Towards Improved Visualization and Optimization of Aquaculture Production Process

Aquaculture is one of the largest, and fastest growing industries in Norway. Recently, the industry has experienced significant development in the daily operations acquiring new technologies and systems that capture data and automate the different processes. These emerging technologies enable the generation of enormous amounts of data from sensors in the fish cages, cameras, boats, and feeding control rooms. Additional information relevant to the aquaculture industry is based on e-mails, manual notes, or intrinsic experiences and knowledge exchanges. One of the critical aspects of successful fish farming operation management, which is yet not achieved, is to allow domain experts to gain insight into the interconnection between the broad spectrum of heterogeneous data currently realized. This paper describes a graph-based database approach to storing and retrieving critical information connected to fish farming operations. The overall architecture is presented with detailed illustrations of how data is visualized and interpreted through a user-friendly interface. Accordingly, this work demonstrates how aquaculture users can benefit from the system to identify possible connections in the data and reveal previously undiscovered causalities and correlations that suggest optimal actions. Further, studies and evaluations of the querying system are conducted, evaluating the capability of the proposed design to process complex relationships. This work showcases that the system helps fish farmers and aquaculture users gain knowledge, reveal hidden links in the data, and improve aquaculture operations.publishedVersio

Enhancing marine industry risk management through semantic reconciliation of underwater IoT data streams

The “Rio+20” United Nations Conference on Sustainable Development (UNCSD) focused on the "Green economy" as the main concept to fight poverty and achieve a sustainable way to feed the planet. For coastal countries, this concept translates into "Blue economy", the sustainable exploitation of marine environments to fulfill humanity needs for resources, energy, and food. This puts a stress on marine industries to better articulate their processes to gain and share knowledge of different marine habitats, and to reevaluate the data value chains established in the past and to support a data fueled market that is going only to in the near future.The EXPOSURES project is working in conjunction with the SUNRISE project to establish a new marine information ecosystem and demonstrate how the ‘Internet of Things’ (IoT) can be exploited for marine applications. In particular EXPOSURES engaged with the community of stakeholders in order to identify a new data value chain which includes IoT data providers, data analysts, and harbor authorities. Moreover we integrated the key technological assets that couple OGC standards for raster data management and manipulation and semantic technologies to better manage data assets.This paper presents the identified data value chain along with the use cases for validating it, and the system developed to semantically reconcile and manage such data collections

CGIAR Platform for Big Data in Agriculture - Plan of Work and Budget 2021

The CGIAR Platform for Big Data in Agriculture is a cross-cutting program of the global CGIAR consortium of non-profit research institutes looking into virtually every aspect of food security spanning: genomics, breeding, agroecology, climate science, and the socioeconomic drivers and context of food systems change. The Platform tends to data standards and data sharing, digital innovation strategy and technology transfer, and research into the intersection of digital technologies and agricultural development in emerging regions

Development of small-scale fisheries and aquaculture ontology

This Master thesis concludes a period of 6 months of internship in the framework of the Master2 EURAMA at INP Purpan School, Toulouse, for the project the “Development of small-scale fisheries and Aquaculture ontology” of the Alliance Bioversity-CIAT and WorldFish. WorldFish proposed to start an ontology for two domains: Small-scale Fisheries and Aquaculture. The objective of the ontology project is to improve the WorldFish data interoperability into the various projects, databases and repositories by (a) addressing inconsistent use of fish, fisheries, and aquaculture related terms across the datasets, (b) highlighting the missing terms in the main semantic resources, and lastly (c) connect and collaborate with the Community of Practice. The thesis describes the steps taken to a) extract the concepts used in WorldFish reporitoy and in papers published by scientists, b) identify the corresponding terms in the FAO thesaurs AGROVOC and get the termIDs, c) allocate valid defintions, d) develop the knowledge model and the ontology using Protégé

