Abstract

It took some time indeed, but the research evolution and transformations that occurred in the smart agriculture field over the recent years tend to constitute the latter as the main topic of interest in the so-called Internet of Things (IoT) domain. Undoubtedly, our era is characterized by the mass production of huge amounts of data, information and content deriving from many different sources, mostly IoT devices and sensors, but also from environmentalists, agronomists, winemakers, or plain farmers and interested stakeholders themselves. Being an emerging field, only a small part of this rich content has been aggregated so far in digital platforms that serve as cross-domain hubs. The latter offer typically limited usability and accessibility of the actual content itself due to problems dealing with insufficient data and metadata availability, as well as their quality. Over our recent involvement within a precision viticulture environment and in an effort to make the notion of smart agriculture in the winery domain more accessible to and reusable from the general public, we introduce herein the model of an aggregation platform that provides enhanced services and enables human-computer collaboration for agricultural data annotations and enrichment. In principle, the proposed architecture goes beyond existing digital content aggregation platforms by advancing digital data through the combination of artificial intelligence automation and creative user engagement, thus facilitating its accessibility, visibility, and re-use. In particular, by using image and free text analysis methodologies for automatic metadata enrichment, in accordance to the human expertise for enrichment, it offers a cornerstone for future researchers focusing on improving the quality of digital agricultural information analysis and its presentation, thus establishing new ways for its efficient exploitation in a larger scale with benefits both for the agricultural and the consumer domains.

Highlights

  • Over recent years, the smart agriculture sector has evolved tremendously towards the bridging of two separate worlds: information technologies and primitive agriculture

  • Lack of structured and rich descriptive metadata; complex, heterogeneous, and multi-channel aggregation workflows; possible shortcomings in the data providing process; These are some of the main causes that result in poor information descriptions

  • Metadata enrichment services through automated metadata processing and feature extraction, possibly along with crowd-sourcing annotation services, available in a centralized way through a dedicated platform can offer a remarkable opportunity for improving the metadata quality of digital smart agriculture content stored in such typical platforms, while at the same time engaging users and raising awareness about smart agriculture assets

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Summary

Introduction

The smart agriculture sector has evolved tremendously towards the bridging of two separate worlds: information technologies and primitive agriculture. Lack of structured and rich descriptive metadata; complex, heterogeneous, and multi-channel aggregation workflows; possible shortcomings in the data providing process (surpassing manual quality control of automatic metadata generation in digital repositories); These are some of the main causes that result in poor information descriptions This drawback highly affects the accessibility, visibility and dissemination range of the available digital content. Metadata quality improvement usually faces another important problem, i.e., the problem of scale, since improving the metadata quality coming from different sources often requires a huge amount of time, effort, and resources that aggregators cannot afford In this framework, metadata enrichment services through automated metadata processing and feature extraction, possibly along with crowd-sourcing annotation services, available in a centralized way through a dedicated platform can offer a remarkable opportunity for improving the metadata quality of digital smart agriculture content stored in such typical platforms, while at the same time engaging users and raising awareness about smart agriculture assets.

Smart Agriculture Overview
Precision Viticulture
Utilization of Artificial Intelligence
Agricultural Data Aggregation—An Interconnected System
Handling of Metadata Semantic Heterogeneity
Content Search and Management
Content Aggregation
Collections and Spaces Management
Metadata Generation and Enrichment
Case Study
Discussion and Conclusions
Full Text
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