Next Article in Journal
Enhanced Reconstruction of Spatially Incoherent Digital Holograms Using Synthetic Point Spread Holograms
Previous Article in Journal
Textile Tactile Senor Based on Ferroelectret for Gesture Recognition
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

The Cutting Edge on Advances in ICT Systems in Agriculture †

Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th Km Charilaou-Thermi Rd., GR 57001 Thessaloniki, Greece
Department of Supply Chain Management, International Hellenic University, GR 60100 Katerini, Greece
Department of Agritechnology, School of Agricultural Sciences, University of Thessaly, Nea Ionia, GR 38446 Volos, Greece
Author to whom correspondence should be addressed.
Presented at the 13th EFITA International Conference, online, 25–26 May 2021.
Eng. Proc. 2021, 9(1), 46;
Published: 11 March 2022
(This article belongs to the Proceedings of The 13th EFITA International Conference)
Modern agriculture has to shoulder the burden of a plethora of challenges associated with demographics, climate change, and natural resources depletion [1], as well as challenges associated with a new socio-technical framework [2,3]. Therefore, there is a need to increase the effectiveness of agricultural practices and sustainability performance. Current breakthrough technologies are capable of strengthening agriculture for addressing rising needs worldwide. Information and Communication Technology (ICT) has become an integral element of Agriculture 4.0. ICT is a concise term for describing any device, software, application, or network that allows for data collection, exchange, and communication [4]. An illustrative instance of how ICT is incorporated in modern agriculture is provided in Figure 1. The procedure begins by collecting the required data from the field (“Sensors” Phase). Once the data have been gathered, a crucial process is the extraction of important information from them (“Data” Phase). Subsequently, this information is exploited during decision-making for management operations (“Decision-making” Phase). Finally, the essential actions are taken in the field (“Action” Phase) based on the decision-making, and then, this cycle of processes starts again.
The object of this editorial is to summarize the innovative approaches of applying ICT in agriculture, as presented at the 13th International Conference of the European Federation for Information Technology in Agriculture, Food and the Environment (EFITA). Overall, 45 works were presented and classified into one of the aforementioned phases depicted in Figure 1.
Regarding the phase associated with sensors, two review studies were presented on the use of modern technologies for monitoring the impact of wind on trees and forests [5], and the combination of the Internet of Multimedia Things, precision agriculture, and agrifood [6], indicating gaps in these fields. Furthermore, Arink et al. [7] utilized visible and near-infrared spectroscopy for a non-destructive quality assessment, while Hahn et al. [8] investigated the use of inductive and capacitive sensors against trunk dendrometer measurements towards optimizing water use. Filintas [9] presented a soil moisture depletion model for maize crops, whereas Hoxha et al. [10] developed a management decision tool for aromatic and medicinal plants based on GPS and historical inventory data. Kateris et al. [11] developed a new image-based technique for weed mapping in vineyards, considering the height of weeds at the inter-row path of vineyards, and Bataka et al. [12] used statistical methodology to compare open-source with industrial weather stations. Tasiopoulos et al. [13] proposed a methodology based on satellite images and machine learning, for accurate flood extent maps, and Aguilar and Cháves [14] focused on teaching–learning strategies at the University of Costa Rica, as a means of supporting geomatic concepts and tools. Finally, Tagarakis et al. [15] acquired kinematic data from wearable sensors during a human–robot field experiment, aiming to identify human activity signatures.
