Kangshun Li · Wei Li · Hui Wang · Yong Liu (Eds.)

11th International Symposium, ISICA 2019Guangzhou, China, November 16–17, 2019Revised Selected Papers

Artificial IntelligenceAlgorithms and Applications

Communications in Computer and Information Science 1205

Communicationsin Computer and Information Science 1205

Commenced Publication in 2007Founding and Former Series Editors:Simone Diniz Junqueira Barbosa, Phoebe Chen, Alfredo Cuzzocrea,Xiaoyong Du, Orhun Kara, Ting Liu, Krishna M. Sivalingam,Dominik Ślęzak, Takashi Washio, Xiaokang Yang, and Junsong Yuan

Editorial Board Members

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More information about this series at http://www.springer.com/series/7899

Kangshun Li • Wei Li • Hui Wang •

Yong Liu (Eds.)

Artificial IntelligenceAlgorithms and Applications11th International Symposium, ISICA 2019Guangzhou, China, November 16–17, 2019Revised Selected Papers

123

EditorsKangshun LiSouth China Agricultural UniversityGuangzhou, China

Wei LiJiangxi University of Scienceand TechnologyGanzhou, China

Hui WangSouth China Agricultural UniversityGuangzhou, China

Yong LiuThe University of AizuAizu-Wakamatsu, Fukushima, Japan

ISSN 1865-0929 ISSN 1865-0937 (electronic)Communications in Computer and Information ScienceISBN 978-981-15-5576-3 ISBN 978-981-15-5577-0 (eBook)https://doi.org/10.1007/978-981-15-5577-0

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Preface

CCIS 1205 comprises the post proceedings of the 11th International Symposium onIntelligence Computation and Applications (ISICA 2019) held in Guangzhou, China,November 16–17, 2019. This volume features the most up-to-date research in evolu-tionary algorithms, parallel and quantum computing, evolutionary multi-objective anddynamic optimization, intelligent multimedia systems, virtualization and AI applica-tions, smart scheduling, intelligent control, big data and cloud computing, deeplearning, and hybrid machine learning systems.

CCIS 1205 is dedicated in memory of Lishan Kang on the 10th anniversary of hisdeath. Prof. Kang was the founder of ISICA, who organized the first ISICA in 2005.Besides his research book on evolutionary computation, Non-Numerical Algorithms:(II) Genetic Algorithms published by China Science Press in 1995, Prof. Kang gavehundreds of public talks and lectures on both domain decomposition methods andevolutionary computation at many universities in China starting in the 1980s. In the late1980s, Prof. Kang foresaw that evolutionary computation was the foundation ofcomputational intelligence while computational intelligence was the future of com-putational science. Nowadays thousands of students and researchers in China arefollowing in his footsteps. Evolutionary computation will bring us to creative evolutionbeyond deep learning from the available big data and powerful hardware.

On behalf of the Organizing Committee, we would like to warmly thank thesponsors: South China Agricultural University, Jiangxi University of Science andTechnology, Intelligent Simulation Optimization and Scheduling Committee of ChinaSimulation Federation, and Computing Intelligence of Guangdong Computer Acad-emy, who helped in one way or another to achieve our goals for the conference. Wewish to express our appreciation to Springer for publishing the proceedings of ISICA2019. We also wish to acknowledge the dedication and commitment of both the staff atthe Springer Beijing Office and the CCIS editorial staff. We would like to thank theauthors for submitting their work, as well as the Program Committee members andreviewers for their enthusiasm, time, and expertise. The invaluable help of activemembers from the Organizing Committee, including Lixia Zhang, Lei Yang, YanChen, Hui Wang, Zhiping Tan, Ying Feng, Dunmin Chen, Yaohua Liu, WenbiaoChen, Xiangzheng Fu, Qiong Liu, Daisy Kansal, Jalil Hassan, and Nwokedi KingsleyObumneme, in setting up and maintaining the online submission systems by Easy-Chair, assigning the papers to the reviewers, and preparing the camera-ready versionof the proceedings is highly appreciated. We would like to thank them personally fortheir help in making ISICA 2019 a success.

March 2020 Kangshun LiWei Li

Hui WangYong Liu

Organization

Honorary Chairs

Kay Chen Tan City University of Hong Kong, ChinaQingfu Zhang City University of Hong Kong, ChinaLing Wang Tsinghua University, China

General Chairs

Kangshun Li South China Agricultural University, ChinaZhangxing Chen University of Calgary, CanadaZhijian Wu Wuhan University, China

Program Chairs

Yiu-ming Cheung Hong Kong Baptist University, ChinaJing Liu Xidian University, ChinaHailin Liu Guangdong University of Technology, ChinaYong Liu University of Aizu, Japan

Local Arrangement Chair

Zhiping Tan South China Agricultural University, China

Publicity Chairs

Lixia Zhang South China Agricultural University, ChinaYan Chen South China Agricultural University, ChinaLei Yang South China Agricultural University, China

Program Committee

Ehsan Aliabadian University of Calgary, CanadaRafael Almeida University of Calgary, CanadaEhsan Amirian University of Calgary, CanadaZhangxing Chen University of Calgary, CanadaIyogun Christopher University of Calgary, CanadaLixin Ding Wuhan University, ChinaXin Du Fujian Normal University, ChinaZhun Fan Shantou University, ChinaZhaolu Guo Jiangxi University of Science and Technology, ChinaGuoliang He Wuhan University, China

