# Nondestructive Detection of Soluble Solids Content in Apples Based on Multi-Attention Convolutional Neural Network and Hyperspectral Imaging Technology

## TL;DR

This paper introduces a new deep learning method using hyperspectral imaging to quickly and accurately detect apple quality based on their sugar content.

## Contribution

A novel multi-attention convolutional neural network (MA-CNN) is proposed for nondestructive apple quality detection.

## Key findings

- The MA-CNN model achieved an Rp2 of 0.9602 and RMSEP of 0.0612 °Brix for detecting soluble solids content in apples.
- The model outperformed other CNN variants like CA-CNN and SA-CNN in predicting apple quality parameters.
- Hyperspectral imaging combined with MA-CNN provides a rapid and effective method for nondestructive apple quality assessment.

## Abstract

Soluble solids content is the most important attribute related to the quality and price of apples. The objective of this study was to detect the soluble solids content (SSC) in ‘Fuji’ apples using hyperspectral imaging combined with a deep learning algorithm. The hyperspectral images of 570 apple samples were obtained and the whole region of apple sample hyperspectral data was collected and preprocessed. In addition, a method involving multi-attention convolutional neural network (MA-CNN) is proposed, which extracts spectral and spatial features from hyperspectral images by embedding channel attention (CA) and spatial attention (SA) modules in a convolutional neural network. The CA and SA modules help the network adaptively focus on important spectral–spatial features while reducing the interference of redundant information. Additionally, the Bayesian optimization algorithm (BOA) is used for model hyperparameter optimization. A comprehensive evaluation is conducted by comparing the proposed model with CA-CNN models, SA-CNN, and the current mainstream models. Furthermore, the best prediction performances for detecting SSC in apple samples were obtained from the MA-CNN model, with an
Rp2 value of 0.9602 and an RMSEP value of 0.0612 °Brix. The results of this study indicated that the MA-CNN algorithm combined with hyperspectral imaging technology can be used as an effective method for rapid detection of apple quality parameters.