An Improved CNN-based Neural Network Model for Fruit Sugar Level Detection

TL;DR

This paper presents a novel CNN-based neural network model that integrates spectral analysis, wavelet decomposition, and genetic algorithms to accurately and nondestructively detect fruit sugar levels from spectral data.

Contribution

It introduces an innovative neural network architecture combining MLP, correlation matrices, and CNN layers for improved fruit sugar level estimation.

Findings

The proposed model outperforms traditional PLS and neural networks.

Wavelet decomposition and genetic algorithms enhance feature selection.

The model demonstrates reliable nondestructive detection of fruit sugar levels.

Abstract

Artificial Intelligence (AI) is widely used in image classification, recognition, text understanding, and natural language processing, leading to significant advancements. In this paper, we introduce AI into the field of fruit quality detection. We designed a regression model for fruit sugar level estimation, utilizing an Artificial Neural Network (ANN) based on the visible/near-infrared (V/NIR) spectra of fruits. After analyzing the fruit spectra, we proposed an innovative neural network structure: the lower layers consist of a Multilayer Perceptron (MLP), a middle layer features a 2-dimensional correlation matrix, and the upper layers contain several Convolutional Neural Network (CNN) layers. Using fruit sugar levels as the detection target, we collected data from two fruit types, Gan Nan Navel and Tian Shan Pear, and conducted separate experiments to compare their results. To assess the reliability of our dataset, we first applied Analysis of Variance (ANOVA). We then explored various strategies for processing spectral data and evaluated their impact. Additionally, we employed Wavelet Decomposition (WD) for dimensionality reduction and a Genetic Algorithm (GA) to identify optimal features. We compared the performance of Neural Network models with traditional Partial Least Squares (PLS) models, and specifically evaluated our proposed MLP-CNN structure against other traditional neural network architectures. Finally, we introduced a novel evaluation metric based on the dataset's standard deviation (STD) to assess detection performance, demonstrating the feasibility of using an artificial neural network model for nondestructive fruit sugar level detection.