The second phase that pertains to data analysis was the subject of seven papers. Specifically, Mietzsch et al. [16] summarized the recent developments of AGROVOC, a multilingual thesaurus of the Food and Agriculture Organization of the United Nations (FAO). In [17], the quality of the digital infrastructure in agriculture was assessed, as well as issues preventing its adoption in the USA and Germany. Sáenz et al. [18] identified drought periods in Ecuador in 2001–2018, while Stratakis et al. [19] integrated ambient intelligence technologies for two precision agriculture applications. Additionally, Lallas et al. [20] proposed an ontology supporting the monitoring of the illegal wood trade, whereas Common Greenhouse Ontology was developed in [21] by considering domain experts and existing ontologies. Lastly, a system was developed for calf body weight estimation based on depth images in [22].
The phase related to decision-making concerned the majority of the presented papers. In particular, a decision support system to study the spread of Ailanthus altissima in particular Greek agro-ecosystems was proposed in [23], while a simple decision support system for soybean yield was presented in [24]. A survey on the Greek business contribution to the 17 Sustainable Development Goals was conducted in [25], and factors affecting ICT adoption constituted the matter of [26]. Additionally, a study by Jablanovic [27] pertained to the benefits of ICT investments in China, while Chiem et al. [28] dealt with the reasons affecting the adoption of rice contract-farming policies. Moreover, machine learning algorithms were utilized in three studies. These algorithms were used for (a) grape ripeness estimation [29], (b) predicting the daily prices of sugar and corn in Brazil [30], and (c) crop water availability mapping in the Danube Basin [31]. Based on circular economy concepts, Tagarakis et al. [32] proposed an integrated system via implementing smart farming tools, while Crovella et al. [33] highlighted a key element towards this transition, namely the collaboration between farmers and stakeholders. Silva et al. [34] proposed a data-driven framework for multi-hazard risk mapping in agriculture, whereas in [35], an automatic monitoring system for rainwater harvesting was proposed. Gigot et al. [36] implemented a methodology for wheat production, and Cicuéndez et al. [37] dealt with the identification and modeling of the gross primary production. Finally, Filintas et al. [38] investigated the effects of two irrigation and fertilization treatments on cotton yield and seed oil.
The studies related to action were those of Hahn et al. [39] and Alexandropoulos et al. [40]. In the former, a remotely controlled seawater fertilizer extraction system was developed, whereas the latter evaluated 15 farm-scale greenhouse gas-based decision support tools based on a number of criteria.
Finally, nine studies were classified as addressing cross-cutting themes. Specifically, Spykman et al. [41] and Gabriel and Gandorfer [42] focused on society’s opinion of the use of digital technologies in agriculture. The requirements for adopting blockchain technology and digital technologies by small and medium farms were investigated in [43] and [44], respectively. The role of modern technologies in the implementation of sustainable agriculture was studied in [45], while recommendations for future research pertaining to Earth observation for agricultural applications were presented in [46]. A survey on the importance of interfaces and middleware in agriculture was conducted in [47]. Finally, two studies focused on the molecular and phenotypic diversity of indigenous oenological strains of Saccharomyces cerevisiae [48], as well as kiwifruit genotypes and cultivars evaluation for susceptibility to four strains of Pseudomonas syringae pv. actinidiae (Psa) biovar 3 [49].
In summary, the 45 studies presented at the 13th EFITA conference brought together engineers, scientists, technicians, academics, and industry people for the sake of exchanging knowledge and ideas on the state-of-the-art and future of ICT use in agriculture. As a concluding remark, ICT has the potential to be the driving force towards strengthening agriculture to meet the growing demands for food in a sustainable manner. However, finding ways to facilitate the adoption of new agri-technologies should be prioritized by focusing on farmers’ education and information, while socio-economic factors that affect their assimilation in the field must be considered.

