Mango Grading System Based on Optimized Convolutional Neural Network

Bin Zheng,Tao Huang,Essam Houssein

doi:10.1155/2021/2652487

Abstract

In order to achieve the accuracy of mango grading, a mango grading system was designed by using the deep learning method. The system mainly includes CCD camera image acquisition, image preprocessing, model training, and model evaluation. Aiming at the traditional deep learning, neural network training needs a large number of sample data sets; a convolutional neural network is proposed to realize the efficient grading of mangoes through the continuous adjustment and optimization of super-parameters and batch size. The ultra-lightweight SqueezeNet related algorithm is introduced. Compared with AlexNet and other related algorithms with the same accuracy level, it has the advantages of small model scale and fast operation speed. The experimental results show that the convolutional neural network model after super-parameters optimization and adjustment has excellent effect on deep learning image processing of small sample data set. Two hundred thirty-four Jinhuang mangoes of Panzhihua were picked in the natural environment and tested. The analysis results can meet the requirements of the agricultural industry standard of the People’s Republic of China—mango and mango grade specification. At the same time, the average accuracy rate was 97.37%, the average error rate was 2.63%, and the average loss value of the model was 0.44. The processing time of an original image with a resolution of 500 × 374 was only 2.57 milliseconds. This method has important theoretical and application value and can provide a powerful means for mango automatic grading.

Highlights

Mango is an important economic crop in southeast China
Xj ∈xj xk∈N(j)/i where N(j)/i represents the neighborhood of target node. i is excluded from the Markov random field (MRF) first-order neighborhood of node j. xi and xj represents the hidden node. mji represents the dissemination of information
The deep learning method based on convolutional neural network can effectively improve the recognition accuracy of mango grade classification, which is more robust and efficient than the traditional feature recognition algorithm

Summary

Introduction

It is native to tropical areas, and its shape is similar to eggs and kidneys. With the rapid development of mango planting industry and people’s increasing demand for mango quality, the quality of mango directly affects its market competitiveness. Erefore, the mango grading has become an indispensable step. China has formulated some standards for mangoes based on shape, color, and surface defects of mango. Based on the standard of the People’s Republic of China “NY/T492-2002 [1]” and “NY/T3011-2016 [2],” mango was divided into three grades according to the characteristics of mango surface defects

Methods

Results

Conclusion