# Hyperspectral Imaging and Grading of Kiwifruit with Hierarchical 3D Convolution Data Processing

## TL;DR

This paper introduces a non-destructive method using hyperspectral imaging and a 3D convolutional network to accurately grade kiwifruit based on sugar content.

## Contribution

The novel H3DAMNet method combines 3D convolution and attention mechanisms for hyperspectral data to improve kiwifruit grading accuracy.

## Key findings

- The method achieved 97.5% overall accuracy in classifying kiwifruit by sugar content.
- It outperforms traditional destructive grading methods with high efficiency and precision.
- The approach sets a reference for classifying other fruits using hyperspectral imaging.

## Abstract

The taste and quality of kiwifruit are key factors affecting consumers’ purchase intention and satisfaction. As an important indicator for measuring kiwifruit quality, sugar content is crucial for quality grading. Accurate and rapid kiwifruit grading based on sugar content is of great significance for ensuring product quality and enhancing market competitiveness. Traditional grading methods mostly adopt destructive sampling, which are cumbersome, low in efficiency, and difficult to meet the needs of modern large-scale production. Therefore, this paper proposes a kiwifruit classification method based on the Hierarchical 3D Convolution and Attention Mechanism Network (H3DAMNet). This method performs 3D convolution operations on multiple dimensions of hyperspectral data blocks simultaneously to deeply extract spatial–spectral features. It assigns weights to each channel through the channel attention mechanism to weaken attention to irrelevant information, and introduces the bottleneck self-attention mechanism to capture the positional dependence in input features, thereby effectively modeling global information. Referring to industry standards, kiwifruit are classified into three grades based on sugar content: first-grade (≥14.5 °Brix), second-grade (13.5–14.5 °Brix), and third-grade (≤13.5 °Brix). On the test set containing 280 kiwifruit samples, the overall accuracy (OA) of this method reaches 97.5% and the average accuracy (AA) is 97.3%, successfully realizing the accurate classification of kiwifruit according to sugar content and setting a reference example for the classification of other similar fruits.