An ontology model to represent aquaponics 4.0 system’s knowledge

Aquaponics, one of the vertical farming methods, is a combination of aquaculture and hydroponics. To enhance the production capabilities of the aquaponics system and maximize crop yield on a commercial level, integration of Industry 4.0 technologies is needed. Industry 4.0 is a strategic initiative characterized by the fusion of emerging technologies such as big data and analytics, internet of things, robotics, cloud computing, and artificial intelligence. The realization of aquaponics 4.0, however, requires an efficient flow and integration of data due to the presence of complex biological processes. A key challenge in this essence is to deal with the semantic heterogeneity of multiple data resources. An ontology that is regarded as one of the normative tools solves the semantic interoperation problem by describing, extracting, and sharing the domains’ knowledge. In the field of agriculture, several ontologies are developed for the soil-based farming methods, but so far, no attempt has been made to represent the knowledge of the aquaponics 4.0 system in the form of an ontology model. Therefore, this study proposes a unified ontology model, AquaONT, to represent and store the essential knowledge of an aquaponics 4.0 system. This ontology provides a mechanism for sharing and reusing the aquaponics 4.0 system’s knowledge to solve the semantic interoperation problem. AquaONT is built from indoor vertical farming terminologies and is validated and implemented by considering experimental test cases related to environmental parameters, design configuration, and product quality. The proposed ontology model will help vertical farm practitioners with more transparent decision-making regarding crop production, product quality, and facility layout of the aquaponics farm. For future work, a decision support system will be developed using this ontology model and artificial intelligence techniques for autonomous data-driven decisions

Food System Digitalization as a Means to Promote Food and Nutrition Security in the Barents Region

The consumption of food and its safety are important for human security. In this paper, we reviewed the literature on future possibilities for transforming the food system through digital solutions in the Barents region. Such digital solutions will make food business operators more efficient, sustainable, and transparent. Developing cross-border infrastructures for digitalization in the region will break the isolation of the local food system, thus simplifying the availability of processed, novel and safe traditional food products. It is necessary for food growers and processors to respond to the trends driven by consumers’ demand while ensuring their safety. Our review highlights the opportunities provided by digital technology to ensure safety and help food business operators predict consumer trends in the future. In addition, digitalization can create conditions that are necessary for the diversification of organizational schemes and the effective monitoring of food processing operations that will help to promote food and nutrition security in the Barents region

From multiple aspect trajectories to predictive analysis: a case study on fishing vessels in the Northern Adriatic sea

In this paper we model spatio-temporal data describing the fishing activities in the Northern Adriatic Sea over four years. We build, implement and analyze a database based on the fusion of two complementary data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed) and fish catch reports (i.e., the quantity and type of fish caught) of the main fishing market of the area. We present all the phases of the database creation, starting from the raw data and proceeding through data exploration, data cleaning, trajectory reconstruction and semantic enrichment. We implement the database by using MobilityDB, an open source geospatial trajectory data management and analysis platform. Subsequently, we perform various analyses on the resulting spatio-temporal database, with the goal of mapping the fishing activities on some key species, highlighting all the interesting information and inferring new knowledge that will be useful for fishery management. Furthermore, we investigate the use of machine learning methods for predicting the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation in order to drive specific policy design. A variety of prediction methods, taking as input the data in the database and environmental factors such as sea temperature, waves height and Clorophill-a, are put at work in order to assess their prediction ability in this field. To the best of our knowledge, our work represents the first attempt to integrate fishing ships trajectories derived from AIS data, environmental data and catch data for spatio-temporal prediction of CPUE – a challenging task

CGIAR Platform for Big Data in Agriculture - Plan of Work and Budget 2021

The CGIAR Platform for Big Data in Agriculture is a cross-cutting program of the global CGIAR consortium of non-profit research institutes looking into virtually every aspect of food security spanning: genomics, breeding, agroecology, climate science, and the socioeconomic drivers and context of food systems change. The Platform tends to data standards and data sharing, digital innovation strategy and technology transfer, and research into the intersection of digital technologies and agricultural development in emerging regions

Governing agricultural data: Challenges and recommendations

The biomedical domain has shown that in silico analyses over vast data pools enhances the speed and scale of scientific innovation. This can hold true in agricultural research and guide similar multi-stakeholder action in service of global food security as well (Streich et al. Curr Opin Biotechnol 61:217–225. Retrieved from https://doi.org/10.1016/j.copbio.2020.01.010, 2020). However, entrenched research culture and data and standards governance issues to enable data interoperability and ease of reuse continue to be roadblocks in the agricultural research for development sector. Effective operationalization of the FAIR Data Principles towards Findable, Accessible, Interoperable, and Reusable data requires that agricultural researchers accept that their responsibilities in a digital age include the stewardship of data assets to assure long-term preservation, access and reuse. The development and adoption of common agricultural data standards are key to assuring good stewardship, but face several challenges, including limited awareness about standards compliance; lagging data science capacity; emphasis on data collection rather than reuse; and limited fund allocation for data and standards management. Community-based hurdles around the development and governance of standards and fostering their adoption also abound. This chapter discusses challenges and possible solutions to making FAIR agricultural data assets the norm rather than the exception to catalyze a much-needed revolution towards “translational agriculture”

Internet of things platform for smart farming: experiences and lessons learnt

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations