The Digital Age in Agriculture presents information related to the digital age in the agriculture sector. Agriculture is an essential activity for the continuity of life, yet is very labor-intensive and faces a wide variety of challenges. In the struggle against these difficulties, the superior features offered by technology provide important benefits. These technologies require expertise in various technical disciplines, and The Digital Age in Agriculture provides information to readers allowing them to make more informed decisions and giving them the opportunity to improve agricultural productivity.
Written by Mehmet Metin Özgüven, an expert who has conducted field studies and with a working technical knowledge of various topics pertaining to the agriculture age, this book covers many subjects important to the age of digital agriculture, including precision agriculture and livestock farming, using agricultural robots and unmanned arial vehicles in agriculture practices, and image processing and machine vision. It is an essential read for researchers, agriculture sector workers, and agricultural engineers.
Author(s): Mehmet Metin Ozguven
Publisher: CRC Press
Year: 2023
Language: English
Pages: 306
City: Berlin
Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Author
Chapter 1 Precision Agriculture
1.1 Introduction
1.2 Variability
1.3 Variable Rate Application
1.3.1 Variable Rate Application Methods
1.3.2 Comparison of Variable Rate Application Methods
1.3.3 Components in Variable Rate Application
1.3.4 Variable Rate Application Systems
1.3.5 Control Technology in Variable Rate Application
1.4 Benefits of Precision Agriculture
1.4.1 Effective Fertilizer Usage
1.4.2 Effective Pesticide Usage
1.4.3 Effective Irrigation
1.4.4 Effective Management
1.4.5 Contribution to Food Safety
1.4.6 Traceability
1.4.7 Risk Management
1.5 Components of Precision Agriculture
References
Chapter 2 Precision Livestock Farming
2.1 Importance of Animal Production
2.2 Animal Nutrition
2.3 Mechanization in Livestock
2.4 Precision Livestock Production
2.5 Precision Livestock Farming Applications
2.5.1 Electronic Animal Recognition Systems
2.5.2 Automatic Feed Measuring Systems
2.5.3 Drinker Systems Measuring Water Consumption
2.5.4 Milk Quality Measurement Systems
2.5.5 Monitoring of Movement Activities in Dairy Cattle
2.5.6 Detection of Lameness
2.5.7 Detection of Estrus
2.5.8 Measurement of Body Characteristics and Gait Analysis
2.5.9 Monitoring of Broiler Houses and Early Warning System
2.5.10 Monitoring Health Status with Voice Analysis
2.5.11 Automatic Temperature Measurement with Thermal Camera
2.5.12 Automatic Animal Weighing, Sorting, and Marking Systems
References
Chapter 3 Agricultural Robots
3.1 What Is a Robot?
3.2 Advantages and Disadvantages of Robots
3.2.1 Advantages of Robots
3.2.2 Disadvantages of Robots
3.3 Robot Accidents and Safety
3.4 Classification of Robots
3.5 New Trend Robots
3.5.1 Smart Autonomous (Robot) Vehicles
3.5.2 Autonomous Flying Robots
3.5.3 Humanoid Robots
3.5.4 Wearable Robots
3.5.5 Underwater Robots
3.5.6 Soft Robots
3.5.7 Industrial and Delta Robots
3.6 Components of Robots
3.6.1 Robot Chassis
3.6.2 Sensors
3.6.3 Controller
3.6.4 Actuators
3.6.5 Manipulator
3.6.6 End Effector
3.7 Robot Kinematics and Dynamics
3.8 Robot (Autonomous) Tractor
3.9 Robot Tractor Equipment
3.9.1 Radar Sensors
3.9.2 Laser Scanners
3.9.3 Lidar
3.9.4 GPS/Inertial Navigation System (INS)
3.9.5 Ultrasonic Sensor
3.9.6 Cameras
3.10 Agricultural Robots
3.11 Examples of Agricultural Robots
3.11.1 Milking Robot
3.11.2 Feeding Robot
3.11.3 Calf Feeding Robot
3.11.4 Barn Cleaning Robot
3.11.5 Mechanical Weed Control Robot
3.11.6 Spraying Robot
3.11.7 Cucumber Harvesting Robot
3.11.8 Paddy Planting Robot
3.11.9 BoniRob Multipurpose Agriculture and Weed Control Robot
References
Chapter 4 Use of Unmanned Aerial Vehicles in Agriculture
4.1 What Is an Unmanned Aerial Vehicle?
