Dissemination Material



  • Gajinov, S., Popovic, T.,  Drajic, D., Gligoric, N. and Krco, S. (2022) ” Qualitative parameter analysis for Botrytis cinerea forecast modelling using IoT sensor networks’, Journal of Networking and Network Applications, Vol.2, Issue 3, pp,120-135 DOI:10.33969/J-NaNA.2022.020305
  • Reis-Pereira, M.,Tosin, R., Martins, R., Neves dos Santos, F., Tavares, F and Cunha, M. (2022) “Kiwi Plant Canker Diagnosis Using Hyperspectral Signal Processing and Machine Learning: Detecting Symptoms Caused by Pseudomonas syringae pv. actinidiae”, Plants 202211, 2154. https://doi.org/10.3390/plants11162154
  • Gonzalez-Vidal, A., Ramallo-Gonzalez, A.P and Skarmeta, A.F. (2022) ‘Intrinsic and extrinsic quality of data for open data repositories’, ICT Express, https://doi.org/10.1016/j.icte.2022.06.001
  • Roussaki, I., Doolin, K., Skarmeta, A., Routis, G., Lopez-Morales, J., Claffey, E., Mora, M., Martinez, J.A (2022) “Building an interoperable space for smart agriculture”, Digital Communication and Networks, https://doi.org/10.1016/j.dcan.2022.02.004
  • Campos, E.M., Saura, P.F., Gonzales-Vidal, A., Herandez-Ramos, J., Bernabe, J.B., Baldini, G., Skarmeta, A. (2021), “Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges”, https://arxiv.org/abs/2108.00974
  • Charvat K., Bergheim R., Bērziņš R., Zadražil F., Langovskis D., Vrobel J., Horakova S. (2021), “Map Whiteboard Cloud Solution for Collaborative Editing of Geographic Information”. Cloud Computing and Data Science 2(2):36-5. Available from: https://ojs.wiserpub.com/index.php/CCDS/article/view/897 
  • da Silva, D.Q., Aguiar, A.S., dos Santos, F.N., Sousa, A.J., Rabino, D.,  Biddoccu, M., Bagagiolo, G., and Delmastro, M. (2021), ” Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards”. Agriculture 202111, 208. https://doi.org/10.3390/agriculture11030208
  • Azpiroz, I., Oses, N,  Quartulli, M., Olaizola, I.G., Guidotti, D., and Marchi, S (2021) “Comparison of Climate Reanalysis and Remote-Sensing Data for Predicting Olive Phenology through Machine-Learning Methods”. Remote Sensors13, 1224. https://doi.org/10.3390/rs1306122
  • López-Morales, J.A., Martínez J.A. and Skarmeta A.F. (2021), “ Improving Energy Efficiency of Irrigation Wells by Using an IoT-Based Platform”. Electronics, 10(3), 250. https://doi.org/10.3390/electronics10030250 
  • Alcaniz, T., Gonzalez-Vidal, A., Ramallo, A. and Skarmeta, A. (2021), “Quality of Information within Internet of Things Data” Available from DOI: 10.5772/intechopen.95844 
  • Bordel, B.  Alcarria R. and Robles T. “Controlling Supervised Industry 4.0 Processes through Logic Rules and Tensor Deformation Functions,” (2021) Informatica, 1-29. doi:10.15388/20-INFOR441
  • González-Vidal, A.,  Rathore, P., Rao, A.S., Mendoza-Bernal, J., Palaniswami, M. and Skarmeta-Gómez., A.F. (2021), “Missing Data Imputation with Bayesian Maximum Entropy for Internet of Things Applications,” in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2020.2987979
  • Oses,N.,  Azpiroz, I., Marchi, S., Guidotti, D., Quartulli, M. and Olaizol, I.G. (2020), “Analysis of Copernicus’ ERA5 Climate Reanalysis Data as a Replacement for Weather Station Temperature Measurements in Machine Learning Models for Olive Phenology Phase Prediction, “ Sensors 2020, 20, 6381. https://doi.org/10.3390/s20216381
  • López-Morales, J.A., Martínez, J.A., and Skarmeta, A.F. (2020), “Digital Transformation of Agriculture through the Use of an Interoperable Platform” Sensors 202020, 1153. https://doi.org/10.3390/s20041153
