Photovoltaic (PV) technology has evolved rapidly during the last decade and alongside the interest for the identification of the key factors that affect PV modules' performance during operation. Among different preventive maintenance techniques, thermal imaging offers considerable advantages regarding equipment's condition monitoring as it provides a nondestructive and cost-effective inspection tool. This paper attempts to address the problem of early defect diagnosis at installed PV modules through the exploitation of spatio-temporal information from thermal images. The proposed method aims to minimize standard maintenance costs and reduce the necessity of human intervention.
The chapter describes the key role that sensor data play in the DataBio project. It introduces the concept of sensing devices and their contribution in the evolution of the Internet of Things (IoT). The chapter outlines how IoT technologies have affected bioeconomy sectors over the years. The last part outlines key examples of sensing devices and IoT data that are exploited in the context of the DataBio project.
The chapter describes DataBio’s pilot applications, led by NEUROPUBLIC S.A., for sustainable agricultural production in Greece. Initially, it introduces the main aspects that drive and motivate the execution of the pilot. The pilot set-up consisted of four (4) different locations, four (4) different crop types and three (3) different types of offered services. The technology pipeline was based on the exploitation of heterogeneous data and their transformation into facts and actionable advice fostering sustainable agricultural growth. The results of the pilot activities effectively showcased how smart farming methodologies can lead to a positive impact from an economical, environmental and societal perspective and achieve the ambitious goal to “produce more with less”. The chapter concludes with “how-to” guidelines and the pilot’s key findings.
The aim of this paper is the digital analysis of thermal images of a material with abnormalities lying beneath its surface focusing on the search of the image's feature that gives the best information about the abnormalities. The process followed observes the change of thermal images with time and by the use of certain algorithms, information concerning the abnormalities, like the depth from the surface is gathered.
The pilot aimed to develop services supporting both the risk and the damage assessment in the agro-insurance domain. It is based on the use of remotely sensed data, integrated with meteorological data, and adopts machine learning and artificial intelligence tools. Netherlands and Greece have been selected as pilot areas . In the Netherlands, the pilot was focused on potato crops for the identification of areas with higher risk, based on the historical analysis of heavy rains. In addition, it covered automated detection of potato parcels with anomalous behaviours (damage assessment) from satellite data, meteorological parameters and soil characteristics. In Greece, the pilot worked with 7 annual crops of high economic interest to the national agricultural sector. The crops have been modelled exploiting the last 3-year NDVI measurements to identify their deviations from the normal crop health behaviour for an early identification of affected parcels in case of adverse events. The models were successfully tested on a flooding event that occurred in 2019 in the Komotini region. Even though the proposed methodologies should be tested over larger areas and compared against a larger validation dataset, the results already now demonstrate how to reduce the operating costs of damage assessors through a more precise and automatic risk assessment. Additionally, the identification of parameters that most affect the crop yield could transform the insurance industry through index-based solutions allowing to dramatically cut costs.
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