Skip to main content
SpringerLink
Log in

Application Possibilities of IoT-based Management Systems in Agriculture

  • Chapter
  • First Online:
Information and Communication Technologies for Agriculture—Theme II: Data

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 183))

Abstract

The optimization of agricultural production and business processes is a crucial task in order to fulfill the demand of the increasing population, to meet quality requirements, to reduce the environmental impact as well as to improve economic efficiency. The Industry 4.0 concept provides various methods in this regard, including data acquisition based on IoT (Internet of Things), or data analytics based on Big Data, to support the decision-making process of the management and the data requirement of process control methods. During preliminary research, several modular data acquisition systems, as well as management applications have been developed based on a production system to measure various environmental factors at multiple spatial points. Considering the experience gained from the testing sessions, there was a need for further development regarding the end-user perspective in order to substantiate the practical application. A comparative research was required, considering previous experience and the literature of data acquisition systems, used in agriculture. The comparison concerned an own iteration of a production system and other systems, developed by researchers of the field, to examine different options and directions. Considering three important factors, the focus was on the data acquisition systems, data management, and data utilization methods. The comparison begins with a quantitative bibliometric analysis, determining the field and characteristic connections using network and cluster analysis, considering the IoT concept as the central element. Subsequently, the progression of a system and its evaluation is presented, performed in a greenhouse. This iteration highly focuses on data management with the modification of the existing infrastructure by integrating the Hadoop ecosystem to achieve a standardized interface.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 59.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science (80), 327, 828–831. https://doi.org/10.1126/science.1183899

    Article  Google Scholar 

  2. FAO (2009). Global agriculture towards 2050.

    Google Scholar 

  3. Lee, M., Hwang, J., & Yoe, H. (2013). Agricultural production system based on IoT. Proceedings of the 2013 IEEE 16th International Conference on Computational Science and Engineering CSE, 833–837. https://doi.org/10.1109/CSE.2013.126

  4. Schuster E, Kumar S, Sarma S, et al. (2011). Infrastructure for data-driven agriculture: Identifying management zones for cotton using statistical modeling and machine learning techniques. In: 2011 8th International Conference & Expo on emerging Technologies for a Smarter World. IEEE, pp. 1–6.

    Google Scholar 

  5. Bongiovanni, R., & Lowenberg-Deboer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5, 359–387. https://doi.org/10.1023/B:PRAG.0000040806.39604.aa

    Article  Google Scholar 

  6. Schimmelpfennig, D., & Ebel, R. (2016). Sequential adoption and cost savings from precision agriculture. Journal of Agricultural and Resource Economics, 41, 97–115.

    Google Scholar 

  7. Lasi, H., Fettke, P., Kemper, H.-G., et al. (2014). Industry 4.0. Business and Information Systems Engineering, 6, 239–242. https://doi.org/10.1007/s12599-014-0334-4

    Article  Google Scholar 

  8. Bartodziej, C. J. (2017). The concept industry 4.0. Springer Fachmedien Wiesbaden.

    Book  Google Scholar 

  9. Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0 - a glimpse. Procedia Manufacturing, 20, 233–238. https://doi.org/10.1016/j.promfg.2018.02.034

    Article  Google Scholar 

  10. Babiceanu, R. F., & Seker, R. (2016). Big data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Computers in Industry, 81, 128–137. https://doi.org/10.1016/j.compind.2016.02.004

    Article  Google Scholar 

  11. Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31–48. https://doi.org/10.1016/j.biosystemseng.2017.09.007

    Article  Google Scholar 

  12. Clasen, M. (2016). Farming 4.0 und andere Anwendungen des Internet der Dinge. Inform der Land-, Forst- und Ernährungswirtschaft, 2016, 33–36.