  • Jallal, M., Gonzalez-Vidal, A., Skarmeta, A.F, Chabaa, S. and Zeroual, A. (2020) , “A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction” Applied Energy, 268, 10.1016/j.apenergy.2020.114977
  • González-Vidal, A., Alcaniz, T., Iggena, T., Bin Ilyas, E. and Skarmeta, A.F. “Domain Agnostic Quality of Information Metrics in IoT-Based Smart Environments”, Intelligent Environments 2020, doi:10.3233/AISE200059 pdf
  • Bordel, B., Alcarria, R and Robles T. “Supervising Industrial Distributed Processes Through Soft Models, Deformation Metrics and Temporal Logic Rules”. In: Rocha Á., Adeli H., Reis L., Costanzo S., Orovic I., Moreira F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1160. Springer, Cham. https://doi.org/10.1007/978-3-030-45691-7_12
  • Magalhães, S.A.; Castro, L.; Moreira, G.; dos Santos, F.N.; Cunha, M.; Dias, J.; Moreira, A.P. (2021), “Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse”, Sensors, https://doi.org/10.3390/s21103569
  • Grainne Dilleen, Ethel Claffey, Anthony Foley, Kevin Doolin (2023), “Investigating knowledge dissemination and social media use in the farming network to build trust in smart farming technology adoption”, Journal of Business & Industrial Marketing, Volume 38 Issue 8, https://doi.org/10.1108/JBIM-01-2022-0060
  • Juan Antonio Martinez, Juan A Lopez-Morales, Antonio Skarmeta, UMU (2021), “Improving Energy Efficiency of Irrigation Wells by Using an IoT-Based Platform”, Electronics, https://doi.org/10.3390/electronics10030250
  • Juan Antonio López-Morales; Juan Antonio Martínez; Antonio F. Skarmeta (2021), “Climate-Aware and IoT-Enabled Selection of the Most Suitable Stone Fruit Tree Variety”, Sensors, https://doi.org/10.3390/s21113867 
  • Pinheiro I, Aguiar A, Figueiredo A, Pinho T, Valente A, Santos F. (2023), “Nano Aerial Vehicles for Tree Pollination”, Applied Science, https://doi.org/10.3390/app13074265
  • Mario San Emeterio de la Parte, José Fernán Martínez Ortega, Vicente Hernández Díaz, Néstor Lucas Martínez, (2023), “Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability”, Journal of Big Data, 10.1186/s40537-023-00729-0
  • Gligoric, N.; Popovic, T.; Drajic, D.; Gajinov, S.; Krco, S. (2021), “Qualitative Parameter Analysis for Botrytis cinerea Forecast Modelling Using IoT Sensor Networks”, Preprints, https://doi.org/10.20944/preprints202108.0072.v1
  • Sandro Augusto Magalhães, António Paulo Moreira, Filipe Neves dos Santos & Jorge Dias (2022), “Active Perception Fruit Harvesting Robots — A Systematic Review”, Journal of Intelligent Robotic Systems, https://link.springer.com/article/10.1007/s10846-022-01595-3
  • Aurora González-Vidal; José Mendoza-Bernal; Alfonso P. Ramallo; Miguel Ángel Zamora; Vicente Martínez; Antonio F. Skarmeta, (2022), “Smart operation of climatic systems in a greenhouse”, Agriculture, 10.3390/agriculture12101729
  • Sandro Augusto Magalhães ,Luís Castro , Germano Moreira ,Filipe Neves dos Santos , Mário Cunha, Jorge Dias  and António Paulo Moreira (2023), “Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse”, Sensors, https://doi.org/10.3390/s21103569
  • Wolferts, Daniel; Stein, Elisabeth; Bernards, Ann-Kathrin; Reiners, René, (2022), “Differences between remote and analog design thinking through the lens of distributed cognition”, Frontiers in AI, https://doi.org/10.3389/frai.2022.915922
  • Arne J. Berre, Harald Sundmaeker, Daoud Urdu, Stefan Rilling, Ioanna Roussaki, Angelo Margugli, Kevin Doolin, Piotr Zaborowski, Rob Atkinson, Raul Palma, Marianna Faraldi (Submitted July 2023), “Interoperability Architectures for Digital Agri-food Platforms, COMPAG – Computers and Electronics in Agriculture”


