IoT and AI in Agriculture: Self- sufficiency in Food Production to Achieve Society 5.0 and SDG's Globally

Foreword
Preface
Acknowledgments
Contents
Chapter 1: IoT x AI: Introducing Agricultural Innovation for Global Food Production
1.1 Introduction
1.2 Key Factors for Plant Growth and Agricultural Production
1.2.1 Source of Light for Indoor Farming Systems
1.2.2 IoT-Based Precision Irrigation Systems
1.2.3 IoT-Based Water Purification
1.3 Artificial Intelligence for Smart Agriculture
1.4 Agricultural Machinery Automation
1.4.1 Farm Automation Technology
1.4.2 Agricultural Robot Navigation System
1.4.3 Automation in Orchard Management
1.5 Conclusion
References
Chapter 2: Strategic Short Note: Transforming Controlled Environment Plant Production Toward Circular Bioeconomy Systems
2.1 Introduction
2.2 Circular CEPPS
2.3 Closing Remarks
References
Chapter 3: Artificial Lighting Systems for Plant Growth and Development in Indoor Farming
3.1 Introduction
3.2 Light for Plant Growth
3.3 Light Quantity
3.3.1 Plant Photosynthesis in Response to Light
3.4 Light Quality
3.4.1 Light Energy Use Efficiency of Lamps (LUEL) and Plant Community (LUEP)
3.5 Light Duration or Photoperiod
3.5.1 Daily Light Integral (DLI)
3.6 Artificial Lights for Plants Growth
3.6.1 Incandescent Lamps
3.6.2 Fluorescent Lamps
3.6.3 High Intensity Discharged (HID) Lamps
3.6.4 Light Emitting Diodes (LED)
3.7 Effect of Using Artificial Light on Plants Grown Indoor
3.8 Conclusion
References
Chapter 4: An IoT-Based Precision Irrigation System to Optimize Plant Water Requirements for Indoor and Outdoor Farming Systems
4.1 Introduction
4.2 Precision Irrigation Management
4.2.1 IoT Technologies for Precision Irrigation Systems
4.2.1.1 IoT Networking Backbone
4.2.1.2 IoT: Irrigation Control System
4.2.1.2.1 Fuzzy Logic-Based Control System
4.2.1.2.2 Artificial Neural Network-Based Control System
4.2.1.2.3 Hybrid Control System
4.2.2 Indoor Precision Watering Management
4.2.2.1 Soil Moisture-Based Scheduling
4.2.2.2 Plant Water Status-Based Scheduling
4.2.3 Outdoor Precision Irrigation Management
4.2.3.1 IoT-Based Irrigation Scheduling
4.2.3.1.1 Soil Moisture-Based Irrigation Scheduling
4.2.3.1.2 Weather-Based Irrigation Scheduling
4.2.3.1.3 Plant-Based Irrigation Scheduling
4.3 Discussion
4.4 Concluding Remarks
References
Chapter 5: Strategic Short Note: Artificial Intelligence and Internet of Things: Application in Urban Water Management
5.1 Introduction
5.2 Methods
5.3 Results
5.4 Conclusion
References
Chapter 6: Purification of Agricultural Polluted Water Using Solar Distillation and Hot Water Producing with Continuous Monito...
6.1 Introduction
6.2 The Architecture of the Proposed IoT-Based Solar Water Distillation and Hot Water System
6.3 Basic Architecture of an IoT-Based Water Purification System
6.4 Water Purification Methods and the Possibility of Using IoT
6.5 Solar Water Distillation and Potential for Improvement with the Latest Innovations
6.5.1 Active and Passive Solar Distillation
6.5.2 Possible Innovations to Improve Vapor Generation in Solar Stills
6.6 Solar Water Heating Systems and Data Monitoring
6.7 IoT-Based Solar Water Distillation and Hot Water System
6.7.1 Study Conducted to Test the Performance of Solar Stills Under Different Improvement Strategies
6.7.2 Performance Evaluation of the Solar Still
6.7.3 Water Quality Results
6.8 Conclusions
References
Chapter 7: Long Range Wide Area Network (LoRaWAN) for Oil Palm Soil Monitoring
7.1 Introduction
7.2 Internet of Things (IoT) in Agriculture
7.3 Wireless Sensor Network in Agriculture
7.4 Soil Electrical Conductivity (EC) and pH in Oil Palm
7.5 LoRaWAN System Design for Soil EC and pH Monitoring
7.6 Signal Propagation Tests
7.6.1 Signal Propagation Test in a Young Oil Palm Plantation
7.6.2 Signal Propagation Test in an Oil Palm Nursery
7.6.3 Signal Propagation Test in an Urban Area
7.7 Calibration of EC and pH Sensors
7.8 Soil EC and pH Measurement Test
7.9 Conclusion
7.10 Recommendation
References
Chapter 8: Strategic Short Note: Application of Smart Machine Vision in Agriculture, Forestry, Fishery, and Animal Husbandry
8.1 Introduction
8.2 Tasks of Smart Machine Vision
8.3 The Components of Smart Machine Vision
8.4 Examples of Smart Machine Vision in Agriculture, Forestry, Fishery, and Animal Husbandry
8.5 Conclusion
References
Chapter 9: Artificial Intelligence in Agriculture: Commitment to Establish Society 5.0: An Analytical Concepts Mapping for Dee...