    Google Scholar 

  13. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming – A review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023

    Article  Google Scholar 

  14. Braun, A. T., Colangelo, E., & Steckel, T. (2018). Farming in the era of Industrie 4.0. Procedia CIRP, 72, 979–984. https://doi.org/10.1016/j.procir.2018.03.176

    Article  Google Scholar 

  15. Voutos, Y., Mylonas, P., Katheniotis, J., & Sofou, A. (2019). A survey on intelligent agricultural information handling methodologies. Sustainability, 11, 3278. https://doi.org/10.3390/su11123278

    Article  Google Scholar 

  16. Elijah, O., Rahman, T. A., Orikumhi, I., et al. (2018). An overview of internet of things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet of Things Journal, 5, 3758–3773. https://doi.org/10.1109/JIOT.2018.2844296

    Article  Google Scholar 

  17. Borgia, E. (2014). The internet of things vision: Key features, applications and open issues. Computer Communications, 54, 1–31. https://doi.org/10.1016/j.comcom.2014.09.008

    Article  Google Scholar 

  18. Fahmideh, M., & Zowghi, D. (2020). An exploration of IoT platform development. Information Systems, 87, 101409. https://doi.org/10.1016/j.is.2019.06.005

    Article  Google Scholar 

  19. Ashton, K. (2009). That “internet of things” thing. RFiD Journal, 22, 97–114.

    Google Scholar 

  20. Da Xu, L., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56, 2941–2962. https://doi.org/10.1080/00207543.2018.1444806

    Article  Google Scholar 

  21. Stamenkovi, Z., Randji, S., Santamaria, I., et al. (2016). Advanced wireless sensor nodes and networks for agricultural applications. https://doi.org/10.1109/TELFOR.2016.7818709

  22. Gilchrist, A. (2016). Industry 4.0. Apress.

    Book  Google Scholar 

  23. Talavera, J. M., Tobón, L. E., Gómez, J. A., et al. (2017). Review of IoT applications in agro-industrial and environmental fields. Computers and Electronics in Agriculture, 142, 283–297. https://doi.org/10.1016/j.compag.2017.09.015

    Article  Google Scholar 

  24. Othman, M. F., & Shazali, K. (2012). Wireless sensor network applications: A study in environment monitoring system. Procedia Engineering, 41, 1204–1210. https://doi.org/10.1016/j.proeng.2012.07.302

    Article  Google Scholar 

  25. Verdouw, C. (2016). Internet of things in agriculture. CAB Reviews: Perspectives in Agriculture, Veterinary Science Nutrition and Natural Resources, 11, 1–12. https://doi.org/10.1079/PAVSNNR201611035

    Article  Google Scholar 

  26. Park, D. H., & Park, J. W. (2011). Wireless sensor network-based greenhouse environment monitoring and automatic control system for dew condensation prevention. Sensors, 11, 3640–3651. https://doi.org/10.3390/s110403640

    Article  Google Scholar 

  27. Su, Y., & Xu, L. (2017). Towards discrete time model for greenhouse climate control. Engineering in Agriculture, Environment and Food, 10, 157–170. https://doi.org/10.1016/j.eaef.2017.01.001

    Article  Google Scholar 

  28. Baviskar J, Mulla A, Baviskar A, et al (2014) Real time monitoring and control system for green house based on 802.15.4 wireless sensor network. 2014 Fourth Int Conf Commun Syst Netw Technol. 98–103. https://doi.org/10.1109/CSNT.2014.28.

  29. Tóth, M., Felföldi, J., & Szilágyi, R. (2019). Possibilities of IoT based management system in greenhouses. Georg Agricultural, 23, 43–62.

    Google Scholar 

  30. Barreto, L., Amaral, A., & Pereira, T. (2017). Industry 4.0 implications in logistics: An overview. Procedia Manufacturing, 13, 1245–1252. https://doi.org/10.1016/j.promfg.2017.09.045

    Article  Google Scholar 

  31. Wu, X., Xu, W., Song, Y., & Cai, M. (2011). A detection method of weed in wheat field on machine vision. Procedia Engineering, 15, 1998–2003. https://doi.org/10.1016/j.proeng.2011.08.373

    Article  Google Scholar 

  32. Habib, M. T., Majumder, A., Jakaria, A. Z. M., et al. (2018). Machine vision based papaya disease recognition. Journal of King Saud University Computer and Information Sciences, 32, 300–309. https://doi.org/10.1016/j.jksuci.2018.06.006

    Article  Google Scholar 

  33. Momin, M. A., Yamamoto, K., Miyamoto, M., et al. (2017). Machine vision based soybean quality evaluation. Computers and Electronics in Agriculture, 140, 452–460. https://doi.org/10.1016/j.compag.2017.06.023

    Article  Google Scholar 

  34. Unay, D., Gosselin, B., Kleynen, O., et al. (2011). Automatic grading of bi-colored apples by multispectral machine vision. Computers and Electronics in Agriculture, 75, 204–212. https://doi.org/10.1016/j.compag.2010.11.006

