{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T06:50:09Z","timestamp":1780469409893,"version":"3.54.1"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T00:00:00Z","timestamp":1658966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Precision agriculture (PA) is the field that deals with the fine-tuned management of crops to increase crop yield, augment profitability, and conserve the environment. Existing Internet of Things (IoT) solutions for PA are typically divided in terms of their use of either aerial sensing using unmanned aerial vehicles (UAVs) or ground-based sensing approaches. Ground-based sensing provides high data accuracy, but it involves large grids of ground-based sensors with high operational costs and complexity. On the other hand, while the cost of aerial sensing is much lower than ground-based sensing alternatives, the data collected via aerial sensing are less accurate and cover a smaller period than ground-based sensing data. Despite the contrasting virtues and limitations of these two sensing approaches, there are currently no hybrid sensing IoT solutions that combine aerial and ground-based sensing to ensure high data accuracy at a low cost. In this paper, we propose a Hybrid Sensing Platform (HSP) for PA\u2014an IoT platform that combines a small number of ground-based sensors with aerial sensors to improve aerial data accuracy and at the same time reduce ground-based sensing costs.<\/jats:p>","DOI":"10.3390\/fi14080233","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T20:49:28Z","timestamp":1659041368000},"page":"233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Hybrid Sensing Platform for IoT-Based Precision Agriculture"],"prefix":"10.3390","volume":"14","author":[{"given":"Hamid","family":"Bagha","sequence":"first","affiliation":[{"name":"School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0588-5931","authenticated-orcid":false,"given":"Ali","family":"Yavari","sequence":"additional","affiliation":[{"name":"School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7880-2140","authenticated-orcid":false,"given":"Dimitrios","family":"Georgakopoulos","sequence":"additional","affiliation":[{"name":"School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,28]]},"reference":[{"key":"ref_1","first-page":"7","article-title":"Adoption of precision agriculture technologies in developed and developing countries","volume":"8","author":"Say","year":"2018","journal-title":"Online J. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1007\/s00607-016-0510-0","article-title":"Internet of things: From internet scale sensing to smart services","volume":"98","author":"Georgakopoulos","year":"2016","journal-title":"Computing"},{"key":"ref_3","unstructured":"Yavari, A. (2019). Internet of Things Data Contextualisation for Scalable Information Processing, Security, and Privacy. [Ph.D. Thesis, RMIT University]."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Morrone, S., Dimauro, C., Gambella, F., and Cappai, M.G. (2022). Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions. Sensors, 22.","DOI":"10.3390\/s22124319"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.compag.2017.11.002","article-title":"Analysis of fieldwork activities during milk production recording in dairy ewes by means of individual ear tag (ET) alone or plus RFID based electronic identification (EID)","volume":"144","author":"Cappai","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3758","DOI":"10.1109\/JIOT.2018.2844296","article-title":"An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges","volume":"5","author":"Elijah","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Triantafyllou, A., Tsouros, D.C., Sarigiannidis, P., and Bibi, S. (2019, January 29\u201331). An architecture model for smart farming. Proceedings of the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini Island, Greece.","DOI":"10.1109\/DCOSS.2019.00081"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.compag.2019.05.028","article-title":"Mysense: A comprehensive data management environment to improve precision agriculture practices","volume":"162","author":"Morais","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1007\/s11119-018-09624-8","article-title":"Architecture design approach for IoT-based farm management information systems","volume":"20","author":"Tekinerdogan","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100161","DOI":"10.1016\/j.iot.2020.100161","article-title":"A smart farming concept based on smart embedded electronics, internet of things and wireless sensor network","volume":"9","author":"Mahbub","year":"2020","journal-title":"Internet Things"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"395","DOI":"10.3233\/AIS-170440","article-title":"Internet of things for smart agriculture: Technologies, practices and future direction","volume":"9","author":"Ray","year":"2017","journal-title":"J. Ambient. Intell. Smart Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.compag.2018.01.003","article-title":"Economic assessment of a smart traceability system (RFID+ DNA) for origin and brand protection of the pork product labelled \u201csuinetto di Sardegna\u201d","volume":"145","author":"Cappai","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Heble, S., Kumar, A., Prasad, K.V.D., Samirana, S., Rajalakshmi, P., and Desai, U.B. (2018, January 5\u20138). A low power IoT network for smart agriculture. Proceedings of the 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore.","DOI":"10.1109\/WF-IoT.2018.8355152"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jayaraman, P.P., Yavari, A., Georgakopoulos, D., Morshed, A., and Zaslavsky, A. (2016). Internet of things platform for smart farming: Experiences and lessons learnt. Sensors, 16.","DOI":"10.3390\/s16111884"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zervopoulos, A., Tsipis, A., Alvanou, A.G., Bezas, K., Papamichail, A., Vergis, S., Stylidou, A., Tsoumanis, G., Komianos, V., and Koufoudakis, G. (2020). Wireless sensor network synchronization for precision agriculture applications. Agriculture, 10.","DOI":"10.3390\/agriculture10030089"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.compag.2017.06.008","article-title":"Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: A case study","volume":"140","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s00521-018-3737-1","article-title":"Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms","volume":"31","author":"Keswani","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.ifacol.2016.10.070","article-title":"Application of integrated control strategy and bluetooth for irrigating romaine lettuce in greenhouse","volume":"49","author":"Hong","year":"2016","journal-title":"IFAC-PapersOnLine"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1109\/TIM.2008.917198","article-title":"Remote sensing and control of an irrigation system using a distributed wireless sensor network","volume":"57","author":"Kim","year":"2008","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.compag.2016.02.003","article-title":"Automatic moth detection from trap images for pest management","volume":"123","author":"Ding","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","article-title":"Plant disease detection by imaging sensors\u2013parallels and specific demands for precision agriculture and plant phenotyping","volume":"100","author":"Mahlein","year":"2016","journal-title":"Plant Dis."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shafi, U., Mumtaz, R., Garc\u00eda-Nieto, J., Hassan, S.A., Zaidi, S.A.R., and Iqbal, N. (2019). Precision agriculture techniques and practices: From considerations to applications. Sensors, 19.","DOI":"10.3390\/s19173796"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Uddin, M.A., Mansour, A., Le Jeune, D., and Aggoune, E.H.M. (2017, January 22\u201324). Agriculture internet of things: AG-IoT. Proceedings of the 2017 27th International Telecommunication Networks and Applications Conference (ITNAC), Melbourne, Australia.","DOI":"10.1109\/ATNAC.2017.8215399"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"17608","DOI":"10.1109\/JSEN.2021.3049471","article-title":"Unmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges","volume":"21","author":"Maddikunta","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, S., Yuan, F., Ata-UI-Karim, S.T., Zheng, H., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., and Cao, Q. (2019). Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sens., 11.","DOI":"10.3390\/rs11151763"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Popescu, D., Stoican, F., Stamatescu, G., Ichim, L., and Dragana, C. (2020). Advanced UAV\u2013WSN system for intelligent monitoring in precision agriculture. Sensors, 20.","DOI":"10.3390\/s20030817"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cambra, C., Sendra, S., Lloret, J., and Garcia, L. (2017, January 21\u201325). An IoT service-oriented system for agriculture monitoring. Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France.","DOI":"10.1109\/ICC.2017.7996640"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Saha, A.K., Saha, J., Ray, R., Sircar, S., Dutta, S., Chattopadhyay, S.P., and Saha, H.N. (2018, January 8\u201310). IOT-based drone for improvement of crop quality in agricultural field. Proceedings of the 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC.2018.8301662"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tan, L., Hou, H., and Zhang, Q. (2016, January 28\u201330). An extensible software platform for cloud-based decision support and automation in precision agriculture. Proceedings of the 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI), Pittsburgh, PA, USA.","DOI":"10.1109\/IRI.2016.35"},{"key":"ref_30","first-page":"19","article-title":"Data-driven precision agricultural applications using field sensors and Unmanned Aerial Vehicle","volume":"1","author":"Pathak","year":"2018","journal-title":"Int. J. Precis. Agric. Aviat."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Joalland, S., Screpanti, C., Varella, H.V., Reuther, M., Schwind, M., Lang, C., Walter, A., and Liebisch, F. (2018). Aerial and ground based sensing of tolerance to beet cyst nematode in sugar beet. Remote Sens., 10.","DOI":"10.3390\/rs10050787"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Idbella, M., Iadaresta, M., Gagliarde, G., Mennella, A., Mazzoleni, S., and Bonanomi, G. (2020). Agrilogger: A new wireless sensor for monitoring agrometeorological data in areas lacking communication networks. Sensors, 20.","DOI":"10.3390\/s20061589"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"105169","DOI":"10.1016\/j.compag.2019.105169","article-title":"Novel soil environment monitoring system based on RFID sensor and LoRa","volume":"169","author":"Deng","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.geoderma.2018.09.046","article-title":"UAV based soil salinity assessment of cropland","volume":"338","author":"Ivushkin","year":"2019","journal-title":"Geoderma"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.asoc.2015.08.027","article-title":"A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method","volume":"37","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ucuz, D., and Muhammed, A.S. (2020, January 1\u20132). Comparison of the IoT platform vendors, microsoft Azure, Amazon web services, and Google cloud, from users\u2019 perspectives. Proceedings of the 2020 8th International Symposium on Digital Forensics and Security (ISDFS), Beirut, Lebanon.","DOI":"10.1109\/ISDFS49300.2020.9116254"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yavari, A., Jayaraman, P.P., Georgakopoulos, D., and Nepal, S. (2017, January 4\u20137). ConTaaS: An Approach to Internet-Scale Contextualisation for Developing Efficient Internet of Things Applications. Proceedings of the Hawaii International Conference on System Sciences 2017 (HICSS-50), Hilton Waikoloa Village, HI, USA.","DOI":"10.24251\/HICSS.2017.715"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/8\/233\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:58:43Z","timestamp":1760140723000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/8\/233"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,28]]},"references-count":37,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["fi14080233"],"URL":"https:\/\/doi.org\/10.3390\/fi14080233","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,28]]}}}