Periodic Reporting for period 1 - CYBELE (FOSTERING PRECISION AGRICULTURE AND LIVESTOCK FARMING THROUGH SECURE ACCESS TO LARGE-SCALE HPC-ENABLED VIRTUAL INDUSTRIAL EXPERIMENTATION ENVIRONMENT EMPOWERING SCALABLE BIG DATA ANALYTICS)
Reporting period: 2019-01-01 to 2020-06-30
Unfortunately, there still remains major technical challenges in empowering farmers and food producers to realise the full potential of PA/PLF. Primarily the in-accessibility of High Performance Computing platforms that are required to process large and complex data sets.
The CYBELE project aims to foster the use of HPC in PA/PLF applications and will do so by developing a suite of tools that make it much easier and faster for farmers, food producers and related stakeholders to build applications that can be rapidly deployed to HPCs, that can take advantage of pre-defined Airtifical Intelligence powered algorithms to improve decision making, which in turn will improve food quality and yield, and ultimately reduce food waste. Through the close collaboration of 9 demonstrators, the CYBELE project will deliver a state of the art data analytics suite of tools that can be used in production to make HPC more accessible and to improve time to market of new products and services that leverage AI for improved decision making.
The objectives of CYBELE are to:
* Demonstrate the value of CYBELE component in real world settings;
* Promote the technologies and solutions to the AgriFood market, in particular PA/PLF;
* Effectively exploit the results of CYBELE by creating new products and new services;
* Build tools that make HPC systems more accessible;
* Ensure data can be processed securely and appropriately for PA/PFL applications.
Major highlights include the deployment of all software artefacts to all four of the HPC centres involved in the project which represented a coordinated effort across WP2, WP3, WP4 and WP5, the technical WPs in CYBELE.
More precisely, University of Copenhagen participated to the 9th European Conference on Precision Livestock, representing the CYBELE project in the subject of automating monitoring of pen fouling in growing pigs using Convolutional Neural Networks (CNN) applied to images captured above the pens. Also, CERTH presented the CYBELE Common Semantic Model at the 7th International Workshop on Semantic Statistics jointly organised with the International Semantic Web Conference (ISWC) which is the premier venue for presenting fundamental research, innovative technology, and applications concerning semantics, data, and the Web. Last but not least, the CYBELE technical partners (RYAX, HLRS, PSNC, ICCS, LEANSCALE, and UBITECH) presented the paper “Converging HPC, Big Data and Cloud technologies for precision agriculture data analytics on supercomputers” at the 15th Workshop on Virtualization in High-Performance Cloud Computing emphasizing on the ways to improve the co-execution of mixed HPC and Big Data workloads within heterogeneous computing facilities.
Below is a List of Publications extending SotA in the frame of CYBELE
Jensen, D. B., Larsen, M. L. V., & Pedersen, L. J. (2019), Comparison of architectures and training strategies for convolutional neural networks intended for location-specific counting of slaughter pigs. 9th European Conference on Precision Livestock Farming.
Konstantinos Perakis, Fenareti Lampathaki, Konstantinos Nikas, Yiannis Georgiou, Oskar Marko, Jarissa Maselyne. (2020). CYBELE – Fostering precision agriculture & livestock farming through secure access to large-scale HPC enabled virtual industrial experimentation environments fostering scalable big data analytics. Computer Networks, Volume 168, 2020, 107035.
Spiros Mouzakitis, Giannis Tsapelas, Sotiris Pelekis, Simos Ntanopoulos, Dimitris Askounis, Sjoukje Osing, Ioannis N.Athanasiadis (2020). Investigation of common big data analytics and decision-making requirements across diverse precision agriculture and livestock farming use cases. Proceedings of 13th International Symposium on Environmental Software Systems, February 5-7, 2020, Wageningen, The Netherlands.
Samson Damilola Fabiyi, Hai Vu, Christos Tachtatzis, Paul Murray, David Harle, Trung Kien Dao, Ivan Andonovic, Jinchang Ren, Stephen Marshall. (2020). Varietal Classification of Rice Seeds Using RGB and Hyperspectral Images. IEEE Access, vol. 8, pp. 22493-22505, 2020
Yiannis Georgiou, Naweiluo Zhou, Li Zhong, Dennis Hoppe, Marcin Pospieszny, Nikela Papadopoulou, Kostis Nikas, Orestis Lagkas Nikolos, Pavlos Kranas, Sophia Karagiorgou, Eric Pascolo, Michael Mercier and Pedro Velho. Converging HPC, Big Data and Cloud technologies for precision agriculture data analytics on supercomputers. Proceedings of VHPC - ISC 2020.
Dimitris Zeginis, Evangelos Kalampokis, Konstantinos Tarabanis (2019).202fStatistical Challenges Towards a Semantic Model for Precision Agriculture and Precision Livestock Farming 20th International Workshop on Semantic Statistics (SemStats2019) co-located with the 18th International Semantic Web Conference (ISWC2019), CEUR-WS, Vol-2549.