Decision Support System and Automated Negotiations

Description

Decision support systems are developed for integrated pest and disease management and nutrition management using open-source technologies as java, android, and low-cost hardware devices like Arduino micro controller. This text discusses the techniques to convert agricultural knowledge in the context of ontology and assist grape growers by providing this knowledge through decision support system. The key features of the book are Presents the design & development of an ontology-based decision support system for integrated crop management. Discusses the techniques to convert agricultural knowledge in text to ontology. Focuses on an extensive study of various e-Negotiation protocols for automated negotiations Provides an architecture for predicting the opponent’s behaviour and various factors which affect the process of negotiation. The text is primarily written for graduate students, professionals, and academic researchers working in the fields of computer science and engineering, agricultural science, and information technology. Cover Half Title Title Page Copyright Page Table of Contents Preface Author Bio Part 1: Decision Support System Chapter 1: Decision Support System for Agriculture 1.1 Problem Statement 1.2 Motivation 1.3 Grape Production in India 1.4 Decision Support Systems 1.5 Integrated Pest Management 1.6 Integrated Nutrient Management and Smart Irrigation 1.7 Government of India Initiatives for Decision Support to Farmers Chapter 2: Ontology Development 2.1 Building Ontology from Text 2.1.1 Building Agricultural Ontology 2.2 Ontology Merging 2.3 Use of Image Processing for Pests and Diseases Detection 2.4 Decision Support Systems and Expert Systems 2.5 Nutrition Management 2.6 Ontology Management 2.7 Irrigation Management Systems Chapter 3: Building Vineyards Knowledge Base 3.1 Introduction 3.1.1 Ontology 3.1.1.1 Upper Ontology 3.1.1.2 Domain Ontology 3.1.2 AGROVOC 3.1.3 Web Ontology Language 3.1.4 Protégé 3.1.5 Jena API 3.2 Ontology Construction from Text 3.2.1 Relationship Extraction 3.2.2 Pest Description Ranking 3.3 IPM Ontology Construction 3.4 Evaluation of Grape-Pest-Management Ontology 3.5 Conclusions Chapter 4: Knowledge Bases: Making It All Together 4.1 Introduction 4.1.1 Literature Survey 4.2 IPM Ontology Merging with IPMOntoShare 4.3 Implementation and Evaluation 4.4 Conclusions Chapter 5: PDMGrapes: Forecasting Occurrence of Pests on Grapes 5.1 Introduction 5.1.1 Literature Survey 5.1.2 Using Fuzzy Logic Based Inference Engine 5.1.2.1 Implementation 5.1.2.1.1 jFuzzyLogic 5.1.2.1.2 JSOUP Library 5.1.2.1.3 Adding Rules to Inference Engine 5.1.3 Semantic Web Rule Language 5.1.4 Extending Ontology 5.1.5 Sensor Ontology 5.1.6 Conclusions 5.2 Disease Detection on Grape Crop 5.2.1 Introduction 5.2.2 Literature Survey 5.2.3 Disease Detection Using Image Processing Techniques 5.2.4 Implementation and Analysis 5.2.4.1 OpenCV 5.2.5 Pest and Disease Management Using PDMGrapes 5.2.6 Smartphone App 5.2.6.1 Android Studio 5.2.7 Existing Practice versus Developed Decision Support System 5.2.8 Performance Evaluation of PDMGrapes Chapter 6: Nutrient Management 6.1 Introduction 6.1.1 Literature Survey 6.2 Methods 6.2.1 Building NM Ontology 6.2.2 Mapping of Concepts from Decision Tree for Ontology Evolution 6.3 Decision Support 6.3.1 Mapping of Concepts from Decision Tree for Rules Formation 6.3.2 Decision Support System 6.4 Evaluation 6.4.1 Quantitative Analysis 6.4.2 Qualitative Analysis 6.5 Conclusions Chapter 7: Irrigation Management 7.1 Introduction 7.2 Literature Survey 7.3 Methodology 7.3.1 Building Vineyard Ontology 7.3.2 Smart Irrigation Ontology 7.4 Conclusions Chapter 8: Crop Suitability Recommendation 8.1 Introduction 8.2 Design and Implementation 8.2.1 Crop Recommendation 8.2.1.1 Fertilizer Recommendation 8.3 Performance Evaluation 8.4 Conclusions Part 2: Automated Negotiation Chapter 9: Negotiation 9.1 Need of Negotiation Automation 9.2 History of Electronic Negotiation 9.3 Negotiation and Latest Technologies 9.4 Multilateral Negotiations and Related Issues 9.5 Negotiations and Multiple Strategies 9.6 Organization of Part 2 Chapter 10: Literature Survey 10.1 Motivation for the Survey 10.2 Auctions versus Negotiations 10.3 Negotiation Types 10.3.1 Distributive Negotiations – The Fixed Pie 10.3.2 Integrative Negotiations – Everybody Wins Something (Usually) 10.4 Negotiation Tactics 10.5 Existing Systems and Issues 10.5.1 Survey on Prediction Part in E-Negotiation 10.5.2 Survey on Multilateral Negotiations and Issues 10.5.3 Survey on Latest Technologies in E-Negotiation 10.6 Survey on Multiple Strategies 10.6.1 Literature Survey on Strategy Selection 10.6.2 Literature Survey on Deployment Techniques 10.6.3 Literature Survey on Acceptance Model 10.7 Comparison of Different Methods with their Strengths and Limitations Chapter 11: Problem Statement and Scope 11.1 Problem Definition 11.2 Scope and Purpose 11.3 Constraints 11.4 Feasibility Study 11.5 The Gap and Analysis 11.6 Mathematical Modeling 11.6.1 Mathematical Model for Multiple Strategies Chapter 12: Methodology 12.1 Design 12.1.1 System Architecture Bilateral E-Negotiation and Opponent’s Behavior Prediction based on Decision Support Systems 12.1.2 System Architecture Automated Multilateral Negotiation with Linear Programming 12.1.3 System Architecture E-Negotiation Using Rule-Based and Case-Based Reasoning (earlier published in www.igi-global.com) 12.1.4 System Architecture Multi Strategy Based E-Negotiation 12.2 System Details 12.2.1 Proposed Negotiation Protocol 12.2.2 Reservation Value 12.2.2.1 Running Negotiation Session 12.2.2.2 E-Negotiation Terminologies (in Multi-Strategy Base) 12.2.3 Generic Framework for Automated Negotiation 12.3 UML Diagrams 12.3.1 Use-Case Diagram 12.3.2 Class Diagram 12.3.3 Activity Diagram 12.3.4 Sequence Diagram 12.3.5 Software and Hardware Requirements Chapter 13: Results and Analysis 13.1 Negotiation Scenario 13.1.1 Agent Selection 13.1.2 Negotiations 13.2 Prediction Algorithm 13.2.1 Working of Multilateral Model 13.2.2 Working of Multi Strategy Selection Model and Algorithm 13.3 Analysis 13.3.1 For Bilateral System with Opponents Behavior Prediction 13.3.2 Multilateral Negotiations 13.3.3 Negotiation Using RBR and CBR and Improved Memory Utilization and Response Time 13.3.4 Multi-Strategy Selection Chapter 14: Conclusion 14.1 Future Work (According to Earlier Published Results on Web Source www.ijert.org) 14.2 Limitations of Research 14.3 Concluding Remarks Bibliography Index