  • Azpiroz, I., Quartulli, M. and Olaizola, I.G (2022) ‘Methodology for Online Phenology Prediction Service Creation’ IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, 17-22 July 2022, Kuala Lumpur, Malaysia  10.1109/IGARSS46834.2022.9883164
  • Juska,V.B and O’Riordan, A (2022) ‘Micro-Surface Engineering of Integrated Silicon Microtechnologies for the Development of Sensing and Biosensing Platforms;, ECS Meeting Abstracts, Volume MA2022-02, 2260 DOI 10.1149/MA2022-02612260mtgabs
  • Mueller, S., Plociennik, M. Zacharczuk, M., Fojud, A., Blaszczak, M., Laskowska, A., Palma, R and Wojtowicz, A. (2022) “Leveraging IoT solutions as a base for development of the agriculture advisory services”, 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 1-3 August, Barcelona, Spain  10.1109/COINS54846.2022.9854950
  • Bilbao-Arechabala, S. and Martinez-Rodriguez, B. (2022) “A practical approach to cross-agri-domain interoperability and integration” 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 1-3 August, Barcelona, Spain 10.1109/COINS54846.2022.9854999
  • Routis, G., Paraskevopoulos, M., Vetsikas, I.A, Roussaki, I., Stavrakoudis, D. and Katsantonis, D. (2022) “Data-Driven and Interoperable Smart Agriculture: An IoT-based Use-Case for Arable Crops”, 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 1-3 August, Barcelona, Spain.10.1109/COINS54846.2022.9855001
  • Gallo, P., Daidone, F., Sgroi, F. and Avantaggiato, M. (2022) “AgriChain: Blockchain Syntactic and Semantic Validation for Reducing Information Asymmetry In Agri-Food’, CEUR Workshop Proceedings June 20-23, Rome, Italy http://ceur-ws.org/Vol-3166/paper08.pdf
  • Bordel, B., Alcarria, R., de la Torre, G., Carretero, I. and Robles, T. (2022) “Increasing the efficiency and workers wellbeing in the European Bakery Industry: An Industry 4.0 Case Study”, ICITS 2022, pp646-658 DOI: 10.1007/978-3-030-96293-7_54
  • Abdipourchenarestansofla, M., Schroth, C. (2022) “The importance of data quality assessment for machinery data in the field of agriculture”, 79th International Conference on Agricultural Engineering, Online, DOI:10.51202/9783181023952-495
  • Bordel, B., Alcarria, R., Robles, T., de la Torre, G. and Carretero, I. (2021) ‘Digital user-industry interactions and Industry 4.0 services to improve customers’ experience and satisfaction in the European bakery sector’, 2021 16th Iberian Conference on Information Systems and Technologies (CISTI), 23 – 26 June 2021, Chaves, Portugal, doi: 10.23919/CISTI52073.2021.9476568
  • Fernandez Pesado, P.J., Rubio Melon, A., Escudero Barbero, R., Sanchez Hernandez B., Caceres Losada, J.L, Calero Gil, R. (2021) ‘The Digitalization of The Field. Use Of Remote Sensing and New Technologies as Sustainable Tools In The Management Of Modern Irrigation. R&D&I Projects: Optireg and DEMETER’, XXXVIII Congreso Nacional de Riegos, Cartagena, 3-5 November 2021,  https://repositorio.upct.es/bitstream/handle/10317/10111/B-03-2021.pdf?sequence=4&isAllowed=y
  • M. Płóciennik et al., “Leveraging Agri-food IoT Solutions to Connect Apiary Owners and Farmers,” 2021 16th International Conference on Telecommunications (ConTEL), 2021, pp. 152-157, doi: 10.23919/ConTEL52528.2021.9495980.
  • Bader, S., Pullmann, J., Mader, C., Tramp, S., Quix, C., Muller, A., Akyurek, H., Bockmann, M., Imbusch, B., Lipp, J.,Geilser, S. and Lange, C. (2020), “The International Data Spaces Information Model – An Ontology for Sovereign Exchange of Digital Content”, International Semantic Web Conference 2020, pp 176-192. DOI:10.1007/978-3-030-62466-8_12
  • Bordel, B., Alcarria, R. and Robles, T. (2020) “Supervising Industrial Distributed Processes Through Soft Models, Deformation Metrics and Temporal Logic Rules”, WorldCIST 2020: Trends and Innovations in Information Systems and Technologies, June 2020, https://doi.org/10.1007/978-3-030-45691-7_12
  • R. Arias Calderon, S. Arias Calderon, P. Martins, A. Cordeiro, J.M Silvestre (2020), “Validation of a fluorometer sensor for characterization of olive varieties and evaluation of fruit ripeness index with non-destructive measurement: preliminary results. A: II Simposio Ibérico de Ingeniería Hortícola: Agricultura 4.0: Ponte de Lima, Portugal: 4- 6 de marzo, 2020, p. 489-493.
  • JA. Lopez-Morales, A. F. Skarmeta and J. A. Martinez, “Agri-food Research Centres as Drivers of Digital Transformation for Smart Agriculture,” 2020 Global Internet of Things Summit (GIoTS), Dublin, Ireland, 2020, pp. 1-5, doi: 10.1109/GIOTS49054.2020.9119646.
  • N. Oses, I. Azpiroz, M. Quartulli, I. Olaizola, S. Marchi and D. Guidotti, “Machine Learning for olive phenology prediction and base temperature optimisation,” 2020 Global Internet of Things Summit (GIoTS), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/GIOTS49054.2020.9119611.
  • I. Roussaki, P. Kosmides, G. Routis, K. Doolin, V. Pevtschin and A. Marguglio, “A Multi-Actor Approach to promote the employment of IoT in Agriculture,” 2019 Global IoT Summit (GIoTS), Aarhus, Denmark, 2019, pp. 1-6, doi: 10.1109/GIOTS.2019.8766416.

