced deep learning techniques shows great promise for precision agriculture.