Agricultural Informatics. Группа авторов

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      1  Cover

      2  Title page

      3  Copyright

      4  Preface

      5  1 A Study on Various Machine Learning Algorithms and Their Role in Agriculture 1.1 Introduction 1.2 Conclusions References

      6  2 Smart Farming Using Machine Learning and IoT 2.1 Introduction 2.2 Related Work 2.3 Problem Identification 2.4 Objective Behind the Integrated Agro-IoT System 2.5 Proposed Prototype of the Integrated Agro-IoT System 2.6 Hardware Component Requirement for the Integrated Agro-IoT System 2.7 Comparative Study Between Raspberry Pi vs Beaglebone Black 2.8 Conclusions 2.9 Future Work References

      7  3 Agricultural Informatics vis-à-vis Internet of Things (IoT): The Scenario, Applications and Academic Aspects — International Trend & Indian Possibilities 3.1 Introduction 3.2 Objectives 3.3 Methods 3.4 Agricultural Informatics: An Account 3.5 Agricultural Informatics & Technological Components: Basics & Emergence 3.6 IoT: Basics and Characteristics 3.7 IoT: The Applications & Agriculture Areas 3.8 Agricultural Informatics & IoT: The Scenario 3.9 IoT in Agriculture: Requirement, Issues & Challenges 3.10 Development, Economy and Growth: Agricultural Informatics Context 3.11 Academic Availability and Potentiality of IoT in Agricultural Informatics: International Scenario & Indian Possibilities 3.12 Suggestions 3.13 Conclusion References

      8  4 Application of Agricultural Drones and IoT to Understand Food Supply Chain During Post COVID-19 4.1 Introduction 4.2 Related Work 4.3 Smart Production With the Introduction of Drones and IoT 4.4 Agricultural Drones 4.5 IoT Acts as a Backbone in Addressing COVID-19 Problems in Agriculture 4.6 Conclusion References

      9  5 IoT and Machine Learning-Based Approaches for Real Time Environment Parameters Monitoring in Agriculture: An Empirical Review 5.1 Introduction 5.2 Machine Learning (ML)-Based IoT Solution 5.3 Motivation of the Work 5.4 Literature Review of IoT-Based Weather and Irrigation Monitoring for Precision Agriculture 5.5 Literature Review of Machine Learning-Based Weather and Irrigation Monitoring for Precision Agriculture 5.6 Challenges 5.7 Conclusion and Future Work References

      10  6 Deep Neural Network-Based Multi-Class Image Classification for Plant Diseases 6.1 Introduction 6.2 Related Work 6.3 Proposed Work 6.4 Results and Evaluation 6.5 Conclusion References

      11  7 Deep Residual Neural Network for Plant Seedling Image Classification 7.1 Introduction 7.2 Related Work 7.3 Proposed Work 7.4 Result and Evaluation 7.5 Conclusion СКАЧАТЬ