Jun He Aberystwyth University, UKYing Huang Gannan Normal University, ChinaDazhi Jiang Shantou University, ChinaXiangjing Lai University of Angers, FranceKangshun Li South China Agricultural University, ChinaWei Li Jiangxi University of Science and Technology, ChinaGuangming Lin Southern University of Science and Technology, ChinaHailin Liu Guangdong University of Technology, ChinaHu Peng Jiujiang University, ChinaAllan Rocha University of Calgary, CanadaZahra Sahaf University of Calgary, CanadaKe Tang Southern University of Science and Technology, ChinaFeng Wang Wuhan University, ChinaHui Wang Nanchang Institute of Technology, ChinaJiahai Wang Sun Yet-sen University, ChinaJing Wang Jiangxi University of Finance and Economics, ChinaLingling Wang Wuhan University, ChinaShenwen Wang Shijiazhuang University of Economics, ChinaXuewen Xia East China Jiaotong University, ChinaXuesong Yan China University of Geosciences, ChinaLei Yang South China Agricultural University, ChinaShuling Yang South China Agricultural University, ChinaXuezhi Yue Jiangxi University of Science and Technology, ChinaMohammad Zeidani University of Calgary, CanadaSanyou Zeng China University of Geosciences, ChinaLixia Zhang South China Agricultural University, ChinaKejun Zhang Zhejiang University, ChinaWensheng Zhang Chinese Academy of Sciences, ChinaAimin Zhou East China Normal University, ChinaXinyu Zhou Jiangxi Normal University, ChinaJun Zou The Chinese University of Hong Kong, Hong Kong,

China

viii Organization

Contents

New Frontier in Evolutionary Algorithms

Citrus Disease and Pest Recognition Algorithm Basedon Migration Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Kangshun Li, Miaopeng Chen, Juchuang Lin, and Shanni Li

Artificial Bee Colony Based on Adaptive Selection Probability . . . . . . . . . . . 21Songyi Xiao, Hui Wang, Minyang Xu, and Wenjun Wang

Average Convergence Rate of Evolutionary Algorithms II:Continuous Optimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Yu Chen and Jun He

Optimization Design of Multi-layer Logistics Network Basedon Self-Adaptive Gene Expression Programming . . . . . . . . . . . . . . . . . . . . 46

Huazhi Zhou, Kangshun Li, Runxuan Xu, Xinyao Qiu, and Zhanbiao Zhu

Potential Well Analysis of Multi Scale Quantum HarmonicOscillator Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Jin Jin and Peng Wang

Design and Implementation of Key Extension and Interface ModuleBased on Quantum Circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Chengcheng Wang, Jiahao Sun, Zhijin Guan, Jiaqing Chen,and Yuehua Li

Research on Atmospheric Data Assimilation Algorithm Based on ParallelTime-Varying Dual Compression Factor Particle Swarm OptimizationAlgorithm with GPU Acceleration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Ke Chen, Yadong Liu, Liyuan Liu, Yidong Yu, Yiqi Dong, and Yala Tong

A Parallel Gene Expression Clustering Algorithm Basedon Producer-Consumer Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Lei Yang, Xin Hu, Kangshun Li, Wensheng Zhang, Yaolang Kong,Rui Xu, and Dongya Wang

Evolutionary Multi-objective and Dynamic Optimization

Decomposition-Based Dynamic Multi-objective Evolutionary Algorithmfor Global Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Qing Zhang, Ruwang Jiao, Sanyou Zeng, and Zhigao Zeng

A Novel Multi-objective Evolutionary Algorithm Basedon Space Partitioning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Xiaofang Wu, Changhe Li, Sanyou Zeng, and Shengxiang Yang

Neural Architecture Search Using Multi-objective Evolutionary AlgorithmBased on Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Weiqin Ying, Kaijie Zheng, Yu Wu, Junhui Li, and Xin Xu

A Collaborative Evolutionary Algorithm Based on Decompositionand Dominance for Many-Objective Knapsack Problems . . . . . . . . . . . . . . . 155

Hainan Huang, Weiqin Ying, Yu Wu, Kaijie Zheng, and Shaowu Peng

A Many-Objective Algorithm with Threshold Elite Selection Strategy . . . . . . 167Shaojin Geng, Di Wu, Penghong Wang, and Xingjuan Cai

Multi-objective Optimization Algorithm Based on Uniform Designand Differential Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180

Jinrong He, Dongjian He, Aiqing Shi, and Guoliang He

Research on Optimization of Multi-target Logistics Distribution Basedon Hybrid Integer Linear Programming Model . . . . . . . . . . . . . . . . . . . . . . 194

Jinfeng Wang, Jiayan Tang, Linqi He, Zirong Ji, Zhenyu He,Cheng Yang, Rongliang Huang, and Wenzhong Wang

Research of Strategies of Maintaining Population Diversityfor MOEA/D Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209

Wenxiang Wang, Xingzhen Tao, Lei Deng, and Jun Zeng

Intelligent Multimedia Systems

Farm Characteristics, Social Dynamics and Dairy Farmers’ Conversionsto Organic Farming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225

