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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
Produktdetails
Advances in Learning Analytics for Intelligent Cloud-IoT Systems
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.
Inhaltsangabe
Preface xiii
1 A Study on Various Machine Learning Algorithms and Their Role in Agriculture 1 Kalpana Rangra and Amitava Choudhury
1.1 Introduction 1
1.2 Conclusions 9
2 Smart Farming Using Machine Learning and IoT 13 Alo Sen, Rahul Roy and Satya Ranjan Dash
2.1 Introduction 14
2.2 Related Work 15
2.3 Problem Identification 22
2.4 Objective Behind the Integrated Agro-IoT System 23
2.5 Proposed Prototype of the Integrated Agro-IoT System 23
2.6 Hardware Component Requirement for the Integrated Agro-IoT System 26
2.7 Comparative Study Between Raspberry Pi vs Beaglebone Black 30
2.8 Conclusions 31
2.9 Future Work 32
3 Agricultural Informatics vis-à-vis Internet of Things (IoT): The Scenario, Applications and Academic Aspects--International Trend & Indian Possibilities 35 P.K. Paul
3.8 Agricultural Informatics & IoT: The Scenario 45
3.9 IoT in Agriculture: Requirement, Issues & Challenges 49
3.10 Development, Economy and Growth: Agricultural Informatics Context 50
3.11 Academic Availability and Potentiality of IoT in Agricultural Informatics: International Scenario & Indian Possibilities 51
3.12 Suggestions 60
3.13 Conclusion 60
4 Application of Agricultural Drones and IoT to Understand Food Supply Chain During Post COVID-19 67 Pushan Kumar Dutta and Susanta Mitra
4.1 Introduction 68
4.2 Related Work 69
4.3 Smart Production With the Introduction of Drones and IoT 72
4.4 Agricultural Drones 75
4.5 IoT Acts as a Backbone in Addressing COVID-19 Problems in Agriculture 77
4.6 Conclusion 81
5 IoT and Machine Learning-Based Approaches for Real Time Environment Parameters Monitoring in Agriculture: An Empirical Review 89 Parijata Majumdar and Sanjoy Mitra
5.1 Introduction 90
5.2 Machine Learning (ML)-Based IoT Solution 90
5.3 Motivation of the Work 91
5.4 Literature Review of IoT-Based Weather and Irrigation Monitoring for Precision Agriculture 91
5.5 Literature Review of Machine Learning-Based Weather and Irrigation Monitoring for Precision Agriculture 92
5.6 Challenges 112
5.7 Conclusion and Future Work 113
6 Deep Neural Network-Based Multi-Class Image Classification for Plant Diseases 117 Alok Negi, Krishan Kumar and Prachi Chauhan
6.1 Introduction 117
6.2 Related Work 119
6.3 Proposed Work 121
6.4 Results and Evaluation 124
6.5 Conclusion 127
7 Deep Residual Neural Network for Plant Seedling Image Classification 131 Prachi Chauhan, Hardwari Lal Mandoria and Alok Negi
7.1 Introduction 131
7.2 Related Work 136
7.3 Proposed Work 139
7.4 Result and Evaluation 142
7.5 Conclusion 144
8 Development of IoT-Based Smart Security and Monitoring Devices for Agriculture 147 Himadri Nath Saha, Reek Roy, Monojit Chakraborty and Chiranmay Sarkar
1 A Study on Various Machine Learning Algorithms and Their Role in Agriculture 1 Kalpana Rangra and Amitava Choudhury
1.1 Introduction 1
1.2 Conclusions 9
2 Smart Farming Using Machine Learning and IoT 13 Alo Sen, Rahul Roy and Satya Ranjan Dash
2.1 Introduction 14
2.2 Related Work 15
2.3 Problem Identification 22
2.4 Objective Behind the Integrated Agro-IoT System 23
2.5 Proposed Prototype of the Integrated Agro-IoT System 23
2.6 Hardware Component Requirement for the Integrated Agro-IoT System 26
2.7 Comparative Study Between Raspberry Pi vs Beaglebone Black 30
2.8 Conclusions 31
2.9 Future Work 32
3 Agricultural Informatics vis-à-vis Internet of Things (IoT): The Scenario, Applications and Academic Aspects--International Trend & Indian Possibilities 35 P.K. Paul
3.8 Agricultural Informatics & IoT: The Scenario 45
3.9 IoT in Agriculture: Requirement, Issues & Challenges 49
3.10 Development, Economy and Growth: Agricultural Informatics Context 50
3.11 Academic Availability and Potentiality of IoT in Agricultural Informatics: International Scenario & Indian Possibilities 51
3.12 Suggestions 60
3.13 Conclusion 60
4 Application of Agricultural Drones and IoT to Understand Food Supply Chain During Post COVID-19 67 Pushan Kumar Dutta and Susanta Mitra
4.1 Introduction 68
4.2 Related Work 69
4.3 Smart Production With the Introduction of Drones and IoT 72
4.4 Agricultural Drones 75
4.5 IoT Acts as a Backbone in Addressing COVID-19 Problems in Agriculture 77
4.6 Conclusion 81
5 IoT and Machine Learning-Based Approaches for Real Time Environment Parameters Monitoring in Agriculture: An Empirical Review 89 Parijata Majumdar and Sanjoy Mitra
5.1 Introduction 90
5.2 Machine Learning (ML)-Based IoT Solution 90
5.3 Motivation of the Work 91
5.4 Literature Review of IoT-Based Weather and Irrigation Monitoring for Precision Agriculture 91
5.5 Literature Review of Machine Learning-Based Weather and Irrigation Monitoring for Precision Agriculture 92
5.6 Challenges 112
5.7 Conclusion and Future Work 113
6 Deep Neural Network-Based Multi-Class Image Classification for Plant Diseases 117 Alok Negi, Krishan Kumar and Prachi Chauhan
6.1 Introduction 117
6.2 Related Work 119
6.3 Proposed Work 121
6.4 Results and Evaluation 124
6.5 Conclusion 127
7 Deep Residual Neural Network for Plant Seedling Image Classification 131 Prachi Chauhan, Hardwari Lal Mandoria and Alok Negi
7.1 Introduction 131
7.2 Related Work 136
7.3 Proposed Work 139
7.4 Result and Evaluation 142
7.5 Conclusion 144
8 Development of IoT-Based Smart Security and Monitoring Devices for Agriculture 147 Himadri Nath Saha, Reek Roy, Monojit Chakraborty and Chiranmay Sarkar
8.1 Introduction 148
8.2 Background & Related Works 150
8.3 Proposed Model 155
8.4 Methodology 160
8.5 Performance Analysis 165
8.6 Future Research Direction 166
8.7 Conclusion 167
9 An Integrated Application of IoT-Based
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