Diseased Shrimp Identification Method Based on Adaptive Convolutional Neural Networks

被引：0

作者：

Liu Z. ^{[1
]}

Zhang S. ^{[1
]}

Jia X. ^{[1
]}

Yang J. ^{[1
]}

Zhang W. ^{[2
]}

Xu Z. ^{[3
]}

机构：

[1] College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing

[2] School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang

[3] College of Quality and Safty Engineering, China Jiliang University, Hangzhou

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2022年 / 53卷 / 05期

关键词：

Deep convolutional neural network; Diseased shrimp; Generalization accuracy; Image entropy;

D O I：

10.6041/j.issn.1000-1298.2022.05.025

中图分类号：

学科分类号：

摘要：

To solve the problem of weak generalization caused by diversity of source of shrimp samples, a novel shrimp features difference model based on shannon information theory was proposed. The model was actually a recognition framework, calculating hyper-parameters based on deep convolutional neural network (DCNN) using entropy reduction rule with multi-source datasets. This rule can clear up the special information entropy from the random input to regular output, breaking the data types changing from three dimensional input to one-dimensional output, realizing dimensionality reduction of shrimp image reducing from high dimension space to low dimensional space. Thus, the DCNN adaptive optimization strategies can be acquired to improve the generlization effectiveness of recognizing diseased shrimp from multiple sources. The experimental results showed that the proposed method in a single dataset can achieve highest accuracy of 97.96%. The generalization experiment was also tested through other four shrimp image datasets, and the generalization precision falling scope was no more than 5 percentage points. © 2022, Chinese Society of Agricultural Machinery. All right reserved.

引用

页码：246 / 256

页数：10

共 27 条

[1] LUO Yan, Detection of shrimp specification based on machine vision, (2013)
[2] LIU Zihao, Study and on-line verification for shrimp classification algorithm based on machine vision technology, (2017)
[3] HANG Xiaoying, ZHOU Zhiming, LI Qian, Et al., Effects of different breeding patterns on growth and disease occurrence of Penaeus vannamei and water quality, Jiangsu Agricultural Sciences, 42, 5, pp. 191-193, (2014)
[4] HU Wuchih, WU Hsinte, ZHANG Yifan, Et al., Shrimp recognition using ShrimpNet based on convolutional neural network, Journal of Ambient Intelligence and Humanized Computing, 12, 1, pp. 1-8, (2020)
[5] HONG Hanmei, Research of melanotic cooked shrimp recognition method using machine vision, (2015)
[6] HASSAN Z, HOSSEIN J, SAMIRA A., Combining image processing technique and three artificial intelligence methods to recognize the freshness of freshwater shrimp, Journal of Food and Bioprocess Engineering, 2, 2, pp. 155-162, (2019)
[7] YU Xinjie, TANG Lie, WU Xiongfei, Et al., Nondestructive freshness discriminating of shrimp using visible/near-infrared hyper-spectral imaging technique and deep learning algorithm, Food Analytical Methods, 11, 3, pp. 768-780, (2017)
[8] LIU Zihao, CHENG Fang, ZHANG Wei, Identification of soft shell shrimp based on deep learning, 2016 ASABE Annual International Meeting, pp. 1-8, (2016)
[9] LIU Zihao, JIA Xiaojun, XU Xinsheng, Study of shrimp recognition using smart networks, Computers and Electronics in Agriculture, 165, 4, pp. 1-10, (2019)
[10] LIU Zihao, Soft-shell shrimp recognition based on improved AlexNet for quality evaluation, Journal of Food Engineering, 266, 10, pp. 1-10, (2020)

← 1 2 3 →