Dionysis Bochtis

TL;DR: A comprehensive review of research dedicated to applications of machine learning in agricultural production systems is presented, demonstrating how agriculture will benefit from machine learning technologies.

TL;DR: This research has shown the benefit of using dedicated system analysis methodologies as a preliminary step to the actual design of a novel farm management information system compared with other more rigid and activity oriented system analysis methods.

TL;DR: In this paper, the authors present a review of current and required advances in agricultural machinery management to prepare for future intelligent manned and/or autonomous sustainable operations in agriculture. But, they do not discuss the future requirements which will facilitate and set the framework for the development efforts necessary for fully implementing future agricultural management models and tools.

TL;DR: A dedicated classification of AFO is devised and tailored to a conceptual application of the VRP within the domain of agricultural field logistics, to provide for the enhanced management of conventional machinery systems, as well as, the mission and route-planning aspects of the high level control of emerging field robots.

TL;DR: In this article, a conceptual model was developed based on a participatory approach and subsequent system analysis, which involved identification of the scope of the system, identification of user requirements, conceptual modelling, identifying of actors and decision processes, and information-needs modelling.