9.1 Introduction
9.2 AI Mapping Concept
9.3 Deep Learning (DL) and Neural Networks (NNs)
9.3.1 Principles of the ANN Learning Process
9.4 DL at the Edge with CNNs
9.5 Deep Learning Algorithms for Object Detection
9.6 Conclusions: DL Serving a New Agriculture Revolution
References
Chapter 10: Potentials of Deep Learning Frameworks for Tree Trunk Detection in Orchard to Enable Autonomous Navigation System
10.1 Introduction
10.2 Materials and Methods
10.2.1 Field Data Collection
10.2.2 Data Preparation
10.2.2.1 Image Frames from Videos
10.2.2.2 Labeling
10.2.2.3 Data Augmentation
10.2.2.4 Data Splitting
10.2.3 Training Model Structure
10.2.3.1 Faster R-CNN (Faster Region Based Convolutional Neural Networks)
10.2.3.1.1 Convolutional Layers
10.2.3.1.2 RPN
10.2.3.1.3 ROI Pooling
10.2.3.1.4 Classification
10.2.3.2 YOLO (You Only Look Once)
10.2.3.3 CenterNet
10.2.4 Training Platform and Validation
10.2.5 Model Testing
10.3 Results
10.3.1 Faster R-CNN Testing
10.3.2 YOLO Testing
10.3.3 CenterNet Testing
10.4 Discussion
10.5 Conclusion
References
Chapter 11: Real-Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT
11.1 Introduction
11.2 Related Works
11.2.1 You Only Look Once (YOLO)
11.2.2 Simple Online and Real Time Tracking with Convolutional Neural Networks (CNNs)
11.2.3 Fruit Detection Using YOLO
11.2.4 Real-Time Fruit Counting Using YOLO and an Object Tracking Algorithm
11.3 Materials and Methods
11.3.1 Field Data Collection
11.3.2 Data Preparation
11.3.2.1 Videos Were Converted into Image Frames
11.3.2.2 Labelling
11.3.2.3 Data Augmentation
11.3.3 Data Splitting
11.3.4 Setting the Target Metric
11.3.5 Evaluation Metrics for the Detection
11.3.6 Components of the YOLOv4 Models
11.3.6.1 Cross-Stage Partial (CSP) Connection
11.3.6.2 CSPDarknet53: YOLOv4 and YOLOv4-CSP´s Backbone
11.3.6.3 YOLOv4-tiny´s Backbone: CSPOSANet
11.3.6.4 Why Were Leaky Rectified Linear Unit and Mish Used as the Activation Functions for the YOLOv4 Models?
11.3.6.5 YOLOv4´s Neck: Path Aggregation Network (PANet)
11.3.6.6 YOLOv4´s Plug-In Module: Spatial Pyramid Pooling (SPP)
11.3.7 Training, Validation, and Optimization
11.3.7.1 Stage-1 Training
11.3.7.2 Hyperparameters
11.3.7.3 Stage-2 Training
11.3.7.4 Error Analysis
11.3.8 Model Comparison
11.3.9 Pear Counting Using the Selected YOLOv4 Model and Deep SORT
11.3.10 Evaluation Metrics for the Pear Counting
11.4 Results and Discussion
11.4.1 Training Details
11.4.2 Model Performance Comparison
11.4.3 Speed-Accuracy Tradeoff in the YOLOv4 Models
11.4.4 Average Precision at Different Thresholds
11.4.5 FLOPS Analysis
11.4.6 YOLOv4 Models on Illumination and Occlusion Challenges
11.4.7 Comparison of the Pear Counting Methods
11.4.8 Breakdown of the False Negative Counts in the ROI Line-Based Counting
11.5 Conclusions
Appendix
References
Chapter 12: Pear Recognition System in an Orchard from 3D Stereo Camera Datasets Using Deep Learning Algorithms
12.1 Introduction
12.2 Materials and Methods
12.2.1 Field Data Collection
12.2.2 Instance Segmentation
12.2.3 Mask R-CNN
12.2.4 ZED AI Stereo Camera
12.2.5 Data Preparation
12.2.5.1 Deep Learning Environment
12.2.5.2 Video to Image Conversion
12.2.5.3 Image Annotation
12.2.6 Data Splitting
12.2.7 Training Process of Mask R-CNN
12.2.7.1 Feature Extraction (Backbone: ResNet101 + FPN)
12.2.7.2 Region Proposal Network (RPN)
12.2.7.3 ROIs and ROI-Align
12.2.7.4 Mask RCNN for Classification and Regression
12.2.7.5 Loss Function