    Article  Google Scholar 

  35. Blok, P. M., Barth, R., & van den Berg, W. (2016). Machine vision for a selective broccoli harvesting robot. IFAC-PapersOnLine, 49, 66–71. https://doi.org/10.1016/j.ifacol.2016.10.013

    Article  MathSciNet  Google Scholar 

  36. Pham, X., & Stack, M. (2018). How data analytics is transforming agriculture. Business Horizons, 61, 125–133. https://doi.org/10.1016/j.bushor.2017.09.011

    Article  Google Scholar 

  37. Ghiwari, S., Sambrekar, K., & Rajpurohit, V. S. (2018). Hierarchical storage for agro informatics system using NoSQL technology. 2017 International Conference on Computing, Communication, Control and Automation ICCUBEA, 2017, 1–5. https://doi.org/10.1109/ICCUBEA.2017.8463693

    Article  Google Scholar 

  38. Hammer, B., He, H., & Martinetz, T. (2014). Learning and modeling big data. 22th European Symposium on Artificial Neural Networks, 23–25.

    Google Scholar 

  39. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19, 171–209. https://doi.org/10.1007/s11036-013-0489-0

    Article  Google Scholar 

  40. Bendre, M. R., Thool, R. C., & Thool, V. R. (2016, 2015). Big data in precision agriculture: Weather forecasting for future farming. Proceedings of 2015 1st International Conference on Next Generation Computing Technologies NGCT, 744–750. https://doi.org/10.1109/NGCT.2015.7375220

  41. Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing and Management, 54, 758–790. https://doi.org/10.1016/j.ipm.2018.01.010

    Article  Google Scholar 

  42. Seddon, J. J. J. M., & Currie, W. L. (2017). A model for unpacking big data analytics in high-frequency trading. Journal of Business Research, 70, 300–307. https://doi.org/10.1016/j.jbusres.2016.08.003

    Article  Google Scholar 

  43. Xie, N., Wang, W., Ma, B., et al. (2015). Research on an agricultural knowledge fusion method for big data. Data Science Journal, 14, 7. https://doi.org/10.5334/dsj-2015-007

    Article  Google Scholar 

  44. Liakos, K. G., Busato, P., Moshou, D., et al. (2018). Machine learning in agriculture: A review. Sensors (Switzerland), 18, 1–29. https://doi.org/10.3390/s18082674

    Article  Google Scholar 

  45. Shin, J.-Y., Kim, K. R., & Ha, J.-C. (2020). Seasonal forecasting of daily mean air temperatures using a coupled global climate model and machine learning algorithm for field-scale agricultural management. Agricultural and Forest Meteorology, 281, 107858. https://doi.org/10.1016/j.agrformet.2019.107858

    Article  Google Scholar 

  46. Sannakki, S., Rajpurohit, V. S., Sumira, F., & Venkatesh, H. (2013). A neural network approach for disease forecasting in grapes using weather parameters. 2013 Fourth International Conference on Computing, Communications and Networking TechnologiesICCCNT, 2013, 2–6. https://doi.org/10.1109/ICCCNT.2013.6726613

    Article  Google Scholar 

  47. Veenadhari, S., Misra, B., & Singh, C. D. (2014). Machine learning approach for forecasting crop yield based on climatic parameters. 2014 International Conference on Computer Communication and Informatics Usher Technologies Tomorrow, Today, ICCCI, 2014, 1–5. https://doi.org/10.1109/ICCCI.2014.6921718

    Article  Google Scholar 

  48. Himesh, S., Prakasa Rao, E. V. S., Gouda, K. C., et al. (2018). Digital revolution and big data: A new revolution in agriculture. CAB Reviews: Perspectives in Agriculture, Veterinary Science Nutrition and Natural Resource, 13. https://doi.org/10.1079/PAVSNNR201813021

  49. Tóth, M., & Szilágyi, R. (2017). Development and testing experiences of a management supporting data acquisition system. Journal of Agricultural Informatics, 8, 55–70. https://doi.org/10.17700/jai.2017.8.2.382

    Article  Google Scholar 

  50. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11, 959–975. https://doi.org/10.1016/j.joi.2017.08.007

    Article  Google Scholar 

  51. Kamada, T., & Kawai, S. (1989). An algorithm for drawing general undirected graphs. Information Processing Letters, 31, 7–15. https://doi.org/10.1016/0020-0190(89)90102-6

    Article  MathSciNet  MATH  Google Scholar 

  52. Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5, 146–166. https://doi.org/10.1016/j.joi.2010.10.002

    Article  Google Scholar 

  53. Nabil Mohammed, M. A., & Govardhan, A. (2010). A comparison between five models of software engineering. International Journal of Computer Science, 7, 94–101.

