Prediction and Context Awareness in Agriculture: A Systematic Mapping

Bruno Guilherme MartiniGilson Augusto HelferJorge Luis Victória BarbosaRegina Célia Espinosa ModoloMarcio Rosa da SilvaRodrigo Marques de Figueiredo

DOI 10.7160/aol.2020.120305
No 3/2020, September
pp. 45-58

Martini, B. G., Helfer, G. A., Barbosa, J. L., Modolo, R. C. E., da Silva M. R. and de Figueiredo, R. M. (2020) “Prediction and Context Awareness in Agriculture: A Systematic Mapping", AGRIS on-line Papers in Economics and Informatics, Vol. 12, No. 3, pp. 45-58. ISSN 1804-1930. DOI 10.7160/aol.2020.120305.

Abstract

The advances in sensorial technology and its use in agriculture have been contributing to the acquisition and analysis of data regarding agricultural production. Studies propose the use of sensors to monitor production or even the use of cameras to obtain crop information, providing data, reminders, and alerts to farmers. Through the obtainment and analysis of these data, context awareness can be used to improve systems, mainly through the prediction techniques applied to agriculture. This article presents a systematic mapping of studies that use prediction and context awareness in agriculture. During the mapping, 10206 articles were initially identified and, after filtering by inclusion and exclusion criteria, 42 articles were selected. The results indicated that 35.7% (15/42) of the studies used one or more prediction techniques and 45.2% (19/42) used image processing through pictures of cameras to obtain information regarding planting. 23 sensors with different functionalities were found, those have been used in the collection of data for context formation in agriculture.

Keywords

Technology for agriculture; systematic mapping; prediction in agriculture; context awareness in agriculture.