12.2.7.6 Model Metrics Function
12.3 Results
12.3.1 Training Details
12.3.2 Evaluation of Model Metrics
12.3.3 Evaluation of Model Effectiveness
12.4 Discussion
12.5 Conclusion
References
Chapter 13: Thermal Imaging and Deep Learning Object Detection Algorithms for Early Embryo Detection: A Methodology Developmen...
13.1 Introduction
13.2 Materials and Methods
13.2.1 Thermal Imaging
13.2.1.1 Transmittance (τ)
13.2.1.2 Emissivity (ε)
13.2.1.3 Reflectance (ρ)
13.2.2 Experimental Environment
13.2.3 Thermal Image Acquisition and Radiometric Corrections
13.2.4 Deep Learning Algorithms and Analysis Environment
13.2.4.1 Models Training
13.2.4.2 Data Labeling
13.2.4.3 Data Augmentation
13.2.4.4 Model Evaluation
13.3 Results
13.3.1 Thermal Features of Incubating Eggs
13.3.2 Training Results
13.4 Discussion
13.5 Conclusions
References
Chapter 14: Strategic Short Note: Intelligent Sensing and Robotic Picking of Kiwifruit in Orchard
14.1 Introduction
14.2 Intelligent Sensing of Kiwifruit
14.3 Nondestructive Picking of Fruit
14.4 Kiwifruit Picking Robot
14.5 Conclusions
References
Chapter 15: Low-Cost Automatic Machinery Development to Increase Timeliness and Efficiency of Operation for Small-Scale Farmer...
15.1 Introduction
15.2 Current Agricultural Trends
15.2.1 Control and Navigation System
15.2.2 Vehicle Motion Models
15.2.3 Navigation Planner
15.2.4 Steering Controllers
15.2.5 Field Sensing, Recognition, and Sensor Data Fusion
15.2.6 Variable-Rate Technologies
15.2.7 Communication Protocols
15.3 Levels of Automation in Farm Machinery
15.3.1 Level 0: No Automation
15.3.1.1 Transformation of Automation on a Power Tiller
15.3.2 Level 1: Assisted Automation
15.3.2.1 Transformation of Automation for Seed/Fertilizer Broadcasting Device
15.3.3 Level-2: Partial Automation
15.3.3.1 Transformation of Partial Automation
15.3.4 Level 3: Conditional Automation
15.3.4.1 Transformation of Conditional Automation
15.3.5 Level 4: High Automation
15.3.5.1 Transformation of Automation for Machinery
15.3.6 Level 5: Full Automation
15.3.6.1 Transformation of Automation Systems
15.4 Discussion
15.5 Conclusion
References
Chapter 16: Vision-Based Leader Vehicle Trajectory Tracking for Multiple Agricultural Vehicles
16.1 Introduction
16.2 Materials and Methods
16.2.1 Leader-Follower Relative Position and Camera-Marker Sensing System
16.2.1.1 Camera Servo System
16.2.1.2 Marker Detection
16.2.1.3 Marker Positioning
16.2.1.4 Offset of Roll Angle between Camera and Marker
16.2.1.5 Transformation of Coordinates and Relative Positioning of the Marker
16.2.2 Camera Vision Data Estimation and Smoothing
16.2.3 Design of Control Law for the Leader Trajectory Tracking of Follower Vehicle
16.3 Field Experiments
16.4 Results and Discussion
16.4.1 Evaluation of Camera-Marker Observation System
16.4.2 Tracking Performance
16.5 Discussion
16.6 Conclusions
References
Chapter 17: Autonomous Robots in Orchard Management: Present Status and Future Trends
17.1 Introduction
17.2 Vision Systems for Autonomous Orchard Robots
17.3 Autonomous Robotic Pruning in the Orchards
17.4 Pollination of Orchards Crops Using Autonomous Robots
17.5 Use of Fertilizer and Liquid Chemical Application Autonomous Robots in Orchards
17.6 Autonomous Robots for Harvesting Fruits in Orchards
17.7 Discussion
17.8 Conclusions
References
Chapter 18: Strategic Short Note: Comparing Soil Moisture Retrieval from Water Cloud Model and Neural Network Using PALSAR-2 f...