Qing Xu, Sylvie Huet, and Wei Li

AnimeGAN: A Novel Lightweight GAN for Photo Animation . . . . . . . . . . . 242Jie Chen, Gang Liu, and Xin Chen

BERT-BiLSTM-CRF for Chinese Sensitive Vocabulary Recognition . . . . . . . 257Yujuan Yang, Xianjun Shen, and Yujie Wang

The Classification of Chinese Sensitive Information Basedon BERT-CNN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269

Yujie Wang, Xianjun Shen, and Yujuan Yang

RASOP: An API Recommendation Method Based on WordEmbedding Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281

Bin Zhang, Lihua Sheng, Lei Jin, and Wanzhi Wen

x Contents

Application of Improved Collaborative Filtering Algorithm in PersonalizedTourist Attractions Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296

Yujie Liang, Xin Li, Jiali Lin, and Dazhi Jiang

Research on Partner Selection in Virtual Enterprises Based on NSGA-II . . . . 307Haixia Gui, Banglei Zhao, Xiangqian Wang, and Huizong Li

Research on Big Data System Based on Cultural Tourism in Dongguan. . . . . 320Ding Li and Kangshun Li

Virtualization and AI Applications

Fusion of Skin Color and Facial Movement for FacialExpression Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333

Wanjuan Song and Wenyong Dong

Automatic Orange Fruit Disease Identification Using VisibleRange Images. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341

Vladimir Peter, Muhammad Asim Khan, and Huilan Luo

Orange Leaf Diseases Identification Using Digital Image Processing . . . . . . . 360Irene Anney Joseph, Muhammad Asim Khan, and Huilan Luo

A Lightweight Convolutional Neural Network for License PlateCharacter Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379

Xingzhen Tao, Lin Li, and Lei Lu

A Robust Green Grape Image Segmentation Algorithm Against VaryingIllumination Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388

Haojie Huang, Qinghua Lu, Lufeng Luo, Zhuangzhuang Zhou,and Zongjie Lin

A Convolutional Neural Network Model of Image Denoisingin Real Scenes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399

Shumin Xie, Xingzhen Tao, and Lei Deng

Multilevel Image Thresholding Based on Renyi Entropy Using CuckooSearch Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405

Zhijun Liang and Yi Wang

Multilevel Image Thresholding Using Bat Algorithm Based on Otsu . . . . . . . 414Suping Liu and Yi Wang

A Color-Filling Algorithm for Dialect Atlas . . . . . . . . . . . . . . . . . . . . . . . . 421Jiaqi Yuan and Huaxiang Cai

Contents xi

Person Re-identification Based on Spatially Constraints and KernelConsensus PCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431

Bin Hu, Yanjing Cai, Shi Cheng, and Zelin Wang

Three-Dimensional Reconstruction and Monitoring of Large-ScaleStructures via Real-Time Multi-vision System. . . . . . . . . . . . . . . . . . . . . . . 442

Yunchao Tang, Mingyou Chen, Xiangguo Wu, Kuangyu Huang,Fengyun Wu, Xiangjun Zou, and Yuxin He

Facial Expression Recognition Adopting Combined Geometricand Texture-Based Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458

Yujiao Gong and Yongbo Yuan

Smart Scheduling

Nested Simulated Annealing Algorithm to Solve Large-ScaleTSP Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473

Lei Yang, Xin Hu, Kangshun Li, Weijia Ji, Qiongdan Hu, Rui Xu,and Dongya Wang

Modeling and Scheduling for the Clean Operationof Semiconductor Manufacturing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 488

Ya-Chih Tsai, Jihong Pang, and Fuh-Der Chou

Research on IRP of Perishable Products Based on Improved DifferentialEvolution Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497

Zelin Wang and Jiansheng Pan

An Improved Hybrid Particle Swarm Optimization for TravelSalesman Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514

Bo Wei, Ying Xing, Xuewen Xia, and Ling Gui

Application of Parametric Design in Urban Planning . . . . . . . . . . . . . . . . . . 526Rongrong Gu and Wuzhong Zhou

Iterated Tabu Search Algorithm for the Multidemand MultidimensionalKnapsack Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541

Dongni Luo, Xiangjing Lai, and Qin Sun

Research on CCE Allocation Algorithm in LTE . . . . . . . . . . . . . . . . . . . . . 551Yuechen Yang, Qiutong Li, and Wenjuan Wei

Research on Tobacco Silk Making Scheduling Based on Improved DE . . . . . 560Qi Ji, Wei Wang, Mingmeng Meng, Chengliang Yang,and Zhongmin Zhang

xii Contents

Intelligent Control

MODRL/D-AM: Multiobjective Deep Reinforcement LearningAlgorithm Using Decomposition and Attention Modelfor Multiobjective Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575

Hong Wu, Jiahai Wang, and Zizhen Zhang

Parameters Tuning of PID Based on Improved ParticleSwarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 590

Wei Yu and Qingmei Zhao

Design and Analysis of Knee-Joint Force Reduction Device and FatigueDetection System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 599

Caihua Qiu and Feng Ding

Design and Implementation of Face Recognition Access Control System . . . . 609Ling Peng and Yanchun Chen

Geohash Based Indoor Navigation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 616Yingshi Ye, Fuyi Wei, Xianzi Cai, and Xiaofeng Hu

Application of NARX Dynamic Neural Network in Quantitative InvestmentForecasting System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 628