References

  1. Alipio, M. I., Earl, A., Dela Cruz, M., Doria, J. D. A., and Fruto, R. M. S. (2017) “A Smart Hydroponics Farming System Using Exact Inference in Bayesian Network“, 6th Global Conference on Consumer Electronics (GCCE 2017), pp. 2-6. ISBN 978-1-5090-4045-2. DOI 10.1109/GCCE.2017.8229470.
  2. Arakeri, M. P., Barsaiya, V. K. B. P. and Sairam, H. V. (2017) “Computer Vision Based Robotic Weed Control System for Precision Agriculture“, International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1201-1205. ISBN 978-1-5090-6367-3. DOI 10.1109/ICACCI.2017.8126005.
  3. Athani, S., Tejeshwar, C. H. (2017) “Soil moisture monitoring using IoT enabled arduino sensors with neural networks for improving soil management for farmers and predict seasonal rainfall for planning future harvest in North Karnataka – India“, International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 43-48. ISBN 978-1-5090-3243-3. DOI 10.1109/I-SMAC.2017.8058385.
  4. Bischoff, V., Oliveira, K. S. F., Gonçales, L. J. and Barbosa., J. L. V. (2018) “Integration of Feature Models: A Systematic Mapping Study“, Information and Software Technology, pp. 1-30. ISSN 0950-5849. DOI 10.1016/j.infsof.2018.08.016.
  5. Carrasquilla-Batista, A., Chacón-Rodrígues, A., Solórzano-Quintana, M. (2016) “Using IoT resources to enhance the accuracy of overdrain measurements in greenhouse horticulture“, CONCAPAN, pp. 1-5. ISBN 978-1-4673-9578-6. DOI 10.1109/CONCAPAN.2016.7942345.
  6. de la Concepcion, R. A, Stefanelli, R. and Trinchero, D. (2014) “A Wireless Sensor Network Platform Optimized for Assisted Sustainable Agriculture“, Global Humanitarian Technology Conference, pp. 159-165. ISBN 978-1-4799-7193-0. DOI 10.1109/GHTC.2014.6970276.
  7. Dalmina, L., Barbosa, J. L.V., Vianna, H. D. (2019) “A systematic mapping study of gamification models oriented to motivational characteristics“, Behaviour & Information Technology, Vol. 38, No. 11, p. 1-18. ISSN 1362-3001. DOI 10.1080/0144929X.2019.1576768.
  8. Dey, A. K., Abowd, G. D. and Salber, D. A. (2001) “A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-aware Applications“, Human-Computer Interaction, Vol. 16, No. 2, pp. 97-166. ISSN 1532-7051. DOI 10.1207/S15327051HCI16234_02.
  9. Dias, L. P. S., Barbosa, J. L. V. and Vianna, H. D. (2018) “Gamification and serious games in depression care: A systematic mapping study“, Telematics and Informatics, Vol. 35, No. 1, pp. 213-224. ISSN 0736-5853. DOI 10.1016/j.tele.2017.11.002.
  10. Divya, P., Sonkiya, S., Das, P., Manjusha, V. V. and Ramesh, M. V. (2014) “CAWIS: Context Aware Wireless Irrigation System“, International Conference on Computer, Communication, and Control Technology (I4CT), pp. 310-315. ISBN 978-1-4799-4555-9. DOI 10.1109/I4CT.2014.6914195.
  11. Eko, R., Sisyanto, N. and Kurniawan, N. B. (2017) “Hydroponic Smart Farming Using Cyber Physical Social System with Telegram Messenger“, International Conference on Information Technology Systems and Innovation (ICITSI), pp. 239-245. ISBN 978-1-5386-3100-3. DOI 10.1109/ICITSI.2017.8267950.
  12. Fiehn, H. B., Schiebel, L., Avila, A. F., Miller, B. and Mickelson, A. (2018) “Smart Agriculture System Based on Deep Learning“, 2nd International Conference on Smart Digital Environment (ICSDE), pp.158-165. ISBN 978-1-4503-6507-9.
  13. Fukatsu, T. (2014) “Web-based sensor network system ‘Field Servers’ for practical agricultural applications“, International Workshop on Web Intelligence and Smart Sensing (IWWISS), pp. 01-08. ISBN 978-1-4503-2747-3. DOI 10.1145/2637064.2637090.
  14. Goap, A., Sharmab, D., Shuklab, A. K. and Krishna, C. R. (2018) “An IoT based smart irrigation management system using Machine learning and open source technologies“, Computers and Electronics in Agriculture, Vol. 155, pp. 41-49. ISSN 0168-1699. DOI 10.1016/j.compag.2018.09.040.
  15. Helfer, G. A., Barbosa, J. L. V., Martini, B. G., dos Santos, R. B. and da Costa A. B.(2019) “Ubiquitous Computing in Precision Agriculture: A Systematic Review", AGRIS on-line Papers in Economics and Informatics, Vol. 11, No. 4, pp. 3-13. ISSN 1804-1930. DOI 10.7160/aol.2019.110401.
  16. Huong, T. T., Thanh, N. H., Thi, N. and Marshall, A. (2018) “Water and Energy-Efficient Irrigation based on Markov Decision Model for Precision Agriculture“, IEEE Seventh International Conference on Communications and Electronics (ICCE), pp. 51-56. ISBN 978-1-5386-3679-4. DOI 10.1109/CCE.2018.8465723.
  17. Ivanov I. and Tsvetkov, V. (2017) “Intelligent Planting“, International Conference on Computer Systems and Technologies (CompSysTech’17), pp. 265-271. ISBN 978-1-4503-5234-5. DOI 10.1145/3134302.3134328.
  18. Jacob, N. K. (2017) “IoT Powered Portable Aquaponics System“, Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing (ICC), pp. 0-5. ISBN 978-1-4503-4774-7. DOI 10.1145/3018896.3018965.
