Call for Chapters: Applications of Image Processing and Soft Computing Systems in Agriculture

Editors
Dr. Navid Razmjooy
Department of Electrical Engineering
Tafresh University, Iran

Dr. Vania V. Estrela
Departamento de Engenharia de Telecomunicações
Universidade Federal Fluminense, Rio de Janeiro, Brazil

Call for Chapters
Proposals Submission Deadline: May 15, 2018
Full Chapters Due: August 15, 2018
Submission Date: August 15, 2018

Introduction
The variety and abundance of qualitative characteristics of agricultural products have been the main reason for the development of different types of Non-Destructive Methods (NDTs). Quality control of these products is one of the most important tasks in manufacturing processes. The use of control and automation has become more widespread, and new approaches provide opportunities for production competition through new technologies. The need to intensify the quality and quantity of a product leads to the use of advanced automated machines.
Machine tools are increasingly becoming more automated and less reliant on human factors. Nowadays, visual machine technology and image processing techniques are widely used in the industry, particularly common in product quality control, robot guidance and self-guided mechanisms.

Machine vision and artificial intelligence are powerful practices for identifying many of the physical, mechanical and chemical characteristics of agricultural products. For instance, before exporting the agricultural products, they are usually graded according to shape, size, and weight. Ranking these products regarding the explained characteristics in a non-destructive way has a great impact on the market, the selection confidence, and its application.

Among the things that can be used to automate agricultural production, there are computerized agrarian machines equipped with vision. Such machines guarantee quality standards through high-speed sorting, optimization of robotic cutting, food detection systems, packaging identification and product labeling. Such automated frameworks can also provide a reliable and standardized product tracking system and other technology items that an agro-company provides to the customers as well as to serve the production.

Insufficient knowledge of the Computer Vision (CV) technology and the lack of familiarity with the economic justification for using CV has created skepticism, and in some cases, there is a negative reaction. Despite this, the machine vision has become progressively more used and its growth has been impressive. The application of image processing boils down to a comparison of sets of numbers against a threshold so that the product is rejected or accepted.

In the last decades, agricultural image processing calls for Soft Computing (SC) because of SC effectiveness in handling uncertainties inherent in acquired image data and the fact that SC algorithms are less intensive computationally speaking.

SC refers to a set of new computing practices in computer science, artificial intelligence, machine learning, and many other useful applications that try to circumvent the limitations of hard optimization methods. In all these areas, it is necessary to study, model, and analyze very complex phenomena with precise scientific methods that failed analytically, and mathematically when it comes to finding feasible solutions in the past.

Compared with soft scientific methods, the optimization methods used some time ago have only succeeded in modeling and analyzing relatively simple systems in mechanics, physics, and some other fields of applied science and engineering.

More sophisticated issues such as the ones related to models for biology, medicine, social sciences, humanities, management sciences, and so on remained outside the main realm of hard computing strategies as well as from exact mathematical and analytical methods.

Some recent examples of soft computing techniques are fuzzy connectedness approaches to image segmentation, fuzzy clustering methods for products like leaf spot disease of cucumber images, optimization based products packing, deep/shallow neural network based inspection systems for agricultural products, etc.

Objective
The shape of agricultural products plays an important role in their marketing value. Typically, factors such as size, shape, color, freshness, and novelty, and ultimately the absence of apparent defects on their surface affect the sale of agricultural products. An interpretation of the form of agricultural production in research is required for a variety of purposes, including examination of the traits of the hereditary form, to describe the variety, plant types, and consumer decision in the purchase of the product. In this book, the main uses of the shape analysis on some agricultural products, such as relationships between form and genetics, adaptation, product characteristics, and product sorting, are examined, besides other insights that can be brought in by computer vision techniques. In this regard, image processing and soft computing algorithms have developed dramatically over the past two decades to measure the external characteristics of agricultural products. Since the analysis of the considered techniques can help us to conduct surveys, the proper investigations about the applications of artificial intelligence in the agriculture can inspire us to develop works that are more sophisticated in the future.

Target Audience
The use of artificial intelligence methods to making manual and human decisions is also highly relevant. The agricultural industry is one of the branches that today needs to calculate and perform automatic operations using artificial intelligence. So that at different stages of plant cultivation, including planting and harvesting, can be used at different stages of storage and processing of agricultural products, including quality measurement. In addition, many applications become viable in macroeconomic decision-making, including management in the field of equipment maintenance, precision agriculture, performance estimation, etc.

Recommended Topics
Soft Computing
• Artificial Neural Networks
• Artificial Immune Systems
• Artificial Life
• Fuzzy Logic Theory
• Rough Sets
• Chaotic Theory
• Evolutionary Computation
• Deep Learning
• Complex-valued neural systems
• Any other related SC technique.

Computer Vision
• Image enhancement/segmentation/compression
• Image analysis
• Scene understanding
• 3-D/4-D image processing
• Visual Information Retrieval and Semantics for Agriculture
• Classification and Clustering for Agriculture
• Preprocessing, Feature Extraction, and Data Mining for Agriculture
• Non-Destructive Testing (NDT) in agriculture and agribusiness.
• Remote Sensing (RS) and Surveillance in agriculture and agribusiness.
• Virtual and Augmented Reality (VAR) in agriculture and agribusiness.
• Graphics and animation
• Other related areas

Submission Procedure
Researchers and practitioners are invited to submit on or before April 25, 2018, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by May 10, 2018 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted by August 15, 2018, and all interested authors must consult the guidelines for manuscript submissions at http://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Trust in Knowledge Management and Systems in Organizations. All manuscripts are accepted based on a double-blind peer review editorial process.

All proposals should be submitted through the eEditorial Discovery®TM online submission manager.

Publisher
This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. For additional information regarding the publisher, please visit www.igi-global.com. This publication is anticipated to be released in 2019.

Important Dates
May 15, 2018: Proposal Submission Deadline
May 30, 2018: Notification of proposal acceptance
August 15, 2018: Full Chapter Submission
October 15, 2018: Review Results Returned
October 31, 2018: Revised Chapter Submission
November 15, 2018: Final Acceptance Notification
November 30, 2018: Final Chapters to Editor

Inquiries
Dr. Navid Razmjooy Email: navid.razmjooy@ieee.org
Department of Electrical Engineering
Tafresh University, Tafresh, Iran


Dr. Vania V. Estrela Email: vania.estrela.phd@ieee.org
Departamento de Engenharia de Telecomunicações
Universidade Federal Fluminense, Rio de Janeiro, Brazil