Author Contributions

Conceptualization, All; writing—original draft preparation, All; writing—review and editing, All. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Lampridi, M.G.; Sørensen, C.G.; Bochtis, D. Agricultural Sustainability: A Review of Concepts and Methods. Sustainability 2019, 11, 5120. [Google Scholar] [CrossRef][Green Version]
  2. Marinoudi, V.; Lampridi, M.; Kateris, D.; Pearson, S.; Sørensen, C.G.; Bochtis, D. The Future of Agricultural Jobs in View of Robotization. Sustainability 2021, 13, 12109. [Google Scholar] [CrossRef]
  3. Benos, L.; Bechar, A.; Bochtis, D. Safety and ergonomics in human-robot interactive agricultural operations. Biosyst. Eng. 2020, 200, 55–72. [Google Scholar] [CrossRef]
  4. Benos, L.; Tagarakis, A.C.; Dolias, G.; Berruto, R.; Kateris, D.; Bochtis, D. Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors 2021, 21, 3758. [Google Scholar] [CrossRef] [PubMed]
  5. Faria, J.S.R.; Silva, R.F.; Brazolin, S.; Cugnasca, C.E. Bibliometric Review on the Use of Internet of Things Technologies to Monitor the Impacts of Wind on Trees and Forests. Eng. Proc. 2021, 9, 16. [Google Scholar] [CrossRef]
  6. Mourikis, A.I.; Kalamatianos, R.; Karydis, I.; Avlonitis, M. A Survey on the Use of the Internet of Multimedia Things for Precision Agriculture and the Agrifood Sector. Eng. Proc. 2021, 9, 32. [Google Scholar] [CrossRef]
  7. Arink, M.; Khan, H.A.; Polder, G. Light Penetration Properties of Visible and NIR Radiation in Tomatoes Applied to Non-Destructive Quality Assessment. Eng. Proc. 2021, 9, 18. [Google Scholar] [CrossRef]
  8. Hahn, F.; Espinoza, J.; Zacarías, U. Mango Leaf Monitoring with Inductive and Capacitive Sensors and Its Comparison with Trunk Dendrometer Measurements. Eng. Proc. 2021, 9, 28. [Google Scholar] [CrossRef]
  9. Filintas, A. Soil Moisture Depletion Modelling Using a TDR Multi-Sensor System, GIS, Soil Analyzes, Precision Agriculture and Remote Sensing on Maize for Improved Irrigation-Fertilization Decisions. Eng. Proc. 2021, 9, 36. [Google Scholar] [CrossRef]
  10. Hoxha, V.; Bombaj, F.; Ilbert, H. Construction of an Observatory, As a Management Tool Decision, Valorisation and Sustainable Preservation of the Resources of Aromatic and Medicinal Plants. Eng. Proc. 2021, 9, 23. [Google Scholar] [CrossRef]
  11. Kateris, D.; Kalaitzidis, D.; Moysiadis, V.; Tagarakis, A.C.; Bochtis, D. Weed Mapping in Vineyards Using RGB-D Perception. Eng. Proc. 2021, 9, 30. [Google Scholar] [CrossRef]
  12. Bataka, E.P.; Miliokas, G.; Katsoulas, N.; Nakas, C.T. A Method Comparison Study between Open Source and Industrial Weather Stations. Eng. Proc. 2021, 9, 8. [Google Scholar] [CrossRef]
  13. Tasiopoulos, L.; Stefouli, M.; Voutos, Y.; Mylonas, P.; Charou, E. Machine Learning Techniques in Agricultural Flood Assessment and Monitoring Using Earth Observation and Hydromorphological Analysis. Eng. Proc. 2021, 9, 40. [Google Scholar] [CrossRef]
  14. Aguilar, J.; Cháves, M. A Review of a Teaching– Learning Strategy Change to Strengthen Geomatic Concepts and Tools in the Biosystems Engineering Academic Studies at the Universidad de Costa Rica. Eng. Proc. 2021, 9, 44. [Google Scholar] [CrossRef]
  15. Tagarakis, A.C.; Benos, L.; Aivazidou, E.; Anagnostis, A.; Kateris, D.; Bochtis, D. Wearable Sensors for Identifying Activity Signatures in Human-Robot Collaborative Agricultural Environments. Eng. Proc. 2021, 9, 5. [Google Scholar] [CrossRef]
  16. Mietzsch, E.; Martini, D.; Kolshus, K.; Turbati, A.; Subirats, I. How Agricultural Digital Innovation Can Benefit from Semantics: The Case of the AGROVOC Multilingual Thesaurus. Eng. Proc. 2021, 9, 17. [Google Scholar] [CrossRef]
  17. Bernhardt, H.; Schumacher, L.; Zhou, J.; Treiber, M.; Shannon, K. Digital Agriculture Infrastructure in the USA and Germany. Eng. Proc. 2021, 9, 1. [Google Scholar] [CrossRef]
  18. Sáenz, C.; Litago, J.; Wiese, K.; Recuero, L.; Cicuéndez, V.; Palacios-Orueta, A. Drought Periods Identification in Ecuador between 2001 and 2018 Using SPEI and MODIS Data. Eng. Proc. 2021, 9, 24. [Google Scholar] [CrossRef]