18.1 Introduction
18.2 Retrieval of Soil Moisture Content in Oil Palm Fields
18.3 Conclusion
References
Chapter 19: Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Appr...
19.1 Introduction
19.2 Materials and Methods
19.2.1 Mutual Subspace Method (MSM)
19.2.2 Research Design for Classifiers and MSM
19.2.3 Field Experiment for Training and Testing with Datasets
19.2.4 Offline Recognition System
19.2.5 Online Recognition System
19.3 Results
19.3.1 Offline Recognition Performance
19.3.2 Online Recognition Performance
19.4 Discussion
19.5 Conclusion
References
Chapter 20: Basal Stem Rot Disease Classification by Machine Learning Using Thermal Images and an Imbalanced Data Approach
20.1 Introduction
20.2 Basal Stem Rot (BSR)
20.3 Detection of BSR Disease
20.3.1 Remote Sensing Techniques for G. boninense Disease Detection
20.3.2 Detection of G. boninense Using Thermal Imaging
20.4 Machine Learning in Crop Disease
20.4.1 Machine Learning in BSR Disease Detection
20.5 Imbalanced Data Approach
20.5.1 Data-Level Approaches
20.5.1.1 Under-Sampling
20.5.1.2 Oversampling
20.5.1.3 Synthetic Minority Oversampling Technique (SMOTE)
20.6 Experimental Methodology
20.6.1 Thermal Data Acquisition
20.6.1.1 Emissivity Measurement
20.6.1.2 Reflected Apparent Temperature (RAT)
20.6.1.3 Atmospheric Temperature and Humidity
20.6.1.4 Object-to-Camera Distance
20.6.2 Pre-processing of Thermal Images
20.6.3 Thermal Image Feature Extraction
20.6.4 Statistical Analysis
20.6.5 Machine Learning Approach
20.6.6 Imbalance Data Approach
20.7 Experimental Evaluation
20.7.1 Time Session Selection
20.7.2 Selection of Feature Temperature
20.7.3 Classification Analysis of Feature Temperature
20.7.4 The Effect of Classifiers on Model Performance
20.7.5 The Effect of Data Imbalance on Classification
20.8 Conclusions
References
Chapter 21: Early Detection of Plant Disease Infection Using Hyperspectral Data and Machine Learning
21.1 Basal Stem Rot (BSR) Disease due to G. boninense Infection
21.2 Hyperspectral Imaging
21.3 Machine Learning
21.4 Research Design
21.4.1 Research Area
21.4.2 Preparation of Samples
21.4.2.1 Artificial Inoculation
21.4.2.2 Polymerase Chain Reaction (PCR)
21.4.3 Hyperspectral Imaging
21.4.3.1 Image Acquisition
21.4.3.2 Spectral Extraction
21.4.3.3 Significant Bands for BSR Detection
21.5 BSR Detection
21.5.1 BSR Detection Using SVM
21.5.2 BSR Detection Using Various Types of ML
21.5.3 BSR Detection Using SVM and a Small Number of Bands
21.6 Conclusion
References
Chapter 22: Strategic Short Note: Development of an Automated Speed Sprayer for Apple Orchards in Japan
22.1 Introduction
22.1.1 Apple Production and Pesticide Spraying
22.2 Past Efforts to Automate SS and Development Goals
22.3 GNSS Application
22.4 ArUco Markers and GNSS Application
22.5 LiDAR Application
22.6 Future Tasks
Reference
Chapter 23: The Spectrum of Autonomous Machinery Development to Increase Agricultural Productivity for Achieving Society 5.0 i...
23.1 Introduction
23.2 Autonomous Machinery: Japanese Spectrum and Current State of Development
23.3 Solution Developments
23.4 Industrial Anticipation and Outlook
23.5 Market Commercialization with Geographic Application Targets
23.6 Conclusions
References