    Google Scholar 

  54. Axinte S-D, Petrica G, Barbu I-D (2017) E-learning platform development model. In: 2017 10th international symposium on advanced topics in electrical engineering (ATEE). IEEE, pp. 687–692.

    Google Scholar 

  55. Tóth, M., & Szilágyi, R. (2017). Gazdálkodást támogató adatgyűjtő rendszer fejlesztése és tesztelése. In Informatika a felsőoktatásban 2017 konferencia (pp. 453–461). Debrecen.

    Google Scholar 

  56. Verdouw, C. N., Beulens, A. J. M., Reijers, H. A., & Van Der Vorst, J. G. A. J. (2015). A control model for object virtualization in supply chain management. Computers in Industry, 68, 116–131. https://doi.org/10.1016/j.compind.2014.12.011

    Article  Google Scholar 

  57. Bin, J. I., Raihana, K., Bhowmik, S., & Shakil, S. R. (2014). Wireless monitoring system and controlling software for smart greenhouse management. 2014 Int Conf informatics. Electron Vision, ICIEV, 2014, 1–5. https://doi.org/10.1109/ICIEV.2014.6850748

    Article  Google Scholar 

  58. Bajer, L., & Krejcar, O. (2015). Design and realization of low cost control for greenhouse environment with remote control. IFAC-PapersOnLine, 28, 368–373. https://doi.org/10.1016/j.ifacol.2015.07.062

    Article  Google Scholar 

  59. dos Santos, U. J. L., Pessin, G., da Costa, C. A., & da Rosa, R. R. (2019). AgriPrediction: A proactive internet of things model to anticipate problems and improve production in agricultural crops. Computers and Electronics in Agriculture, 161, 202–213. https://doi.org/10.1016/j.compag.2018.10.010

    Article  Google Scholar 

  60. Yoon, C., Huh, M., Kang, S. G., et al. (2018). Implement smart farm with IoT technology. International Conference on Advanced Communication Technology ICACT, 2018, 749–752. https://doi.org/10.23919/ICACT.2018.8323908

    Article  Google Scholar 

  61. Gorrepotu, R., Korivi, N. S., Chandu, K., & Deb, S. (2018). Sub-1GHz miniature wireless sensor node for IoT applications. Internet of Things, 1–2, 27–39. https://doi.org/10.1016/j.iot.2018.08.002

    Article  Google Scholar 

  62. Reka, S. S., Chezian, B. K., & Chandra, S. S. (2019). A novel approach of IoT-based smart greenhouse farming system (pp. 227–235). Springer.

    Google Scholar 

  63. Azaza, M., Tanougast, C., Fabrizio, E., & Mami, A. (2016). Smart greenhouse fuzzy logic based control system enhanced with wireless data monitoring. ISA Transactions, 61, 297–307. https://doi.org/10.1016/j.isatra.2015.12.006

    Article  Google Scholar 

  64. Akkaş, M. A., & Sokullu, R. (2017). An IoT-based greenhouse monitoring system with Micaz motes. Procedia Computer Science, 113, 603–608. https://doi.org/10.1016/j.procs.2017.08.300

    Article  Google Scholar 

  65. Abd El-Kader, S. M., & Mohammad El-Basioni, B. M. (2013). Precision farming solution in Egypt using the wireless sensor network technology. Egyptian Informatics Journal, 14, 221–233. https://doi.org/10.1016/j.eij.2013.06.004

    Article  Google Scholar 

  66. Goumopoulos, C., O’Flynn, B., & Kameas, A. (2014). Automated zone-specific irrigation with wireless sensor/actuator network and adaptable decision support. Computers and Electronics in Agriculture, 105, 20–33. https://doi.org/10.1016/j.compag.2014.03.012

    Article  Google Scholar 

  67. Suresh VM, Sidhu R, Karkare P, et al (2018) Powering the IoT through embedded machine learning and LoRa. IEEE World Forum Internet Things, WF-IoT 2018 - Proc 2018-Janua: 349–354. https://doi.org/10.1109/WF-IoT.2018.8355177.

