  Artificial Intelligence(AI) widely applies in Image Classification and
Recognition, Text Understanding and Natural Language Processing, which makes
great progress. In this paper, we introduced AI into the fruit quality
detection field. We designed a fruit sugar degree regression model using an
Artificial Neural Network based on spectra of fruits within the
visible/near-infrared(V/NIR)range. After analysis of fruit spectra, we
innovatively proposed a new neural network structure: low layers consist of a
Multilayer Perceptron(MLP), a middle layer is a 2-dimensional correlation
matrix layer, and high layers consist of several Convolutional Neural
Network(CNN) layers. In this study, we used fruit sugar value as a detection
target, collecting two fruits called Gan Nan Navel and Tian Shan Pear as
samples, doing experiments respectively, and comparing their results. We used
Analysis of Variance(ANOVA) to evaluate the reliability of the dataset we
collected. Then, we tried multiple strategies to process spectrum data,
evaluating their effects. In this paper, we tried to add Wavelet
Decomposition(WD) to reduce feature dimensions and a Genetic Algorithm(GA) to
find excellent features. Then, we compared Neural Network models with
traditional Partial Least Squares(PLS) based models. We also compared the
neural network structure we designed(MLP-CNN) with other traditional neural
network structures. In this paper, we proposed a new evaluation standard
derived from dataset standard deviation(STD) for evaluating detection
performance, validating the viability of using an artificial neural network
model to do fruit sugar degree nondestructive detection.
