Sensing Technologies for Field and In-House Crop Production: Technology Review and Case Studies (Smart Agriculture, 7)

Preface
Contents
1 A Review of Three-Dimensional Multispectral Imaging in Plant Phenotyping
	1.1 Introduction
	1.2 Three-Dimensional and Multispectral Imaging
		1.2.1 Three-Dimensional Imaging
		1.2.2 Multispectral Imaging
	1.3 Three-Dimensional Multispectral Imaging Systems in Plant Phenotyping
		1.3.1 System Structure
		1.3.2 Factors Affecting Imaging
	1.4 Commercial Three-Dimensional Multispectral Imaging Systems
	1.5 Conclusion and Outlook
	References
2 Recent Advances in Soil Nutrient Monitoring: A Review
	2.1 Introduction
	2.2 Basic Concepts of Soil Nutrients
	2.3 Soil Nutrient Measurement Methods
		2.3.1 Laboratory Analysis Methods
		2.3.2 Spectroscopy Methods
		2.3.3 Electrochemical Methods
	2.4 Summary and Outlook
	References
3 Plant Phenotyping Robot Platform
	3.1 Overview of Crop Phenotyping Robots
	3.2 Design Requirements of Crop Phenotyping Robot Platform
	3.3 Research Status of Crop Phenotyping Robots
		3.3.1 Robot Platform for Indoor Crop Phenotype
		3.3.2 Field Crop Phenotyping Robot Platform
	3.4 A Case Study of Tomato Plant Phenotype Inspection Robot
		3.4.1 Hardware Components
		3.4.2 Software Design
		3.4.3 Extraction of Phenotype Information
	References
4 Autonomous Crop Image Acquisition System Based on ROS System
	4.1 Overview of the Indoor Crop Inspection System
		4.1.1 Research Status of Indoor Crop Inspection Systems
		4.1.2 Indoor Crop Inspection System Overall Design
	4.2 System Hardware Design
		4.2.1 Main Control Module
		4.2.2 Motion Control Module
		4.2.3 Image Acquisition Module
		4.2.4 Real-Time Monitoring Module
	4.3 Indoor Crop Inspection System Testing
		4.3.1 Feasibility Testing of the System
		4.3.2 System Stability Testing and Application
	4.4 Extraction of Three-Dimensional Distribution of Potato Plant CWSI
		4.4.1 Image Data Processing
		4.4.2 Extraction of Crop Canopy CWSI
	4.5 Conclusion
	References
5 SeedingsNet: Field Wheat Seedling Density Detection Based on Deep Learning
	5.1 Introduction
	5.2 Materials and Methods
		5.2.1 Experimental Field and Data Collection
		5.2.2 Data Preprocessing
		5.2.3 Detection Model
		5.2.4 Performance Evaluation
	5.3 Results
		5.3.1 Results of Training Three Models
		5.3.2 Comparison Results of Input Images with Different Processing Methods
	5.4 Conclusion
	References
6 Wheat Lodging Detection Using Smart Vision-Based Method
	6.1 Introduction
		6.1.1 Remote Sensing Role in Crop Lodging Detection
		6.1.2 Lodging Severity
		6.1.3 Changes in Wheat Lodging Over Time
		6.1.4 Imbalance Challenge
	6.2 Material and Methods
		6.2.1 Image Data Gathering
		6.2.2 Generating Auto Dataset
		6.2.3 Feature Extraction
		6.2.4 Changing Loss Function
		6.2.5 Time-Dependent Modeling of Wheat Lodging
	6.3 Results and Discussion
		6.3.1 Classification Results of CNNs and Classic Classifiers
		6.3.2 LSTM Based Model Classification Results
		6.3.3 Changing the Loss Function in Wheat Lodging Classification
	6.4 Conclusion
	References
7 Design, Construction, and Experiment-Based Key Parameter Determination of Auto Maize Seed Placement System
	7.1 Introduction
	7.2 Materials and Methods
		7.2.1 Introduction to the Auto Seeding Machine and Seed Placement System
		7.2.2 The Overall Procedure of Key Parameter Determination for Seed Placement System
		7.2.3 Bottom Tilt Angle of Seed Container
		7.2.4 Brush Spinning and Linear Speed Configuration
		7.2.5 Seed Dropping Height Study
	7.3 Results and Discussion
		7.3.1 Bottom Plate Angle Determination of Seed Container
		7.3.2 Brush Spinning and Horizontal Speed Configuration
		7.3.3 Seed Dropping Height
	7.4 Conclusions
	References
8 Development and Test of an Auto Seedling Detection System
	8.1 Introduction
	8.2 System Design
		8.2.1 Auto Seedling Counting System Design
	8.3 Materials and Methods
		8.3.1 Image Acquisition
		8.3.2 Color Images Correction
		8.3.3 Visual Seeding Counting
		8.3.4 Images Color Segmentation
		8.3.5 Image-Block Seedlings Detection (IBSD) Algorithm
		8.3.6 Evaluation
	8.4 Results and Discussion
		8.4.1 Effect of Seedling Growth days
		8.4.2 Effect of Illumination Condition
	8.5 Conclusion
	References