The first results of a new approach for implementing operational monitoring tool to control the performance of forest mechanisation chains are proposed and discussed. The solution is based on Global Navigation Satellite System (GNSS) tools that are the core of a datalogging system that, in combination with a specific inference-engine, is able to analyse process times, work distances, forward speeds, vehicle tracking and number of working cycles in forest operations. As a consequence the operational monitoring control methods could provide an evaluation of the efficiency of the investigated forest operations.The study has monitored the performance of a tower yarder with crane and processor-head, during logging operations. The field surveys consisted on the installation of the GNSS device directly on the forest equipment for monitoring its movements. Simultaneously the field survey considered the integration of the GNSS information with a time study of work elements based on the continuous time methods supported by a time study board. Additionally, where possible, the onboard computer of the forest machine was also used in order to obtain additional information to be integrated to the GNSS data and the time study. All the recorded GNSS data integrated with the work elements study were thus post-processed through GIS analysis.The preliminary overview about the application of this approach on harvesting operations has permitted to assess a good feasibility of the use of GNSS in the relief of operative times in high mechanised forest chains. Results showed an easy and complete identification of the different operative cycles and elementary operations phases, with a maximum difference between the two methodologies of 10.32%. The use of GNSS installed on forest equipment, integrated with the inferenceengine and also with an interface for data communication or data storage, will permit an automatic or semi-automatic operational monitoring, improving the quantity of data and reducing the engagement of the surveyor.

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At today, available mechatronics technology allows exploiting smart and precise sensors as well as embedded and effective mechatronic systems for developing (semi-)autonomous robotic platforms able to both navigate in different outdoor environments and implementing Precision Farming techniques. In this work, the experimental outdoor assessment of the performance of a mobile robotic lab, the ByeLab — Bionic eYe Laboratory — is presented and discussed. The ByeLab, developed at the Faculty of Science and Technology of the Free University of Bolzano (I), has been conceived with the aim of creating a (semi-)autonomous robotic system able to sense and monitor the health status of orchards and vineyards. For assessing and measuring the shape and the volume of the canopy, LIDAR technology coupled with ad-hoc developed algorithms have been exploited. To validate the ByeLab different experimental tests have been carried out. In addition to the in-lab and structured environments experimental tests that allowed to tune the algorithms, in this work the assessment of its capabilities — in particular the sensoric system — has been made outdoor controlled environment tests.

Precision agriculture has been increasingly recognized for its potential ability to improve agricultural productivity, reduce production cost, and minimize damage to the environment. In this work, the current stage of our research in developing a mobile platform equipped with different sensors for orchard monitoring and sensing is presented. In particular, the mobile platform is conceived to monitor and assess both the geometric and volumetric conditions as well as the health state of the canopy. To do so, different sensors have been integrated and effective data-processing algorithms implemented for a reliable crop monitoring. Experimental tests have been performed allowing to obtain both a precise volume reconstruction of several plants and an NDVI mapping suitable for vegetation state evaluations.

After a comparative evaluation around the concepts of Industry 4.0, Precision Agriculture and Smart Farming, the paper discusses the necessity of identifying solutions able to apply the methods of the so-called Knowledge Management 4.0 in the agri-environmental enterprises. To this aim, an ontology based on a conceptual map derived from the data-to-information transformation is here proposed to support the design of a new generation of Farm Information Systems (FIS) able to fit the production needs of agri-environmental enterprises. The data-to-information cycle is split into four phases: A) Data collection, B) Data processing, C) Data analysis and evaluation, and D) Use of information. Phases A and D comprise tools of “light digitization” typically referring to OLTP components (On Line Transactional Processes). On their turn, phases B and C are typically formed by “heavy digitization” components (On Line Analytical Processes), generally more complicated to be managed by the farmers directly. The conceptual map defined by the ontology is firstly useful to plan the composition of the FIS according to a modular approach, possibly following a strategy that starts to consider the introduction of OLTP components, for then evolving gradually towards more articulated solutions that include also OLAP component and related functions. In this way, decision makers can manage practical planning instrument able to show clearly the level of complexity a FIS can reach, thus evaluating its sustainability with respect to the financial, cultural and professional resources available at the farm. Finally, after having underlined the opportunities offered by Cloud Computing and widespread connectivity, that provide an easier adoption of OLAP tools, some application cases are presented and discussed.