  68. Yiming, Z., Xianglong, Y., Xishan, G., et al. (2007). A design of greenhouse monitoring & control system based on ZigBee wireless sensor network. Wireless Communications, Networking and Mobile Computing 2007 WiCom 2007 International Conference, 2563–2567. https://doi.org/10.1109/WICOM.2007.638

  69. Stočes, M., Vaněk, J., Masner, J., & Pavlík, J. (2016). Internet of things (IoT) in agriculture - selected aspects. Agris On-line Papers in Economics and Informatics, 8, 83–88. https://doi.org/10.7160/aol.2016.080108

    Article  Google Scholar 

  70. Keshtgari, M., & Deljoo, A. (2012). A wireless sensor network solution for precision agriculture based on Zigbee technology. Wireless Sensor Network, 04, 25–30. https://doi.org/10.4236/wsn.2012.41004

    Article  Google Scholar 

  71. Tafa, Z., Ramadani, F., & Cakolli, B. (2018). The design of a ZigBee-based greenhouse monitoring system. In 2018 7th Mediterranean conference on embedded computing (MECO) (pp. 1–4). IEEE.

    Google Scholar 

  72. Dan, L., Jianmei, S., Yang, Y., & Jianqiu, X. (2017). Precise agricultural greenhouses based on the IoT and fuzzy control. In Proceedings - 2016 international conference on intelligent transportation, big data and smart city. ICITBS 2016.

    Google Scholar 

  73. Castello, C. C., Fan, J., Davari, A., & Chen, R. X. (2010). Optimal sensor placement strategy for environmental monitoring using wireless sensor networks. Proceedings of the Annual Southeastern Symposium on System Theory, 275–279. https://doi.org/10.1109/SSST.2010.5442825

  74. Polato, I., Ré, R., Goldman, A., & Kon, F. (2014). A comprehensive view of Hadoop research - a systematic literature review. Journal of Network and Computer Applications, 46, 1–25. https://doi.org/10.1016/j.jnca.2014.07.022

    Article  Google Scholar 

  75. Hadi, M. S., Lawey, A. Q., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2018). Big data analytics for wireless and wired network design: A survey. Computer Networks, 132, 180–199. https://doi.org/10.1016/j.comnet.2018.01.016

    Article  Google Scholar 

  76. Buyya, R., Calheiros, R. N., & Vahid Dastjerdi, A. (2016). Big data: Principles and paradigms. Morgan Kaufmann.

    Google Scholar 

  77. Pandey, A. K., Agrawal, C. P., & Agrawal, M. (2017). A hadoop based weather prediction model for classification of weather data. In 2017 second international conference on electrical (pp. 1–5). Computer and Communication Technologies (ICECCT).

    Google Scholar 

  78. Chi, M., Plaza, A., Benediktsson, J. A., et al. (2016). Big data for remote sensing: Challenges and opportunities. Proceedings of the IEEE, 104, 2207–2219. https://doi.org/10.1109/JPROC.2016.2598228

    Article  Google Scholar 

  79. Lamrhari, S., Elghazi, H., Sadiki, T., & El Faker, A. (2016). A profile-based big data architecture for agricultural context. Proc 2016 Int Conf Electr Inf Technol ICEIT 2016. 22–27. https://doi.org/10.1109/EITech.2016.7519585

  80. Yan, J., Xin, S., Liu, Q., et al. (2014). Intelligent supply chain integration and management based on cloud of things. International Journal of Distributed Sensor Networks, 2014. https://doi.org/10.1155/2014/624839

  81. Minh QT, Phan TN, Takahashi A, et al (2017) A cost-effective smart farming system with knowledge base. In: ACM International Conference Proceeding Series. pp. 309–316.

    Google Scholar 

  82. Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037

    Article  Google Scholar 

  83. Wiska R, Habibie N, Wibisono A, et al (2017) Big sensor-generated data streaming using Kafka and Impala for data storage in Wireless Sensor Network for CO2 monitoring. 2016 Int Work Big Data Inf Secur IWBIS. 97–101. https://doi.org/10.1109/IWBIS.2016.7872896.

