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001481050 019__ $$a1398568714
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001481050 0247_ $$a10.1007/978-981-99-4973-1$$2doi
001481050 035__ $$aSP(OCoLC)1399168573
001481050 040__ $$aEBLCP$$beng$$cEBLCP$$dYDX$$dGW5XE$$dEBLCP
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001481050 050_4 $$aS494.5.D3
001481050 08204 $$a632.0285631$$223/eng/20230928
001481050 1001_ $$aWang, Rujing.
001481050 24510 $$aDeep learning for agricultural visual perception :$$bcrop pest and disease detection /$$cRujing Wang,Lin Jiao, Kang Liu.
001481050 260__ $$aSingapore :$$bSpringer,$$c2023.
001481050 300__ $$a1 online resource (140 p.)
001481050 500__ $$aBackbone Network Based on Transformer
001481050 504__ $$aIncludes bibliographical references.
001481050 5050_ $$aIntro -- Preface -- Contents -- About the Authors -- Chapter 1: Introduction -- 1.1 Motivation -- 1.2 Prior Work About Agricultural Pest Recognition and Detection -- 1.2.1 Traditional Manual Methods -- 1.2.2 Research on Pest Recognition Method Based on Machine Learning Method -- 1.2.3 Pest Recognition and Detection Methods Based on Deep Learning Technology -- 1.2.3.1 Pest Recognition and Detection Based on Fixed Equipment -- 1.2.3.2 Research on Pest Recognition Based on Unmanned Aerial Vehicle Dataset -- 1.2.3.3 Research on Pest Identification on Mobile Terminal Device Dataset
001481050 5058_ $$a1.2.4 Pest Datasets -- 1.2.5 Problems of Pest Detection -- 1.3 Plant Disease Detection Methods -- 1.3.1 Plant Disease Recognition Method Based on Traditional Machine Learning -- 1.3.2 Plant Disease Detection Method Based on Deep Learning Technology -- 1.3.2.1 Disease Recognition Methods -- 1.3.2.2 Disease Segmentation Methods -- 1.3.2.3 Disease Detection Methods -- 1.3.3 Plant Disease Dataset -- 1.3.4 Problems of Current Disease Detection Methods -- 1.4 Organization of the Book -- References -- Chapter 2: Deep Learning Technology -- 2.1 Neural Networks -- 2.1.1 Neurons and Neural Network Models
001481050 5058_ $$a2.1.1.1 Neuron Model -- 2.1.1.2 Neural Network Model -- 2.1.2 Forward and Backward Propagation -- 2.1.2.1 Forward Propagation -- 2.1.2.2 Backward Propagation -- 2.1.2.3 Gradient Descent -- 2.1.3 Activation Function -- 2.1.3.1 Sigmoid Activation Function -- 2.1.3.2 Tanh Activation Function -- 2.1.3.3 ReLU Activation Function -- 2.1.4 Loss Function -- 2.1.4.1 Cross-Entropy Loss Function -- 2.1.4.2 Mean Squared Error Loss Function -- 2.1.4.3 Smooth L1 Loss Function -- 2.1.5 Optimizer -- 2.1.5.1 Momentum Stochastic Gradient Descent -- 2.2 Convolutional Neural Networks
001481050 5058_ $$a2.2.1 Neural Networks and Image Processing -- 2.2.2 Convolutional Neural Network -- 2.2.3 Convolutional Layer -- 2.2.4 Pooling Layer -- 2.3 Image Recognition Technology -- 2.3.1 History of Image Recognition Technology -- 2.3.2 Image Recognition Based on Traditional Machine Learning -- 2.3.2.1 Image Preprocessing -- Image Denoising -- Image Enhancement -- Image Segmentation -- 2.3.2.2 Traditional Image Feature Extraction -- 2.3.2.3 Classifiers Based on Traditional Machine Learning -- KNN (K-Nearest Neighbor) -- SVM (Support Vector Machines)
001481050 5058_ $$a2.3.3 Image Recognition Based on Deep-Learning Technology -- 2.3.3.1 Naive Convolutional Neural Network: AlexNet and VGGNet -- 2.3.3.2 Wider Convolutional Neural Networks: GoogLeNet -- 2.3.3.3 Deeper Convolutional Neural Networks: ResNet, DenseNet -- 2.3.3.4 Light-Weight Convolutional Neural Network: MobileNet -- 2.3.4 Introduction to Image Recognition Based on Few-Shot Learning -- 2.4 Object Detection Methods Based on Deep Learning -- 2.4.1 Framework of Object Detection Networks -- 2.4.1.1 Feature Extraction Network (Backbone Network) -- CNN-Based Backbones
001481050 506__ $$aAccess limited to authorized users.
001481050 520__ $$aThis monograph provides a detailed and systematic introduction to the application of deep learning technology in the intelligent monitoring of crop diseases and pests. Taking 24 types of crop pests, wheat aphids, and wheat diseases with complex backgrounds as examples, a large-scale crop pest and disease dataset was constructed to provide necessary data support for the deep learning module. Various schemes for identifying and detecting large-scale crop diseases and pests based on deep convolutional neural network technology have also been proposed. This book can be used as a reference for teachers and students majoring in agriculture, computer science, artificial intelligence, intelligent science and technology, and other related fields in higher education institutions. It can also be used as a reference book for researchers in fields such as image processing technology, intelligent manufacturing, and high-tech applications.
001481050 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 28, 2023).
001481050 650_0 $$aDeep learning (Machine learning)
001481050 650_0 $$aArtificial intelligence$$xAgricultural applications.$$xMedical applications$$0(DLC)sh 88003000
001481050 655_0 $$aElectronic books.
001481050 7001_ $$aJiao, Lin.
001481050 7001_ $$aLiu, Kang.
001481050 77608 $$iPrint version:$$aWang, Rujing$$tDeep Learning for Agricultural Visual Perception$$dSingapore : Springer,c2023$$z9789819949724
001481050 852__ $$bebk
001481050 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-4973-1$$zOnline Access$$91397441.1
001481050 909CO $$ooai:library.usi.edu:1481050$$pGLOBAL_SET
001481050 980__ $$aBIB
001481050 980__ $$aEBOOK
001481050 982__ $$aEbook
001481050 983__ $$aOnline
001481050 994__ $$a92$$bISE