Computer Vision and Machine Learning in Agriculture /

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 machin...

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Bibliographic Details
Corporate Author:	SpringerLink (Online service)
Other Authors:	Uddin, Mohammad Shorif (Editor), Bansal, Jagdish Chand (Editor)
Format:	eBook
Language:	English
Published:	Singapore : Springer Singapore : Imprint: Springer, 2021.
Edition:	1st ed. 2021.
Series:	Algorithms for Intelligent Systems,
Subjects:	Computational intelligence. Machine learning. Robotics. Agriculture. Optical data processing. Computational Intelligence. Machine Learning. Image Processing and Computer Vision. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Chapter 1. Introduction to Computer Vision and Machine Learning Applications in Agriculture
Chapter 2. Robots and Drones in Agriculture - A Survey
Chapter 3. Detection of Rotten Fruits and Vegetables using Deep Learning
Chapter 4. Deep Learning-Based Essential Paddy Pests Filtration Technique: A Better Economic Damage Management Process
Chapter 5. Deep CNN-Based Mango Insect Classification
Chapter 6. Implementation of a Deep Convolutional Neural Network for the Detection of Tomato Leaf Diseases
Chapter 7. A Multi-Plant Disease Diagnosis Method using Convolutional Neural Network
Chapter 8. A Deep Learning-Based Approach for Potato Diseases Classification
Chapter 9. An In-Depth Analysis of Different Segmentation Techniques in Automated Local Fruit Disease Recognition
Chapter 10. Machine Vision Based Fruit and Vegetable Disease Recognition: A Review
Chapter 11. An Efficient Bag-of-Features for Diseased Plant Identification.