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      Agricultural Informatics

      Automation Using the IoT and Machine Learning

      AvAmitava Choudhury,Arindam Biswas

      Inbunden, Engelska, 2021

      Del i serien Advances in Learning Analytics for Intelligent Cloud-IoT Systems

      2 206 kr

      Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

      Beskrivning

      Despite the increasing population (the Food and Agriculture Organization of the United Nations estimates 70% more food will be needed in 2050 than was produced in 2006), issues related to food production have yet to be completely addressed. In recent years, Internet of Things technology has begun to be used to address different industrial and technical challenges to meet this growing need. These Agro-IoT tools boost productivity and minimize the pitfalls of traditional farming, which is the backbone of the world's economy. Aided by the IoT, continuous monitoring of fields provides useful and critical information to farmers, ushering in a new era in farming. The IoT can be used as a tool to combat climate change through greenhouse automation; monitor and manage water, soil and crops; increase productivity; control insecticides/pesticides; detect plant diseases; increase the rate of crop sales; cattle monitoring etc.Agricultural Informatics: Automation Using the IoT and Machine Learning focuses on all these topics, including a few case studies, and they give a clear indication as to why these techniques should now be widely adopted by the agriculture and farming industries.

      Produktinformation

      • Utgivningsdatum:2021-03-30
      • Mått:10 x 10 x 10 mm
      • Vikt:454 g
      • Format:Inbunden
      • Språk:Engelska
      • Serie:Advances in Learning Analytics for Intelligent Cloud-IoT Systems
      • Antal sidor:304
      • Förlag:John Wiley & Sons Inc
      • ISBN:9781119768845

      Utforska kategorier

      • Agronomi och lantbruk inom Naturvetenskap och teknik
      • Artificiell intelligens inom Data och IT

      Mer om författaren

      Amitava Choudhury PhD is an assistant professor in the school of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.Arindam Biswas PhD is an assistant professor in School of Mines and Metallurgy at Kazi Nazrul University, Asansol, West Bengal, India.Manish Prateek PhD is Professor and Dean, School of Computer Science, at the University of Petroleum and Energy Studies, Dehradun, India.Amlan Chakrabarti PhD is a Full Professor in the A.K. Choudhury School of Information Technology at the University of Calcutta.

      Innehållsförteckning

      • Preface xiii1 A Study on Various Machine Learning Algorithms and Their Role in Agriculture 1Kalpana Rangra and Amitava Choudhury1.1 Introduction 11.2 Conclusions 92 Smart Farming Using Machine Learning and IoT 13Alo Sen, Rahul Roy and Satya Ranjan Dash2.1 Introduction 142.2 Related Work 152.3 Problem Identification 222.4 Objective Behind the Integrated Agro-IoT System 232.5 Proposed Prototype of the Integrated Agro-IoT System 232.6 Hardware Component Requirement for the Integrated Agro-IoT System 262.7 Comparative Study Between Raspberry Pi vs Beaglebone Black 302.8 Conclusions 312.9 Future Work 323 Agricultural Informatics vis-à-vis Internet of Things (IoT): The Scenario, Applications and Academic Aspects--International Trend & Indian Possibilities 35P.K. Paul3.1 Introduction 363.2 Objectives 363.3 Methods 373.4 Agricultural Informatics: An Account 373.5 Agricultural Informatics & Technological Components: Basics & Emergence 403.6 IoT: Basics and Characteristics 413.7 IoT: The Applications & Agriculture Areas 433.8 Agricultural Informatics & IoT: The Scenario 453.9 IoT in Agriculture: Requirement, Issues & Challenges 493.10 Development, Economy and Growth: Agricultural Informatics Context 503.11 Academic Availability and Potentiality of IoT in Agricultural Informatics: International Scenario & Indian Possibilities 513.12 Suggestions 603.13 Conclusion 604 Application of Agricultural Drones and IoT to Understand Food Supply Chain During Post COVID-19 67Pushan Kumar Dutta and Susanta Mitra4.1 Introduction 684.2 Related Work 694.3 Smart Production With the Introduction of Drones and IoT 724.4 Agricultural Drones 754.5 IoT Acts as a Backbone in Addressing COVID-19 Problems in Agriculture 774.6 Conclusion 815 IoT and Machine Learning-Based Approaches for Real Time Environment Parameters Monitoring in Agriculture: An Empirical Review 89Parijata Majumdar and Sanjoy Mitra5.1 Introduction 905.2 Machine Learning (ML)-Based IoT Solution 905.3 Motivation of the Work 915.4 Literature Review of IoT-Based Weather and Irrigation Monitoring for Precision Agriculture 915.5 Literature Review of Machine Learning-Based Weather and Irrigation Monitoring for Precision Agriculture 925.6 Challenges 1125.7 Conclusion and Future Work 1136 Deep Neural Network-Based Multi-Class Image Classification for Plant Diseases 117Alok Negi, Krishan Kumar and Prachi Chauhan6.1 Introduction 1176.2 Related Work 1196.3 Proposed Work 1216.4 Results and Evaluation 1246.5 Conclusion 1277 Deep Residual Neural Network for Plant Seedling Image Classification 131Prachi Chauhan, Hardwari Lal Mandoria and Alok Negi7.1 Introduction 1317.2 Related Work 1367.3 Proposed Work 1397.4 Result and Evaluation 1427.5 Conclusion 1448 Development of IoT-Based Smart Security and Monitoring Devices for Agriculture 147Himadri Nath Saha, Reek Roy, Monojit Chakraborty and Chiranmay Sarkar8.1 Introduction 1488.2 Background & Related Works 1508.3 Proposed Model 1558.4 Methodology 1608.5 Performance Analysis 1658.6 Future Research Direction 1668.7 Conclusion 1679 An Integrated Application of IoT-Based WSN in the Field of Indian Agriculture System Using Hybrid Optimization Technique and Machine Learning 171Avishek Banerjee, Arnab Mitra and Arindam Biswas9.1 Introduction 1729.2 Literature Review 1759.3 Proposed Hybrid Algorithms (GA-MWPSO) 1779.4 Reliability Optimization and Coverage Optimization Model 1799.5 Problem Description 1819.6 Numerical Examples, Results and Discussion 1829.7 Conclusion 18310 Decryption and Design of a Multicopter Unmanned Aerial Vehicle (UAV) for Heavy Lift Agricultural Operations 189Raghuvirsinh Pravinsinh Parmar10.1 Introduction 19010.2 History of Multicopter UAVs 19210.3 Basic Components of Multicopter UAV 19310.4 Working and Control Mechanism of Multicopter UAV 20710.5 Design Calculations and Selection of Components 21010.6 Conclusion 21811 IoT-Enabled Agricultural System Application, Challenges and Security Issues 223Himadri Nath Saha, Reek Roy, Monojit Chakraborty and Chiranmay Sarkar11.1 Introduction 22411.2 Background & Related Works 22611.3 Challenges to Implement IoT-Enabled Systems 23211.4 Security Issues and Measures 24011.5 Future Research Direction 24311.6 Conclusion 24412 Plane Region Step Farming, Animal and Pest Attack Control Using Internet of Things 249Sahadev Roy, Kaushal Mukherjee and Arindam Biswas12.1 Introduction 25012.2 Proposed Work 25412.3 Irrigation Methodology 25712.4 Sensor Connection Using Internet of Things 25912.5 Placement of Sensor in the Field 26312.6 Conclusion 267References 268Index 271
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