  84. Gallinucci, E., Golfarelli, M., & Rizzi, S. (2019). A hybrid architecture for tactical and strategic precision agriculture (pp. 13–23). Springer International Publishing.

    Google Scholar 

  85. Jardak, C., Riihijärvi, J., Oldewurtel, F., & Mähönen, P. (2010). Parallel processing of data from very large-scale wireless sensor networks. HPDC 2010 - Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, 787–794. https://doi.org/10.1145/1851476.1851590

  86. Goldstein, A., Fink, L., Meitin, A., et al. (2018). Applying machine learning on sensor data for irrigation recommendations: Revealing the agronomist’s tacit knowledge. Precision Agriculture, 19, 421–444. https://doi.org/10.1007/s11119-017-9527-4

    Article  Google Scholar 

  87. Habaragamuwa, H., Ogawa, Y., Suzuki, T., et al. (2018). Detecting greenhouse strawberries (mature and immature), using deep convolutional neural network. Engineering in Agriculture, Environment and Food, 11, 127–138. https://doi.org/10.1016/j.eaef.2018.03.001

    Article  Google Scholar 

  88. Uchida Frausto, H., & Pieters, J. G. (2004). Modelling greenhouse temperature using system identification by means of neural networks. Neurocomputing, 56, 423–428. https://doi.org/10.1016/j.neucom.2003.08.001

    Article  Google Scholar 

  89. Ehret, D. L., Hill, B. D., Raworth, D. A., & Estergaard, B. (2008). Artificial neural network modelling to predict cuticle cracking in greenhouse peppers and tomatoes. Computers and Electronics in Agriculture, 61, 108–116. https://doi.org/10.1016/j.compag.2007.09.011

    Article  Google Scholar 

  90. Song, X., Zhang, G., Liu, F., et al. (2016). Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model. Journal of Arid Land, 8, 734–748. https://doi.org/10.1007/s40333-016-0049-0

    Article  Google Scholar 

  91. Gao, L., Zhang, M., & Chen, G. (2013). An intelligent irrigation system based on wireless sensor network and fuzzy control. Journal of Networks, 8, 1080–1087. https://doi.org/10.4304/jnw.8.5.1080-1087

    Article  Google Scholar 

  92. Somov, A., Seledets, I., Matveev, S., et al. (2019). Pervasive agriculture: IoT-enabled greenhouse for plant growth control. IEEE Pervasive Computing, 17, 65–75. https://doi.org/10.1109/mprv.2018.2873849

    Article  Google Scholar 

Download references

Acknowledgments

This paper was supported by EFOP3.6.3-VEKOP-16-2017-00007—“Young researchers for talent”—Supporting careers in research activities in higher education program.

Author information

Authors and Affiliations

  1. Faculty of Economics and Business, University of Debrecen, Debrecen, Hungary

    Mihály Tóth, János Felföldi, László Várallyai & Róbert Szilágyi

  2. Károly Ihrig Doctoral School of Management and Business, University of Debrecen, Debrecen, Hungary

    Mihály Tóth

Authors
  1. Mihály Tóth
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. János Felföldi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. László Várallyai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Róbert Szilágyi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mihály Tóth .

Editor information

Editors and Affiliations

  1. Institute for Bio-economy and Agri-technology (iBO), Centre for Research & Technology Hellas (CERTH), Thessaloniki, Greece

    Dionysis D. Bochtis

  2. School of Agriculture, Aristotle Univeristy of Thessaloniki, Thessaloniki, Greece

    Dimitrios E. Moshou

  3. Institute for Bio-economy and Agri-technology (iBO), Centre for Research & Technology Hellas (CERTH), Thessaloniki, Greece

    Giorgos Vasileiadis

  4. Institute for Bio-economy and Agri-technology (iBO), Centre for Research & Technology Hellas (CERTH), Thessaloniki, Greece

    Athanasios Balafoutis

  5. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Tóth, M., Felföldi, J., Várallyai, L., Szilágyi, R. (2022). Application Possibilities of IoT-based Management Systems in Agriculture. In: Bochtis, D.D., Moshou, D.E., Vasileiadis, G., Balafoutis, A., Pardalos, P.M. (eds) Information and Communication Technologies for Agriculture—Theme II: Data. Springer Optimization and Its Applications, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-030-84148-5_4

Download citation

Publish with us

Policies and ethics

Search

Navigation