A quality detection method of corn based on spectral technology and deep learning model

Corn is an important food crop in the world. With economic development and population growth, the nutritional quality of corn is of great significance to high-quality breeding, scientific cultivation and fine management. Aiming at the problems of cumbersome steps, time-consuming and laborious, and low accuracy in the current research on corn quality detection. This paper proposes to combine near-infrared (NIR) spectroscopy technology with deep learning technology to build a corn quality detection model based on convolutional neural network (LeNet-5). The original spectral data were preprocessed by wavelet transform (WT) and multivariate scattering correction (MSC) to remove noise interference and spectral scattering information. The Competitive Adaptive Reweighted Sampling Algorithm (CARS) was applied to optimize the characteristic wavenumber and reduce redundant data. According to the optimized characteristic wave number, it was input into the constructed corn quality detection model for simulation test, and the average detection accuracy rate of the test set was 96.46%, the average precision rate was 95.42%, the average recall rate was 97.92%, the average F1score was 96.64%, and the average recognition time was 51.95 s. Compared with traditional machine learning models such as BP neural network, K Nearest Neighbor (KNN), Support Vector Machine (SVM), Generalized Linear Model (GLM), Linear Discriminant Analysis (LDA), and Naive Bayesian (NB), the deep learning LeNet-5 network model constructed in this paper has an average accuracy increase of 39.32%, and has a higher detection accuracy.

Automated summary

This study proposes a method combining near-infrared spectroscopy technology with deep learning technology to build a corn quality detection model. Through preprocessing and feature optimization, the model shows high accuracy and recall rates on the test set, and it significantly outperforms traditional machine learning models.