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

✍ Scribed by Mohammad Shorif Uddin (editor), Jagdish Chand Bansal (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 180
• Edition: 1st ed. 2021
• Category: Library

✦ Synopsis

This book discusses computer vision, a noncontact as well as a nondestructive technique involving the development of theoretical and algorithmic tools for automatic visual understanding and recognition which finds huge applications in agricultural productions. It also entails how rendering of machine learning techniques to computer vision algorithms is boosting this sector with better productivity by developing more precise systems. Computer vision and machine learning (CV-ML) helps in plant disease assessment along with crop condition monitoring to control the degradation of yield, quality, and severe financial loss for farmers. Significant scientific and technological advances have been made in defect assessment, quality grading, disease recognition, pests, insects, fruits, and vegetable types recognition and evaluation of a wide range of agricultural plants, crops, leaves, and fruits. The book discusses intelligent robots developed with the touch of CV-ML which can help farmers to perform various tasks like planting, weeding, harvesting, plant health monitoring, and so on. The topics covered in the book include plant, leaf, and fruit disease detection, crop health monitoring, applications of robots in agriculture, precision farming, assessment of product quality and defects, pest, insect, fruits, and vegetable types recognition.

✦ Table of Contents

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