  19. Stratakis, C.; Stivaktakis, N.; Bouloukakis, M.; Leonidis, A.; Doxastaki, M.; Kapnas, G.; Evdaimon, T.; Korozi, M.; Kalligiannakis, E.; Stephanidis, C. Integrating Ambient Intelligence Technologies for Empowering Agriculture. Eng. Proc. 2021, 9, 41. [Google Scholar] [CrossRef]
  20. Lallas, E.; Karageorgos, A.; Ntalos, G. An Ontology Based Approach for Regulatory Compliance of EU Reg. No 995/2010 in Greece. Eng. Proc. 2021, 9, 38. [Google Scholar] [CrossRef]
  21. Bakker, R.; van Drie, R.; Bouter, C.; van Leeuwen, S.; van Rooijen, L.; Top, J. The Common Greenhouse Ontology: An Ontology Describing Components, Properties, and Measurements inside the Greenhouse. Eng. Proc. 2021, 9, 27. [Google Scholar] [CrossRef]
  22. Yamamoto, Y.; Ohkawa, T.; Ohta, C.; Oyama, K.; Nishide, R. Depth Image Selection Based on Posture for Calf Body Weight Estimation. Eng. Proc. 2021, 9, 20. [Google Scholar] [CrossRef]
  23. Voutos, Y.; Godsil, N.; Sotiropoulou, A.; Mylonas, P.; Bouchagier, P.; Exarchos, T.; Martinis, A.; Kabassi, K. Capturing and evaluating the effects of the expansive species Ailanthus altissima on agro-ecosystems at the Ionian Islands. Eng. Proc. 2021, 9, 19. [Google Scholar] [CrossRef]
  24. Nitta, A.; Chonan, Y.; Hayashi, S.; Nakamura, T.; Tsuji, H.; Murakami, N.; Nishide, R.; Ohkawa, T.; Ozawa, S. An Easily Installed Method of the Estimation of Soybean Yield Based on Meteorological Environments with Regression Analysis. Eng. Proc. 2021, 9, 26. [Google Scholar] [CrossRef]
  25. Chrysos-Anestis, A.; Achillas, C.; Folinas, D.; Aidonis, D.; Anestis, M.C. Sensitivity of Greek Organisations in Sustainability Issues. Eng. Proc. 2021, 9, 4. [Google Scholar] [CrossRef]
  26. Romanelli, T.L.; Muñoz-Arriola, F.; Colaço, A.F. Conceptual Framework to Integrate Economic Drivers of Decision Making for Technology Adoption in Agriculture. Eng. Proc. 2021, 9, 43. [Google Scholar] [CrossRef]
  27. Jablanovic, V. Investment in Information and Communication Technology in Agriculture and Soybean Production Stability: The Case of China. Eng. Proc. 2021, 9, 34. [Google Scholar] [CrossRef]
  28. Tuyen, M.C.; Sirisupluxana, P.; Bunyasiri, I.; Hung, P.X. Rice Contract Farming in Vietnam: Insights from a Qualitative Study. Eng. Proc. 2021, 9, 6. [Google Scholar] [CrossRef]
  29. Vrochidou, E.; Bazinas, C.; Papakostas, G.A.; Pachidis, T.; Kaburlasos, V.G. A Review of the State-of-Art, Limitations, and Perspectives of Machine Vision for Grape Ripening Estimation. Eng. Proc. 2021, 9, 2. [Google Scholar] [CrossRef]
  30. Silva, R.F.; Barreira, B.L.; Cugnasca, C.E. Prediction of Corn and Sugar Prices Using Machine Learning, Econometrics, and Ensemble Models. Eng. Proc. 2021, 9, 31. [Google Scholar] [CrossRef]
  31. Migdall, S.; Dotzler, S.; Gleisberg, E.; Appel, F.; Muerth, M.; Bach, H.; Weikmann, G.; Paris, C.; Marinelli, D.; Bruzzone, L. Crop Water Availability Mapping in the Danube Basin Based on Deep Learning, Hydrological and Crop Growth Modelling. Eng. Proc. 2021, 9, 42. [Google Scholar] [CrossRef]
  32. Tagarakis, A.C.; Dordas, C.; Lampridi, M.; Kateris, D.; Bochtis, D. A Smart Farming System for Circular Agriculture. Eng. Proc. 2021, 9, 10. [Google Scholar] [CrossRef]
  33. Crovella, T.; Paiano, A.; Lagioia, G.; Cilardi, A.M.; Trotta, L. Modelling Digital Circular Economy framework in the Agricultural Sector. An Application in Southern Italy. Eng. Proc. 2021, 9, 15. [Google Scholar] [CrossRef]
  34. Silva, R.F.; Fava, M.C.; Saraiva, A.M.; Mendiondo, E.M.; Cugnasca, C.E.; Delbem, A.C.B. A Theoretical Framework for Multi-Hazard Risk Mapping on Agricultural Areas Considering Artificial Intelligence, IoT, and Climate Change Scenarios. Eng. Proc. 2021, 9, 39. [Google Scholar] [CrossRef]
  35. Brenes, R.; Torres, A.; Aguilar, J.; Aguilar, R. Awareness Raising and Capacity Building through a Scalable Automatic Water Harvest Monitoring System to Improve Water Resource Management in Monteverde Community, Costa Rica. Eng. Proc. 2021, 9, 45. [Google Scholar] [CrossRef]
