Computer Vision and Machine Learning in Agriculture (Algorithms for Intelligent Systems)

Preface
Contents
Editors and Contributors
	About the Editors
	Contributors
Introduction to Computer Vision and Machine Learning Applications in Agriculture
	1 Introduction
	2 Computer Vision and Machine Learning in Agriculture
	3 Challenges and Future Scopes
	4 Conclusion
	References
Robots and Drones in Agriculture—A Survey
	1 Introduction
	2 Robotics Basic
		2.1 Robotic Mechanism
		2.2 Agricultural Robot Classification
	3 Robots in Agricultural Applications
		3.1 Robots in Path Navigation
		3.2 Robots in Crop Production
		3.3 Robots in Weed Removal and Disease and Pest Control
		3.4 Robots in Crop Harvesting
	4 Drones in Agriculture
	5 Commercialization and Current Challenges of Agricultural Robots
	6 Conclusion
	References
Detection of Rotten Fruits and Vegetables Using Deep Learning
	1 Introduction
	2 Computer Vision and Machine Learning in Fruits and Vegetable Processing
		2.1 Segmentation and Detection of Fruits and Vegetables from the Natural Environment
		2.2 Classification of Fruits and Vegetables
		2.3 Grading of Fruits and Vegetables
		2.4 Sorting the Defective Fruits and Vegetables
	3 Materials and Methods
		3.1 Dataset
		3.2 Convolutional Neural Network
		3.3 Proposed Convolutional Neural Network Architecture
		3.4 AlexNet Architecture
	4 Experimentation and Results
	5 Discussion
	6 Conclusion
	References
Deep Learning-Based Essential Paddy Pests\' Filtration Technique for Economic Damage Management
	1 Introduction
	2 Related Works
	3 Pests Classification
		3.1 Beneficial Pests
		3.2 Non-beneficial Pests
	4 Methodology
	5 Deep Learning
		5.1 Convolutional Neural Network
	6 Experiments
		6.1 Dataset
		6.2 Experimental Setup
		6.3 Confusion Matrix
		6.4 Computation Time
	7 Conclusion
	References
Deep CNN-Based Mango Insect Classification
	1 Introduction
	2 Related Works
	3 Methodology
		3.1 Dataset Preparation
		3.2 Ensemble-Based Classification
	4 Results and Discussion
		4.1 Performance of Fine-Tuned Models
		4.2 Ensemble Method Performance
	5 Conclusion
	References
Implementation of a Convolutional Neural Network for the Detection of Tomato-Leaf Diseases
	1 Introduction
	2 Related Works
	3 Methodology
	4 Results Analysis
	5 Conclusion
	References
A Multi-Plant Disease Diagnosis Method Using Convolutional Neural Network
	1 Introduction
	2 Related Work
	3 Dataset
	4 Methodology
		4.1 Image Pre-processing
		4.2 Baseline Architecture
		4.3 Loss Function
	5 Evaluation
		5.1 Evaluation Metric
		5.2 Experimental Setup
		5.3 Experiments and Comparisons
	6 Conclusion
	References
A Deep Learning-Based Approach for Potato Disease Classification
	1 Introduction
	2 Related Works
	3 Materials and Methods
		3.1 Dataset Description
		3.2 Deep Convolutional Neural Network
	4 Experimental Results and Discussions
		4.1 Performance Matrix
		4.2 Number of Iterations
	5 Conclusion
	References
An In-Depth Analysis of Different Segmentation Techniques in Automated Local Fruit Disease Recognition
	1 Introduction
	2 Related Works
	3 Research Methodology
	4 Local Fruit Diseases and Performance Metrics
		4.1 Disease Description
		4.2 Metric Description
	5 Experimental Evaluation
	6 Conclusion
	References
Machine Vision-Based Fruit and Vegetable Disease Recognition: A Review
	1 Introduction
	2 Fruit and Vegetable Disease Recognition Basics
	3 Fruit and Vegetable Disease Recognition Data Set and Methods
	4 Performance Evaluation Metrics
	5 State-of-the-Art Approaches
	6 Conclusion
	References
An Efficient Bag-of-Features for Diseased Plant Identification
	1 Introduction
	2 Proposed Method
		2.1 Feature Extraction
		2.2 Codebook Construction
		3.3 Feature Encoding Using W2DVQ Method
		3.4 Classification
	4 Results and Discussion
	5 Conclusion and Points for Future Work
	References