Jiao Peng and QingLong Tang

A Deep Reinforcement Learning Algorithm Using Dynamic AttentionModel for Vehicle Routing Problems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 636

Bo Peng, Jiahai Wang, and Zizhen Zhang

Elliptical Wide Slot Microstrip Patch Antenna Design by Using DynamicConstrained Multiobjective Optimization Evolutionary Algorithm . . . . . . . . . 651

Rangzhong Wu, Caie Hu, and Zhigao Zeng

Imputation Methods Used in Missing Traffic Data: A Literature Review . . . . 662Pan Wu, Lunhui Xu, and Zilin Huang

Big Data and Cloud Computing

Mining and Analysis Based on Big Data in Public Transportation . . . . . . . . . 681Yinxin Bao, Chengyu Zhang, and Quan Shi

The Study on Low Laser Damage Technology of SE Solar Cell . . . . . . . . . . 689Shuaidi Song, Chunhua Sheng, Rui Cao, Tan Song, and Qiang Wang

Information Security Risk and Protective Measures of Computer Networkin Big Data Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 699

Lei Deng, Haiping Li, and Fanchun Li

Contents xiii

Anti-lost Intelligent Tracker Based on NB-IoT Technology for the Elderly . . . 709Juanjuan Tao, Shucheng Xie, Jinwei Jiang, and Wenbin Wei

Performance Optimization of Cloud Application at SoftwareArchitecture Level. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 724

Xin Du, Youcong Ni, Peng Ye, Xin Wang, and Ruliang Xiao

Mining and Analysis of Big Data Based on New Energy Public Transit. . . . . 739Yinxin Bao and Quan Shi

Statistical Learning

The Network Design of License Plate Recognition Basedon the Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 749

Xingzhen Tao, Fahui Gu, and Shumin Xie

Research on Intelligent Algorithm for Image Quality Evaluation Basedon Image Distortion Type and Convolutional Neural Network . . . . . . . . . . . 759

Lei Deng, Fahui Gu, and Shumin Xie

Artificial Bee Colony Algorithm Based on New Search Strategy. . . . . . . . . . 772Minyang Xu, Hui Wang, Songyi Xiao, and Wenjun Wang

Regression Network for Real-Time Pedestrian Detection . . . . . . . . . . . . . . . 780Wanjuan Song and Wenyong Dong

Dynamic Gesture Recognition Based on HMM-DTW Model UsingLeap Motion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 788

Geng Tu, Qingyuan Li, and Dazhi Jiang

Learning Target Selection in Creating Negatively CorrelatedNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 799

Yong Liu

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 809

xiv Contents

New Frontier in EvolutionaryAlgorithms

Citrus Disease and Pest Recognition AlgorithmBased on Migration Learning

Kangshun Li1(&), Miaopeng Chen1, Juchuang Lin1, and Shanni Li2

1 College of Mathematics and Informatics, South China Agricultural University,Guangzhou 510642, Chinalikangshun@sina.com

2 Digital Grid Research Institute, China Southern Power Grid,Guangzhou 510660, China

Abstract. Citrus is the largest fruit production in the world. Owing to thedamage by various pest diseases, the production of citrus is reduced and thequality is getting worse and worse every year. The recognition and control of thecitrus diseases are very important. By now the main measures we take to controlthem is sowing pesticides, which is not good for the environment and do harm tothe soil greatly. The technology of image identification can recognize what kindof citrus disease they have with high efficiency and low cost, which is alsoenvironmentally friendly and is not limited by time and space. It is our toppriority to apply it to recognize and prevent the disease from citrus. In order todetect citrus pest disease and control them automatically, we studied the pestsand traits of citrus leaves and their multi-fractal characteristics and methods forfiguring pests and diseases, and created a model for detecting leaf images ofcitrus. We use Keras and Tensorflow to build the model. To reduce recognitionloss and improve accuracy, we put the citrus photos into the model and train itpersistently. After examining, the recognition accuracy of citrus greening dis-ease of 120 images can reach 96%. The experimental result shows that themodel can recognize citrus diseases with high accuracy and robustness.

Keywords: Image recognition � Machine learning � Deep learning �Convolutional neural network � Migration learning � VGG16 model

1 Introduction

There are many precedents of plant disease recognition at home and abroad. In 2017,Zhao et al. [1] adapted the Otus threshold segmentation algorithm to extract 4 kinds ofdiseased potato leaves images and extract underlying visual feature vectors. She usedthe SVMc classifier, and the recognition rate is 92%. In 2018, Shi JiHong [2] tried tocombine traditional database with service of WeChat public platform, focused onagricultural disease and pest recognition and realize image database construction basedon WeChat public account which provided the users a convenient query, identify anddisease prediction platform. Sharada et al. [3] trained a deep convolutional network todetect 26 kinds of diseases of 14 kinds of plants, its classification accuracy reached99.35% in 54306 training disease photos. And it highlights the importance of deeplearning and convolutional network.

© Springer Nature Singapore Pte Ltd. 2020K. Li et al. (Eds.): ISICA 2019, CCIS 1205, pp. 3–20, 2020.https://doi.org/10.1007/978-981-15-5577-0_1

Rastogi A et al. [4] proposed a universal system for leaf disease identification, thefirst stage is based on feature extracting and artificial neural network recognition, thesecond stage is based on Kemans segmentation and ANN disease classification.