  19. Joshi, J., Polepally, S., Kumar, P., Samineni, R., Rahul, S. R., Sumedh, K., Tej, D. G. K. and Rajapriya, V. (2017) “Machine Learning Based Cloud Integrated Farming“, International Conference on Machine Learning and Soft Computing, pp. 1-6. ISBN 978-1-4503-4828-7. DOI 10.1145/3036290.3036297.
  20. Kokkonis, G., Kontogiannis, S. and Tomtsis, D. (2017) “FITRA - A Neuro-fuzzy computational algorithm approach based on an embedded water planting system“, International Conference on Internet of things, Data and Cloud Computing (ICC), pp. 0-7. ISBN 978-1-4503-4774-7. DOI 10.1145/3018896.3018934.
  21. Kubicek, P., Kozel, J., Stampach, R. and Lukas, V. (2013) “Prototyping the visualization of geographic and sensor data for agriculture“, Computers and Electronics in Agriculture, Vol. 97, pp. 83-91. ISSN 0168-1699. DOI 10.1016/j.compag.2013.07.007.
  22. Kumar, S., Gogul, I., Raj., D., Pragadesh, S. K. and Sarathkumar, S. (2016) “Smart Autonomous Gardening Rover with Plant Recognition using Neural Networks“, International Conference On Advances In Computing & Communications (ICACC), Vol. 93, pp. 975-981. ISSN 1877-0509. DOI 10.1016/j.procs.2016.07.289.
  23. López-Riquelme, J. A., Pavón-Pulido, N., Navarro-Hellín, H., Soto-Valles, F. and Torres-Sáncheza, R. (2017) “A software architecture based on FIWARE cloud for Precision Agriculture“, Agriculture Water Management, Vol. 183, pp. 123-135. ISSN 0378-3774. DOI 10.1016/j.agwat.2016.10.020.
  24. Luimula, M., Shelby, Z., Tervonen, J., Markkula, J., Weckström, P. and Verronen, P. (2009) “Developing Geosensor Network Support for Locawe Platform – Application of Standards in Low-Rate Communication Context“, International Conference on Pervasive Services, pp. 73-82. ISBN 978-1-60558-644-1. DOI 10.1145/1568199.1568211.
  25. Ma, J., Li, X., Wen, H., Fu, Z. and Zhang, L. (2015) “A key frame extraction method for processing greenhouse vegetables production monitoring video“, Computers and Electronics in Agriculture, Vol. 111, pp. 92-102. ISSN 0168-1699. DOI 10.1016/j.compag.2014.12.007.
  26. Manzatto, C. V., Bhering, S. B. and Simões, M. (1999) “Agricultura de precisão: propostas e ações da Embrapa solos. EMBRAPA Solos“, EMBRAPA Solos., Jun. 1999. [Online] Available: http://www.cnps.embrapa.br/search/pesqs/proj01/proj01.html [Acessed: 10 Sep. 2019].
  27. Mehra, M., Saxena, S., Sankaranarayanan, S., Tom, R. J.and Veeramanikandan, M. (2018) “IoT based hydroponics system using Deep Neural Networks“, Computers and Electronics in Agriculture, Vol. 155, pp. 473-486. ISSN 0168-1699. DOI 10.1016/j.compag.2018.10.015.
  28. Nagini, S., Rajini Kanth, T. V. and Kiranmayee, B. V. (2016) “Agriculture yield prediction using predictive analytic techniques“, International Conference on Contemporary Computing and Informatics (IC3I). ISBN 978-1-5090-5256-1. DOI 10.1109/IC3I.2016.7918789.
  29. Park, J., Choi, J., Lee, Y. and Min, O. (2017) “A Layered Features Analysis in Smart Farm Environments“, Proceedings of the International Conference on Big Data and Internet of Thing (BDIOT), pp. 169-173. ISBN 978-1-4503-5430-1. DOI 10.1145/3175684.3175720.
  30. Patil S. S. and Thorat, S. A. (2016) “Early Detection of Grapes Diseases Using Machine Learning and IoT“, Second International Conference on Cognitive Computing and Information Processing (CCIP). ISBN 978-1-5090-1025-7. DOI 10.1109/CCIP.2016.7802887.
  31. Petersen, K., Vakkalanka, S. and Kuzniarz, L. (2015) “Guidelines for conducting systematic mapping studies in software engineering: An update“, Information and Software Technology, Vol. 64, pp. 1-18. ISSN 0950-5849. DOI 10.1016/j.infsof.2015.03.007.
  32. Pimentel, D., Berger, B., Filiberto, D., Newton, M., Wolfe, B., Karabinakis, E., Clark, S., Poon, E., Abbett, E. and Nandagopal, S. (2004) “Water resources: agricultural and environmental issues“, BioScience, pp. 909-918. ISSN 0006-3568. DOI 10.1641/0006-3568(2004)054[0909:WRAAEI]2.0.CO;2.
  33. Plazas, J. A. P., Gaona-García, P. A. and Marin, C. E. M. (2018) “Proposal of a computational intelligence prediction model based on Internet of Things technologies“, IEEE International Conference on Smart Internet of Things, pp. 186-191. ISBN 978-1-5386-8543-3. DOI 10.1109/SmartIoT.2018.00041.
  34. Popovic, T., Latinovic´, N., Pešic´, A., Zecevic´, Z., Krstajic´, B. and Djukanovic´, S. (2017) “Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: A case study“, Computers and Electronics in Agriculture, Vol. 140, pp. 255-265. ISSN 0168-1699. DOI 10.1016/j.compag.2017.06.008.
  35. Rajendrakumar, S., Rajashekarappa, Parvati, V. K., Parameshachari, B. D., Sunjiv Soyjaudah, K. M. and Banu, R. (2017) “An Intelligent Report Generator for Efficient Farming“, International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT). ISBN 978-1-5386-2361-9. DOI 10.1109/ICEECCOT.2017.8284640.