  36. Gigot, C.; Hamernig, D.; Deytieux, V.; Diallo, I.; Deudon, O.; Gourdain, E.; Aubertot, J.-N.; Robin, M.-H.; Bancal, M.-O.; Huber, L.; et al. Developing a Method to Simulate and Evaluate Effects of Adaptation Strategies to Climate Change on Wheat Crop Production: A Challenging Multi-Criteria Analysis. Eng. Proc. 2021, 9, 21. [Google Scholar] [CrossRef]
  37. Cicuéndez, V.; Litago, J.; Sánchez-Girón, V.; Recuero, L.; Sáenz, C.; Palacios-Orueta, A. Identification and Modeling Carbon and Energy Fluxes from Eddy Covariance Time Series Measurements in Rice and Rainfed Crops. Eng. Proc. 2021, 9, 9. [Google Scholar] [CrossRef]
  38. Filintas, A.; Nteskou, A.; Katsoulidi, P.; Paraskebioti, A.; Parasidou, M. Rainfed and Supplemental Irrigation Modelling 2D GIS Moisture Rootzone Mapping on Yield and Seed Oil of Cotton (Gossypium hirsutum) Using Precision Agriculture and Remote Sensing. Eng. Proc. 2021, 9, 37. [Google Scholar] [CrossRef]
  39. Hahn, F.; González, C.J.; Delfín, C.M. Production of Fertilizer from Seawater with a Remote Control System. Eng. Proc. 2021, 9, 29. [Google Scholar] [CrossRef]
  40. Alexandropoulos, E.; Anestis, V.; Bartzanas, T. Farm-Scale Greenhouse Gas Emissions’ Decision Support Systems. Eng. Proc. 2021, 9, 22. [Google Scholar] [CrossRef]
  41. Spykman, O.; Emberger-Klein, A.; Gabriel, A.; Gandorfer, M. Society’s View on Autonomous Agriculture: Does Digitalization Lead to Alienation? Eng. Proc. 2021, 9, 12. [Google Scholar] [CrossRef]
  42. Gabriel, A.; Gandorfer, M. Have City Dwellers Lost Touch with Modern Agriculture? In Quest of Differences between Urban and Rural Population. Eng. Proc. 2021, 9, 25. [Google Scholar] [CrossRef]
  43. Aranda, R.S.; Silva, R.F.; Cugnasca, C.E. Requirements Identification for a Blockchain-Based Traceability Model for Animal-Based Medicines. Eng. Proc. 2021, 9, 11. [Google Scholar] [CrossRef]
  44. Sodano, V. Tailored Digitization for Rural Development. Eng. Proc. 2021, 9, 14. [Google Scholar] [CrossRef]
  45. Popescu, L.; Safta, A.S. Analysis of the Concept of Feasibility in Sustainable Agricultural Systems. Eng. Proc. 2021, 9, 13. [Google Scholar] [CrossRef]
  46. Charvat, K.; Safar, V.; Kubickova, H.; Horakova, S.; Mildorf, T. Strategic Research Agenda for Utilisation of Earth Observation in Agriculture. Eng. Proc. 2021, 9, 35. [Google Scholar] [CrossRef]
  47. Treiber, M.; Bernhardt, H. NEVONEX—The Importance of Middleware and Interfaces for the Digital Transformation of Agriculture. Eng. Proc. 2021, 9, 3. [Google Scholar] [CrossRef]
  48. Karampatea, A.; Tsakiris, A.; Kourkoutas, Y.; Skavdis, G. Molecular and Phenotypic Diversity of Indigenous Oenological Strains of Saccharomyces cerevisiae Isolated in Greece. Eng. Proc. 2021, 9, 7. [Google Scholar] [CrossRef]
  49. Thomidis, T.; Goumas, D.E.; Zotos, A.; Triantafyllidis, V.; Kokotos, E. Susceptibility of Twenty-Three Kiwifruit Cultivars to Pseudomonas syringae pv. actinidiae. Eng. Proc. 2021, 9, 33. [Google Scholar] [CrossRef]
Figure 1. The four phases of modern agriculture through exploiting ICT.
Figure 1. The four phases of modern agriculture through exploiting ICT.
Engproc 09 00046 g001
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Lampridi, M.; Benos, L.; Aidonis, D.; Kateris, D.; Tagarakis, A.C.; Platis, I.; Achillas, C.; Bochtis, D. The Cutting Edge on Advances in ICT Systems in Agriculture. Eng. Proc. 2021, 9, 46.

AMA Style

Lampridi M, Benos L, Aidonis D, Kateris D, Tagarakis AC, Platis I, Achillas C, Bochtis D. The Cutting Edge on Advances in ICT Systems in Agriculture. Engineering Proceedings. 2021; 9(1):46.

Chicago/Turabian Style

Lampridi, Maria, Lefteris Benos, Dimitrios Aidonis, Dimitrios Kateris, Aristotelis C. Tagarakis, Ilias Platis, Charisios Achillas, and Dionysis Bochtis. 2021. "The Cutting Edge on Advances in ICT Systems in Agriculture" Engineering Proceedings 9, no. 1: 46.

Article Metrics

Back to TopTop