All the above researches show the hot topic of plant disease recognition based oncomputer vision combined with the popular interconnecting devices. But the recog-nition with high accuracy and low response time is the guarantee for the promotion ofthe identification technology. This paper researches on the recognition algorithmsbased on migration learning, and finds its great advantage in the recognition of citrusdisease and pest.

2 Deep Learning and Migration Algorithm

2.1 Machine Learning

Machine learning specializes in how computer simulates or realizes human learningbehavior to acquire new knowledge or skills, and how to reorganize knowledgestructures and continuously develop its performance. It is central to artificial intelli-gence and the base to make computers intelligent. Machine learning mainly refers tothat computer acquires knowledge from experience(data) and we could deem it asfiguring out patterns and then learned from it. And machine learning is also calledpattern recognition [5].

Automatically learning from data instead of following certain rules is a dataanalysis method or technique, and experience-based learning is the focus of machinelearning. Machine learning is not programmed to perform a task, but programmed tolearn to perform a task [6]. Machine learning can be divided into supervised learning,semi-supervised learning, and unsupervised learning in the light of to what extend it ismanually intervened. The division basis of supervised learning and unsupervised one iswhether their input data needs labels. The algorithms of supervised learning mainlycontain classification and regression, while unsupervised learning’s is clustering.Artificial neural network abstracts the neural networks in human brain into certainmodels according to different linking mode from an informatic processing standpoint.And it is a computing paradigm which is composed of a large number of nodes, alsoknown as artificial neural units, connecting to each other. The model may containseveral layers, which could be grouped into input layer, hidden layer and output layer.Each layer could have lots of neural units in it. The neural unit is depicted in Fig. 1.

The regular present of a neural unit is shown in Fig. 1. Each neural unit in thenetwork has its input and output. The unit gathers its input from other neural units inthe preceding layer to form the weighted output.

Z is the weighed input of neural unit i (n is the total number of the unit’s inputs; wn

represents the bias of the unit, and its weight is 1):

Z ¼Xn

i¼1xi � wi ð1Þ

4 K. Li et al.

Then uses the weighted input Z as the input of its activation function, and finallyoutputs the result to other units.

Ai is the output of the neural unit i (a is the activation function of unit i):

Ai ¼ a Zð Þ ð2Þ

2.2 Deep Learning

2.2.1 Deep Learning Network StructureMachine learning could be divided into swallow learning and deep learning. Differentfrom swallow learning which has only one hidden layer, deep learning has a lot ofhidden layers. The overall structure of neural network is shown in Fig. 2.

Fig. 1. Artificial neural unit

Fig. 2. Sequential neural network structure

Citrus Disease and Pest Recognition Algorithm 5

The symbols definition of the sequential neural network is shown in Table 1.

2.2.2 The Forward Propagation ProcessThe units in the input layer get the inputs and use them as their output, the activationfunction of the units in the input layer can be described as a(x) = x, and the output ofthe first layer in the model(input layer, l = 1) can be depicted as, M is the total numberof units in input layer:

a1i ¼ xi i ¼ 1; 2; . . .;Mð Þ ð3Þ

Each unit in the hidden layers receives other units’ outputs in the preceding layer asits input if it’s fully connected. And it uses the weighed input as the input of itsactivation function and gets the output.

The output of unit i in layer l, l > 1(m − 1 is the total number of units in layer l-1;a() is the activation function of the unit):

zli ¼Xm

j¼1al�1j � wl

ij

� �þ bli ð4Þ

ali ¼ a zli� � ð5Þ

Finally we get the outputs aN1 ; aN2 ; . . .; a

Nk , assume there are k units in the output

layer, we gather them as model’s result. And the predict result is usually evaluated bythe loss functions, some of which are listed below. by represents the output of the model,y represents the input data’s label, n is the total number of the input data. Common lossfunctions are shown as below:

squared error loss function

L by; yð Þ ¼ 12

Xn

i¼1y� byð Þ2 ð6Þ

cross entropy loss function

L by; yð Þ ¼Xn

i¼1�y � logby � 1� yð Þ � log 1� byð Þ ð7Þ

Since the weighted input procedure presents the linear function and activationfunctions are usually nonlinear. Combined with linear and nonlinear function, the deep

Table 1. Sequential neural network symbols

Neural unitsymbols

Symbol meanings

ali The output of unit i in layer l, if it represents the output of input layer, itcan also be replaced by xi

wlij The weight of the input from unit j in layer l-1 to unit i in layer l

bli The bias of the weighted input of unit i in layer l

zli The weighted input of unit i in layer l

6 K. Li et al.

learning network could stimulate a large amount of transform in the world theoretically.The network itself is usually the approach of some kind of algorithm or function innature, or some expression of strategy [7].

2.2.3 The Back Propagation ProcessThe model relies on the gradient decline algorithm, shown in formula (8) below, whichis the mathematical basis of supervised learning model. The gradient descent method isa typical method which calculate the minimum values of the target function by slowlymoving the point in the define domain to explore rather than finding the solution of theequation with partial derivatives equal to 0 [8]. w and b are weights and bias of themodel, g is a small positive number which we call learning rate, Loss presents the lossfunction of the model, later we use L to represent it instead. w� and b� are the updatedvalues of w and b.