White Papers

DEMETER White paper 1.3

DEMETER Pilot 1.3 White paper on:
Smart Irrigation Service in Rice and Maize Cultivation

DEMETER White paper 1.4

DEMETER Pilot 1.4 White paper on:
IoT Corn Management and Decision Support Platform

DEMETER White paper 4.2

DEMETER Pilot 4.2 White paper on:
Consumer Awareness: Milk Quality and Animal Welfare Tracking

DEMETER White paper 5.2

DEMETER Pilot 5.2 White paper on:
Farm of Things in Extensive Cattle Holdings

White Paper 2.2

DEMETER Pilot 2.2 White paper on:
Automated Documentation of Arable Crop Processes

White paper pilot 2.1

DEMETER Pilot 2.1 White paper on:
In-Service Condition Monitoring of Agricultural Machinery

White paper pilot 3.1

DEMETER Pilot 3.1 White paper on:
Support system to support Olive Growers

White Paper 3.3

DEMETER Pilot 3.3 White paper on:
Pest Management Control on Fruit Flies

DEMETER White paper web template

DEMETER Pilot 4.3 White paper on:
Proactive Milk Quality Control

White paper pilot 5.3

DEMETER Pilot 5.3 White paper on:
Pollination Optimisation

White Paper - Visualisation

DEMETER White paper on:
DEMETER Advanced Visualisation Tools
April 2022

White Paper - Visualisation (3)

DEMETER White paper on:
DEMETER Decision Enabler and Advisory Support Tools
April 2022

White Paper - Visualisation (2)

DEMETER White paper on:
DEMETER Stakeholder Open Collaboration Space
April 2022

White paper benchmarking (1)

DEMETER White paper on:
 DEMETER Benchmarking and Performance Indicator Tools
November 2021

White Paper - Tech Integration (1)

DEMETER White paper on:
 DEMETER Technology Integration Tools
September 2021

DEMETER White Paper D6.4

DEMETER White paper on:
Regulatory and Policy Framework Analysis
May 2021

DEMETER White paper farmer needs and concerns (2)

DEMETER White paper on:
Farmers’ technology needs and requirements
May 2021

DEMETER White Paper

DEMETER White paper on
Decision Enablers and Advisory Support
April 2021

White paper

Engineering White paper featuring DEMETER
Smart Agriculture
April 2021

Design Principles

Open DEI Position paper (DEMETER contributor)
Design Principles for Data Spaces
April 2021