  36. Rodríguez, S., Gualotuña, T. and Grilo, C. (2017) “A System for the Monitoring and Predicting of Data in Precision Agriculture in a Rose Greenhouse Based on Wireless Sensor Networks“, Procedia Computer Science. Vol. 121, pp. 306-313. ISSN 1877-0509. DOI 10.1016/j.procs.2017.11.042.
  37. Rupanagudi, S. R., Ranjani, B. S., Prathik Nagarj, Bhat, V. G. and Thippeswamy, G. (2015) “A Novel Cloud Computing based Smart Farming System for Early Detection of Borer Insects in Tomatoes“, International Conference on Communication, Information & Computing Technology (ICCICT). ISBN 978-1-4799-5522-0. DOI 10.1109/ICCICT.2015.7045722.
  38. Saha, S., Paul, S., Halder, S. and Majumder, K. (2017) “Smart Agricultural System : Better Accuracy and Productivity“, Devices for Integrated Circuit (DevIC), pp. 23-24. ISBN 978-1-5090-4724-6. DOI 10.1109/DEVIC.2017.8073960.
  39. Santos, U. J. L., Pessin, G., da Costa, C. A., da Rosa Righi, R. (2018) “AgriPrediction: A proactive internet of things model to anticipate problems and improve production in agricultural crops“, Computers and Electronics in Agriculture, Vol. 161, pp. 202-213. ISSN 0168-1699. DOI 10.1016/j.compag.2018.10.010.
  40. Sarangdhar, A. A. and Pawar, P. V. R.(2017) “Machine Learning Regression Technique for Cotton Leaf Disease Detection and Controlling using IoT“, International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 449-454. ISBN 978-1-5090-5686-6. DOI 10.1109/ICECA.2017.8212855.
  41. Shahriar S. and Mcculluch, J. (2014) “A Dynamic Data-driven Decision Support for Aquaculture Farm Closure“, Procedia Computer Science, Vol. 29, pp. 1236-1245. ISSN 1877-0509. DOI 10.1016/j.procs.2014.05.111.
  42. Tan, L. (2016) “Cloud-based Decision Support and Automation for Precision Agriculture in Orchards“, IFAC-PapersOnLine, Vol. 49, No. 16, pp. 330-335. ISSN 2405-8963. DOI 10.1016/j.ifacol.2016.10.061.
  43. Tan, W., Zhao, C. and Wu, H. (2016) “Intelligent alerting for fruit-melon lesion image based on momentum deep learning“, Multimed. Tools and Application, pp. 16741-16761. ISSN 1380-7501. DOI 10.1007/s11042-015-2940-7.
  44. Treboux, J. and Genoud, D. (2018) “Improved Machine Learning Methodology for High Precision Agriculture“, Global Internet of Things Summit (GIoTS), pp. 1-6. ISBN 978-1-5386-6451-3. DOI 10.1109/GIOTS.2018.8534558.
  45. Winiczenko, R., Górnicki, K., Kaleta, K. and Janaszek-Mańkowska, M. (2016) “Optimisation of ANN topology for predicting the rehydrated apple cubes colour change using RSM and GA“, Neural Computing & Applications, Vol. 30, pp. 1795-1809. ISSN 0941-0643. DOI 10.1007/s00521-016-2801-y.
  46. Xu, D., Li, D., Fei, B., Wang, Y. and Peng, F. (2014) “A GPRS-Based Low Energy Consumption Remote Terminal Unit for Aquaculture Water Quality“, International Federation for Information Processing (IFIP). pp. 492-503. DOI 10.1007/978-3-642-54341-8_52.
  47. Yahata, S., Onishi, T., Yamaguchi, K., Ozawa, S., Kitazono, J., Ohkawa, T., Yoshida, T. and Muraka, N. (2017) “A Hybrid Machine Learning Approach to Automatic Plant Phenotyping for Smart Agriculture“, International Joint Conference on Neural Networks (IJCNN), pp. 1787-1793. ISSN 2161-4407. DOI 10.1109/IJCNN.2017.7966067.
  48. Zewge, A. and Dittrich, Y. (2017) “Systematic Mapping Study of Information Technology for Development in Agriculture (The Case of Developing Countries)“, Electronic Journal of Information Systems in Developing Countries (EJISDC), pp. 1-25. ISSN 1681-4835. DOI 10.1002/j.1681-4835.2017.tb00602.x.
  49. Zhang, P., Zhang, Q., Liu, F., Li, J. Cao, N. and Song, C. (2017) “The Construction of the Integration of Water and Fertilizer Smart Water Saving Irrigation System Based on Big Data“, International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), pp. 392-397. ISBN 978-1-5386-3221-5. DOI 10.1109/CSE-EUC.2017.258.
  50. Zhou, B. and Li, L. (2017) “Security monitoring for intelligent water-saving precision irrigation system using cloud services in multimedia context“, Multimedia Tools Applications, pp 1-15. ISSN 1380-7501.
  51. Zhou, L., Song, L., Xie, C. and Zhang, J. (2012) "Applications of Internet of Things in the Facility Agriculture", In Li D., Chen Y. (eds) "Computer and Computing Technologies in Agriculture VI", CCTA 2012. IFIP Advances in Information and Communication Technology, Vol 392. ISSN 1868-4238. DOI 10.1007/978-3-642-36124-1_36.
  52. Zhou, H., Liu, B. and Dong, P. (2011) “The Technology System Framework of the Internet of Things and Its Application Research in Agriculture, In Li D., Chen Y. (eds) Computer and Computing Technologies in Agriculture V. CCTA 2011, IFIP Advances in Information and Communication Technology, Vol 368. E-ISBN 978-3-642-27281-3, ISBN 978-3-642-27280-6. DOI 10.1007/978-3-642-27281-3_35.

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