Dw;Dbð Þ ¼ �g@Loss@w

;@Loss@b

� �w�; b�ð Þ ¼ wþDw; bþDbð Þ ð8Þ

We define neural unit error of unit i in layer l (l > 1) as below:

dli ¼@L@zli

ð9Þ

Neural unit error has relations with the gradient of weights and bias below. As longas we get the value of the neural unit errors of the layers from hidden layer to outputlayer, we could get the gradients of each parameter of the model easily.

@L@wl

ij¼ dlj � al�1

i@C@zlj

¼ dlj;@zlj@wl

ji¼ al�1

i

!ð10Þ

@C@blj

¼ dlj@C@zlj

¼ dlj;@zlj@blj

¼ 1

!ð11Þ

The neural unit error in output layer, shown as below (N represents the output layer,a() is the activation function of unit i in the output layer, there are k units in the outputlayer):

dNi ¼ @L@zNi

¼ @L@aNi

� a0zNi� �

i ¼ 1; 2; . . .; kð Þ ð12Þ

And the neural unit errors in layer l and in layer l-1 has relations, shown as follows(m, n are the total number of units in layer l-1 and layer l; a() is the activation functionof unit j in layer l-1):

Citrus Disease and Pest Recognition Algorithm 7

dl�1j ¼

Xn

i¼1

@C@zli

� @zli@al�1

j� @a

l�1j

@zl�1j

¼Xn

i¼1dli � wl

ij � a0zl�1j

� �j ¼ 1; 2; . . .;mð Þ ð13Þ

The back propagation algorithm of sequential network model:

1) Get the output of the model, count the neural unit error in the output layer accordingto formula (12).

2) If the preceding layer is not the input layer, use formula (10), (11) to count thegradients of the weights and bias of this layer, turn to step 3; else turn to step 4.

3) Use formula (13) to count the neural unit errors in the preceding layer, turn tostep 2.

4) Get all the gradients of the parameters in the model, then update the parametersaccording to formula (8).

2.3 Migration Learning

Migration learning is a sort of machine learning which refers to adapting a pretrainedmodel to another recognition task. The migration learning refers to the migration fromthe original task and data to the target task and data, using the weight parameters in theoriginal data domain to improve the predictive function of the target task [9]. It canefficiently reduce the over-fitting degree of the normal convolution neural network.

3 Citrus Pest and Diseases Identification Based on DeepLearning and Migration Learning

3.1 Problem Description

At present, the fruit plantation area reaches 1130 thousand hectares, among whichcitrus’s occupy 266 thousand, in Guangdong province, China. And citrus is the maintype of fruit in Guangdong. Due to the numerous citrus diseases, the planting area ofcitrus in Guangdong reduces by more than 30% and the diseases causes the directeconomic loss of about 4 billion yuan each year. Therefore the recognition of citrusdiseases is of great importance. In the past, people used the convolutional neuralnetwork (CNN) model to distinguish the disease citrus from healthy one. The con-volution network can better solve the problem that it’s hard to find an appropriatefeature to train because of the citrus leaves’ great similarity, and we don’t have tochoose features manually for the training, CNN is capable of learning from the original2D photo. And It can extract new features of the input photos as well as renewing thefeatures it learned persistently. But CNN also has drawbacks like overfitting.

This paper focus on the sick recognition of citrus, including citrus greening disease,Citrus canker disease and Citrus Anthracnose. We try to build a recognition model withthe CNN and later we build the model based on migration learning methods.

8 K. Li et al.

3.2 The Structure of Convolutional Neural Network

Convolutional neural network is a kind of feed forward neural network. CNN’s net-work structure which is local-sensitive and weight-sharing makes it more like a bio-logical neural network, and it decreases the number of neural units’ weights, andlargely reduces the complexity of the model.

CNN can be divided into convolutional layer, sampling layer, flatten layer, andfully connected layer. The structure is shown in Fig. 6. The first three layers form thebasic unit for CNN.

3.2.1 Convolutional LayerConvolutional layer is used to extract the 2-dimension feature of the photo, usingdifferent kernels to detect different edge of the interest region. C is short for convo-lutional layer, which is used to extract features from the picture. Different from otherneural network, CNN uses a matrix to store the values of the weights, which is definedas the convolutional kernel. When the layer gets the input photo data, it will use thekernel to scan the photo from the up-left side to the right-bottom side, and each step ofthe convolution will generate a new pixel in the output feature map according to thelinear transformation between the kernel and the region of photo it cover. The sche-matic diagram of convolution is shown in Fig. 3.

Fig. 3. Schematic diagram of convolution with kernel size (2 � 2), input size (3 � 3)

Citrus Disease and Pest Recognition Algorithm 9

3.2.2 Sampling LayerSampling layer mainly aims at reducing the size of the feature map and extract theprimary features. Through the process of sampling, the number of parameters drops.Sampling layer execute the pooling function which select the max, min, average valueof the region it covers as its output. The sampling filter does not store any weights likethe convolutional kernel. The schematic diagram of max-pooling is depicted in Fig. 4.

