Agricultural Informatics

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 References

12 8 Development of IoT-Based Smart Security and Monitoring Devices for Agriculture 8.1 Introduction 8.2 Background & Related Works 8.3 Proposed Model 8.4 Methodology 8.5 Performance Analysis 8.6 Future Research Direction 8.7 Conclusion References

13 9 An Integrated Application of IoT-Based WSN in the Field of Indian Agriculture System Using Hybrid Optimization Technique and Machine Learning 9.1 Introduction 9.2 Literature Review 9.3 Proposed Hybrid Algorithms (GA-MWPSO) 9.4 Reliability Optimization and Coverage Optimization Model 9.5 Problem Description 9.6 Numerical Examples, Results and Discussion 9.7 Conclusion References

14 10 Decryption and Design of a Multicopter Unmanned Aerial Vehicle (UAV) for Heavy Lift Agricultural Operations 10.1 Introduction 10.2 History of Multicopter UAVs 10.3 Basic Components of Multicopter UAV 10.4 Working and Control Mechanism of Multicopter UAV 10.5 Design Calculations and Selection of Components 10.6 Conclusion References

15 11 IoT-Enabled Agricultural System Application, Challenges and Security Issues 11.1 Introduction 11.2 Background & Related Works 11.3 Challenges to Implement IoT-Enabled Systems 11.4 Security Issues and Measures 11.5 Future Research Direction 11.6 Conclusion References

16 12 Plane Region Step Farming, Animal and Pest Attack Control Using Internet of Things 12.1 Introduction 12.2 Proposed Work 12.3 Irrigation Methodology 12.4 Sensor Connection Using Internet of Things 12.5 Placement of Sensor in the Field 12.6 Conclusion References

17 Index

18 End User License Agreement

1 Cover

2 Table of Contents

3 Title page

4 Copyright

5 Preface

6 Begin Reading

7 Index

8 End User License Agreement

1 Chapter 1 Figure 1.1 Layers and connection of a feed-forward back propagation ANN [2]. Figure 1.2 Fuzzy cluster membership function representation in various field [9]... Figure 1.3 Decision tree structure for crop details prediction [4].

2 Chapter 2 Figure 2.1 Applications of agro-IoT. Figure 2.2 Flow chart of step by step process of agro-IoT farming. Figure 2.3 Proposed prototype model for integrated Agro-IoT system. Figure 2.4 Proposed image processing method to detect pest and weed. Figure 2.5 Proposed image processing method to detect fire region. Figure 2.6 Beaglebone black.

3 Chapter 3 Figure 3.1 Stakeholders of Agro Informatics and allied Nomenclatures. Figure 3.2 The IoT Architecture at a glance. Figure 3.3 The gateway for IoT and its stakeholders.Figure 3.4 Major components of IoT.

4 Chapter 4Figure 4.1 Applying drone technology to precision agriculture.Figure 4.2 Four technologies of the IoT technology management used in the proces...Figure 4.3 Overall architecture of the precision agriculture environment.

5 Chapter 5Figure 5.1 IoT-based intelligent agriculture monitoring.Figure 5.2 Mode of work in IoT-based weather and irrigation monitoring.Figure 5.3 Mode of work in ML-based weather and irrigation monitoring.Figure 5.4 Data storage in IoT-based weather and irrigation monitoring.Figure 5.5 Data storage in ML-based weather and irrigation monitoring.

6 Chapter 6Figure 6.1 Proposed work flow chart.Figure 6.2 Sample images.Figure 6.3 CNN architecture.Figure 6.4 Training and Validation Accuracy of CNNFigure 6.5 Training and validation loss of CNN.Figure 6.6 Confusion matrix (without normalization).Figure 6.7 Confusion matrix (with normalization).

7 Chapter 7Figure 7.1 CNN architecture.Figure 7.2 Left: regular block and Right: residual block.Figure 7.3 General approach for automated classification of plant block.Figure 7.4 Distribution of classes during training.Figure 7.5 Distribution of classes during validation.Figure 7.6 Annotated image.Figure 7.7 Training and validation accuracy of ResNet-50 using batch normalizati...Figure 7.8 Training and validation loss of ResNet-50 using batch normalization.Figure 7.9 Training and validation accuracy of ResNet-50 without batch normaliza...Figure 7.10 Training and validation loss of ResNet-50 without batch normalizatio...

8 Chapter 8Figure 8.1 Framework of proposed system.Figure 8.2 Flowchart.Figure 8.3 Snapshots of (a) home screen of phone application when buzzer and LED...Figure 8.4 Snapshots of readings of sensors: (a) pH reading of soil using ph sen...Figure 8.5 Some images of tomatoes taken using UAV. (a) image of healthy tomatoe...Figure 8.6 Graph shows the comparison of the production of tomatoes before and a...

9 Chapter 9Figure 9.1 Block diagram of GA-MWPSO algorithm.Figure 9.2 (a) Plotting of coverage ratio vs. coverage range without using optim...

10 Chapter 10Figure 10.1 Different types of UAV.Figure 10.2 Early-stage development of multicopterFigure 10.3 Recent successful multicopter products available in the market.Figure 10.4 Basic system and components of multicopter UAV.Figure 10.5 Common frame configurations in multirotor UAV platform.Figure 10.6 Airframe of multicopter UAV.Figure 10.7 LiPo 16,000 mAh batteries with anti-spark plug connectors.Figure 10.8 Discharge curve of LiPo battery (Source: http://learningrc.com).Figure 10.9 6S voltage sensor (Buzzer).Figure 10.10 Carbon fiber two blade propeller (a) 22X55 carbon fibre heavy duty ...Figure 10.11 BLDC motor mounted on arm.Figure 10.12 Detailed connection diagram of ESC.Figure 10.13 18 channel remote controller transmitter and receiver.Figure 10.14 Control mode of RC transmitter.Figure 10.15 Pixhawk open-source flight controller.Figure 10.16 Wireless communication of GCS (Source: [30]).Figure 10.17 Field setup of the ground control system.Figure 10.18 Screenshots of Mission Planner GCS software.Figure 10.19 Radio telemetry module.Figure 10.20 Alpha GPS 13 module for multicopter UAV.Figure 10.21 An octacopter in hovering state.Figure 10.22 Movement of an octacopter (a) upward movement, (b) forward movement...Figure 10.23 Movement of an octacopter (a) rightward movement, (b) Yaw movement.Figure 10.24 Various fuselage configuration (a) Plus or I configuration, (b) X-c...Figure 10.25 Propeller with different number of blade (a) single-blade propeller...Figure 10.26 Performance graphs of different number of blades (a) Thrust v/s Pow...Figure 10.27 Alloy type X-configurations and their geometry parameters.