Blockchain research, practice and policy: Applications, benefits, limitations, emerging research themes and research agenda

YesThe blockchain has received significant attention from technology focussed researchers, highlighting its perceived impact and emerging disruption potential, but has been slow to engender any significant momentum within the Information Systems (IS) and Information Management (IM) literature. This study approaches the subject through an IS/IM lens developing the key themes from the blockchain based research via a comprehensive review. This analysis of the body of literature highlights that although few commercial grade blockchain applications currently exist, the technology demonstrates significant potential to benefit a number of industry wide use cases. This study expands on this point articulating through each of the key themes to develop a detailed narrative on the numerous potential blockchain applications and future direction of the technology, whilst discussing the many barriers to adoption. The study asserts that blockchain technology has the potential to contribute to a number of the UN Sustainability Development Goals and engender widespread change within a number of established industries and practices

A fuzzy superiority and inferiority ranking based approach for IT service management software selection

Abstract Purpose – Information technology service management (ITSM) has become a major IT department management system in organizations. Successful implementation of ITSM depends on select adequate ITSM software. Evaluation and selection of the ITSM solution or software packages is complicated and time-consuming decision-making problem. This paper aims to present an approach for dealing with such a problem. Design/methodology/approach – This approach introduces functional, non-functional requirements and novel fuzzy out-ranking evaluation method for ITSM software selection. The presented approach breaks down ITSM software selection criteria into two broad categories, namely, functional (service strategy, service design, service transition, service operation, continual service improvement according to Information Technology Infrastructure Library V3) and non-functional requirements (quality, technical, vendor, implementation) including totally 46 selection criteria. A novel fuzzy superiority and inferiority ranking (FSIR) was developed and made applicable for ITSM software selection based on identified criteria. Findings – The proposed approach is applied to IT services company to select and acquire ITSM software, and the provided numerical example illustrates the applicability of the approach for this choice. The approach can facilitate firms to achieve suitable ITSM software and have a precise acquisition decision; however, the limitation of dependency on experts’ competence and proficiency in the both ITSM field and IT technical issues exists. Research limitations/implications – The approach can facilitate firms to achieve suitable ITSM software and have a precise acquisition decision; however, the limitation of dependency on experts’ competence and proficiency in the both ITSM field and IT technical issues exists. Practical implications – Facilitating of ITSM implementation through its handy software selection is the major impact of current research. Originality/value – A facile FSIR-based approach for software selection has been customized to contribute to the current literature in the ITSM field. Facilitating of ITSM implementation through its handy software selection is the major impact of current research

Computer Vision in Self-Steering Tractors

Automatic navigation of agricultural machinery is an important aspect of Smart Farming. Intelligent agricultural machinery applications increasingly rely on machine vision algorithms to guarantee enhanced in-field navigation accuracy by precisely locating the crop lines and mapping the navigation routes of vehicles in real-time. This work presents an overview of vision-based tractor systems. More specifically, this work deals with (1) the system architecture, (2) the safety of usage, (3) the most commonly faced navigation errors, (4) the navigation control system of tractors and presents (5) state-of-the-art image processing algorithms for in-field navigation route mapping. In recent research, stereovision systems emerge as superior to monocular systems for real-time in-field navigation, demonstrating higher stability and control accuracy, especially in extensive crops such as cotton, sunflower, maize, etc. A detailed overview is provided for each topic with illustrative examples that focus on specific agricultural applications. Several computer vision algorithms based on different optical sensors have been developed for autonomous navigation in structured or semi-structured environments, such as orchards, yet are affected by illumination variations. The usage of multispectral imaging can overcome the encountered limitations of noise in images and successfully extract navigation paths in orchards by using a combination of the trees’ foliage with the background of the sky. Concisely, this work reviews the current status of self-steering agricultural vehicles and presents all basic guidelines for adapting computer vision in autonomous in-field navigation

Computer Vision in Self-Steering Tractors

Automatic navigation of agricultural machinery is an important aspect of Smart Farming. Intelligent agricultural machinery applications increasingly rely on machine vision algorithms to guarantee enhanced in-field navigation accuracy by precisely locating the crop lines and mapping the navigation routes of vehicles in real-time. This work presents an overview of vision-based tractor systems. More specifically, this work deals with (1) the system architecture, (2) the safety of usage, (3) the most commonly faced navigation errors, (4) the navigation control system of tractors and presents (5) state-of-the-art image processing algorithms for in-field navigation route mapping. In recent research, stereovision systems emerge as superior to monocular systems for real-time in-field navigation, demonstrating higher stability and control accuracy, especially in extensive crops such as cotton, sunflower, maize, etc. A detailed overview is provided for each topic with illustrative examples that focus on specific agricultural applications. Several computer vision algorithms based on different optical sensors have been developed for autonomous navigation in structured or semi-structured environments, such as orchards, yet are affected by illumination variations. The usage of multispectral imaging can overcome the encountered limitations of noise in images and successfully extract navigation paths in orchards by using a combination of the trees’ foliage with the background of the sky. Concisely, this work reviews the current status of self-steering agricultural vehicles and presents all basic guidelines for adapting computer vision in autonomous in-field navigation