3.2.3 Dropout Layer and Flatten LayerThe usage of dropout layer is to disable some neural units randomly, preventing themodel from overfitting and gradient vanishing. CNN use the dropout function to pickneural units in the hidden layer randomly and disable them during the training process.Because the disabled units could not transmit the signal forward, it averts the over-fitting problem effectively [10].

Flatten layer is designed to flatten the output result of sampling layer, it’s usuallyconnected between the parts of feature extraction and pattern recognition in the model.

3.2.4 Fully Connected LayerFully connected means each neural unit in the layer are fully connected to the pre-ceding layers’ units. Convolutional layer, activation layer and sampling layer are thelayers to extract features, and the duty of fully connected layer is to integrate thefeatures and ready for classification and identification procedure [11]. It is just like theneural network in Fig. 2.

3.3 The Forward Propagation of Convolution Neural Network

3.3.1 Symbols Definition of the Convolutional Neural NetworkCNN’s symbols definition is shown in Table 2.

Fig. 4. Schematic diagram of max-pooling with filter size (2 � 2), input size (4 � 4)

10 K. Li et al.

3.3.2 The Input Process of the CNNThe input process of CNN is shown in Fig. 5, and the relation of input layer is depictedbelow, in formula (15) (we use the input size of (5, 5) as an example):

aIij ¼ xij ð15Þ

Table 2. Convolutional neural network symbols

CNN layers Layer’ssymbol

Symbol meaning

Input layer xij It represents the input in row i, column j

aIij It represents the output of the input layer in row i, column j

Filter of CNN wFkij It represents the weight in row i, column j in the filter in

CNN’s sublayer kConvolutionallayer

zFkij It represents the weighted input of the neural unit in row i,column j in CNN’s sublayer k

bFk It represents the bias of the neural unit in CNN’s sublayer k

aFkij It represents the output of the neural unit in row i, column j inCNN’s sublayer k

Sampling layer zPkij It represents the input of the sampling layer in row i, column jin CNN’s sublayer k

aPkij It represents the output of the sampling layer in row i, columnj in CNN’s sublayer k

Flatten layer aFi It represents the output of the flatten layer of unit i

Output layer wOij It represents the weight in output layer which is from neural

unit j in the flatten layer to unit i in the output layer

zOn It represents the weighted input of the nth neural unit in theoutput layer

bOn It represents the bias of the unit n in the output layer

aOn It represents the output of the unit n in the output layer

Fig. 5. Input process of the convolutional neural network

Citrus Disease and Pest Recognition Algorithm 11

3.3.3 The Convolution Process of CNNThe convolution process of CNN is shown in Fig. 6, and the relations in convolutionallayer are depicted below, assuming that the CNN has 3 sublayers (Win;Hin are the inputsize of the convolutional layer, depicted as Wi;Hi below; Wout;Hout are the output sizeof the convolutional layer, depicted as Wo;Ho below; Wf ;Hf are the kernel’s size,Pw;Ph are the padding number of the output feature map, stridew; strideh are the stridesof the kernel in the horizontal direction and vertical direction; af ðÞ represents theactivation of the convolutional layer):

Wout ¼ Win � Fw þ 2 � Pw

Stridewþ 1 ð16Þ

Hout ¼ Hin � Hw þ 2 � Ph

Stridehþ 1 ð17Þ

cFkij ¼XWf

k¼1

XHf

l¼1wFkkl � aIiþ k�1jþ l�1 i ¼ 1; 2; . . .;Wo; j ¼ 1; 2; . . .;Hoð Þ ð18Þ

zFkij ¼ cFkij þ bFk i ¼ 1; 2; . . .;Wo; j ¼ 1; 2; . . .;Hoð Þ ð19Þ

aFkij ¼ af zFkij� �

ð20Þ

3.3.4 The Sampling Process of CNNThe max-pooling process of CNN is shown in Fig. 7, and the relations in samplinglayer are depicted below (Win;Hin are the input size of the sampling layer, depicted asWi;Hi below; Wp;Hp are the size of the sampling filter; Wout;Hout are the output size ofthe sampling layer, depicted as Wo;Ho below):

Fig. 6. Convolution process of the convolutional neural network

12 K. Li et al.

Wout ¼ Win

Wp;Hout ¼ Wout

Hpð21Þ

zPkij ¼ Max aFkkl� �ði ¼ 1; 2; . . .Wo; j ¼ 1; 2; . . .;Ho; k ¼ 1; 2; . . .;Wp; l ¼ 1; 2; . . .;HpÞ

ð22Þ

aPkij ¼ zPkij ð23Þ

3.3.5 The Flatten Process and Output Process of CNNThe flatten and output process of CNN is shown in Fig. 8, and the relations in flattenlayer and output layer are depicted below (Win;Hin are the input size of the flatten layer,assume that M = Win � Hin and there are K sublayers in the model, and there areN = M � K units in the flatten layer; there are O units in the output layer; ao is theactivation function of the output layer):

aFi ¼ aPljk i ¼ 1; 2; . . .;N; l ¼ iM

þ 1; j ¼ i%MWin

þ 1; k ¼ i%Win

� �ð24Þ

zOi ¼Xn

j¼1wOij � aFj

� �þ bOi i ¼ 1; 2; . . .;Oð Þ ð25Þ

aOi ¼ ao zOi� � ð26Þ

Fig. 7. Sampling process of the convolutional neural network

Citrus Disease and Pest Recognition Algorithm 13

3.4 The Backward Propagation of Convolution Neural Network

The backward propagation algorithm of CNN is similar to other deep learning model,but the major difference is that convolutional layer’s weights are in the convolutionalkernels. The flatten layer and the subsequent layers backward propagation process isthe same as deep learning model.