4.2 Drone Systems
4.3 Components of Drones
4.4 Drone Operation
4.5 Issues to Consider in Drone Design
4.6 Cameras and Sensors Used with Drones
4.6.1 Optical Cameras
4.6.2 Multispectral Cameras
4.6.3 Hyperspectral Cameras
4.6.4 Thermal Cameras
4.6.5 Light Detection and Ranging (Lidar)
4.6.6 Synthetic Aperture Radar (SAR)
4.7 Use of Drones in Agriculture
4.7.1 Precision Agriculture
4.7.2 Examining the Growth Status of Plants
4.7.3 Classification of Plants
4.7.4 Determination of Plant Phenotypes
4.7.5 Detection of Plant Diseases and Pests
4.7.6 Detection of Weeds
4.7.7 Agricultural Spraying
4.7.8 Artificial Pollination
4.7.9 Variable Rate Fertilization
4.7.10 Yield Estimate
4.7.11 Determination of Soil Fertility
4.7.12 Use in Irrigation Applications
4.7.13 Use in Animal Breeding
References
Chapter 5 Agriculture 5.0 and the Internet of Things
5.1 What Is Agriculture 4.0?
5.2 Supportive Technologies in Agriculture 4.0
5.3 What Is the Internet of Things (IoT)?
5.4 Big Data and Analytics
5.5 5G and Its Use in Agriculture
5.6 IoT Application Examples in Agriculture
5.6.1 Digital Agriculture Platforms
5.6.2 Telemetry Systems
5.6.3 Fertilizer Information System
5.6.4 Water Quality Measurements
5.6.5 Smart Milk-Monitoring Platform
5.7 Agriculture 5.0
References
Chapter 6 Image Processing and Machine Vision in Agriculture
6.1 What Is Image Processing?
6.2 Image Processing Operations
6.3 Stages of Image Processing
6.4 Image Processing Application Examples in Agriculture
6.4.1 Detection of Plant Diseases and Pests
6.4.2 Visual Object Detection
6.4.3 Determining the Fertilizer Distribution Pattern
6.4.4 Determination of Agricultural Product Characteristics
6.4.5 Dynamic Obstacle Detection
6.5 What Is Machine Vision?
6.6 Machine Vision Application Examples in Agriculture
6.6.1 Sorting Agricultural Products by Quality
6.6.2 Detection of Plant Diseases and Pests
6.6.3 Detection of Weeds
6.6.4 Detection of Plant and Soil
References
Chapter 7 Data Mining in Agriculture
7.1 What Is Data Mining?
7.2 Data Mining Process
7.3 Data Pre-processing
7.4 Data Mining Techniques
7.4.1 Classification Techniques
7.4.1.1 K-Nearest Neighbor
7.4.1.2 Artificial Neural Networks
7.4.1.3 Support Vector Machines
7.4.2 Statistically Based Approaches
7.4.2.1 Principal Component Analysis
7.4.2.2 Interpolation and Regression
7.4.3 Clustering Techniques
7.4.3.1 K-Means
7.4.3.2 Biclustering
7.5 Data Mining Practice Examples in Agriculture
7.5.1 Use in Clone Selection
7.5.2 Monitoring Animal Eating Behaviors
7.5.3 Animal Identification
7.5.4 Identifying Animal Sounds
7.5.5 Detection of Plant Disease and Pest Damage
7.5.6 Detection of Plant Pests
7.5.7 Detection of Weeds
7.5.8 Yield Estimate
7.5.9 Identification of Plant Leaves
References
Chapter 8 Artificial Intelligence, Machine Learning, and Deep Learning in Agriculture
8.1 What Is Artificial Intelligence?
8.2 Sub-branches of Artificial Intelligence
8.3 What Is Machine Learning?
8.4 Machine Learning Processes
8.5 Machine Learning Methods
8.6 Machine Learning Performance Metrics
8.7 What Is Deep Learning?
8.8 Common Architectural Principles of Deep Networks
8.9 Deep Learning Architectures
8.10 Fundamentals of Convolutional Neural Networks
8.11 Image Classification and Detection
8.12 Artificial Intelligence, Machine Learning, and Deep Learning Application Examples in Agriculture
8.12.1 Detection of Plant Diseases and Pests
8.12.2 Detection of Weeds
8.12.3 Monitoring the Growth of Plants
8.12.4 Detection of Plant and Tree Locations
8.12.5 Detection of Real-Time Fruit
8.12.6 Detection of Grape Yield
8.12.7 Identification of Plant Leaves
8.12.8 Monitoring of Plant-Growing Environments
8.12.9 Detection of Real-Time Object
8.12.10 Identification of Agricultural Machinery
8.12.11 Smart Sprayer
8.12.12 Use in Agricultural Product Drying
8.12.13 Identification of Animals
8.12.14 Use in Feeding Applications in Livestock
8.12.15 Identification of Animal Sounds
References
Index