Define neural unit errors in the output layers and convolutional layers as below (dOnmeans the neural unit error of output unit n; dFkij means the neural unit error in row i,column j of convolutional layer in sublayer k):

dOn ¼ @L@zOn

ð27Þ

dFkij ¼ @L@zFkij

ð28Þ

Through the neural unit errors, it is easy to get the gradient of each parameter of themodel. The relation between gradients and neural unit errors are shown below (Wf ;Hf

are the kernel’s size; Wo;Ho are the output size of the convolutional layer):

@L@wO

ij¼ dOn � aFj ;

@L@bOi

¼ dOn ð29Þ

Fig. 8. Output process of the convolutional neural network

14 K. Li et al.

@L@wFk

ij¼Xwo

k¼1

Xho

l¼1dFkkl � aFkiþ k�1jþ l�1 i ¼ 1; 2; . . .;wf ; j ¼ 1; 2; . . .; hf

� � ð30Þ

@C@bFk

¼Xwo

k¼1

Xho

l¼1dFkkl ð31Þ

Then we try to figure out the expressions of neural unit errors in output layer andconvolutional layers:

dOn ¼ @L@aOn

� @aOn

@zOn¼ @L

@aOn� a0

o zOn� � ð32Þ

dFkij ¼XO

i¼1dOi � wO

ik

n o� vð Þ � a0

Fk zFkij� �

ð33Þ

v values 1 or 0 depend on whether aFkij is the largest number among the region of thefilter of the convolutional layer Fk . Then we get all the neural unit errors and we couldcalculate all the gradients of the parameters in the convolutional network and updatethe parameters according to gradient decline algorithm.

3.5 Data Preprocessing

In order to reduce overfitting phenomenon, we extend the dataset by flipping theoriginal photos horizontally and vertically and scaling randomly [12], the trainingphotos are shown in Fig. 9. Then we set the plant images to (224, 224), and we dividethe dataset according to the ratio of 5:1 into the training set and test set.

Fig. 9. Data preprocessing

Citrus Disease and Pest Recognition Algorithm 15

3.6 Convolutional Network Model

The preliminary experiment is to build a convolutional network with 4 convolutionallayers. Its structure is depicted in Table 3.

We test the model in different situations, such as different convolutional kernel sizeand different learning rate. And the test results are shown in title 4.

3.7 Migration Learning Model

The preliminary experiment shows that the CNN has high accuracy rate on test dataset,but it can easily become overfitting. In order to solve this problem, we decide to alterour model based on the migration learning algorithm. We choose the VGG-16 model asthe base of our recognition model. VGG model is a typical CNN with high classifi-cation and recognition rate. It increases the depth of the network steadily by addingmore convolutional layers. And very small convolutional filters (3 * 3) make it worksuccessfully [13].

3.7.1 The Structure of VGG16The structure of VGG16 is shown in Fig. 10 below. There are totally 16 weightedlayers in the model, 13 convolutional layers and the last 3 fully connected layers. Itsbasic unit is a convolutional layer followed by a sampling layer. The kernel size ofconvolutional layers are all 3 � 3, using relu as its activation function to train themodel quickly. And the kernel of sampling layer is 2 � 2, adopting max polling.

Table 3. Convolutional neural network structure

Layer (type) Output shape Parameter count

Convolution_1 (convolution) (224, 224, 16) 448Max_pooling_1 (Max pooling) (112, 112, 16) 0Convolution_2 (convolution) (112, 112, 32) 4640Max_pooling_2 (Max pooling) (56, 56, 32) 0Convolution_3 (convolution) (56, 56, 64) 18496Max_pooling_3 (Max pooling) (28, 28, 64) 0Convolution_4 (convolution) (28, 28, 128) 73856Max_pooling_4 (Max pooling) (14, 14, 128) 0Flatten_1 (Flatten) (25088) 0Dense_1 (Dense) (512) 12845568Dropout_1 (Dropout) (512) 0Dense_2 (Dense) (11) 5643Dropout_2 (Dropout) (11) 0Dense_3 (Dense) (2) 24

16 K. Li et al.

3.7.2 Fine-Tuning MethodWe choose the fine-tuning method to construct our model, shown in Fig. 11. Fine-tuning means adjusting the model which trained by others to train our data. It can beseen as using the front layers of the original models to extract the features of the photos,and the newly add layers to classify [14]. The increase of network layers will not leadto the explosion of the parameters, because the parameters are mainly concentrated inthe last three fully connected layers [15]. As in Fig. 16, the layers before the fullyconnected layers can not be trained, only the last three layers can be trained and itsparameters could be updated. Since the parameters in convolutional layers areuntrainable, the time for training the model is less and it’s with high efficiency.

3.7.3 Migration Model ConstructionOur migration model is shown in Table 4, we remove the top three FC layers and add aflatten, dropout and 2 dense layers for training.

Fig. 10. Structure of VGG16

Fig. 11. Structure of VGG16 with fine-tuning

Citrus Disease and Pest Recognition Algorithm 17