{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T17:42:25Z","timestamp":1774806145505,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T00:00:00Z","timestamp":1636588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Guangdong Province of China","award":["2019A1515011346"],"award-info":[{"award-number":["2019A1515011346"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the context of smart agriculture, high-value data sensing in the entire crop lifecycle is fundamental for realizing crop cultivation control. However, the existing data sensing methods are deficient regarding the sensing data value, poor data correlation, and high data collection cost. The main problem for data sensing over the entire crop lifecycle is how to sense high-value data according to crop growth stage at a low cost. To solve this problem, a data sensing framework was developed by combining edge computing with the Internet of Things, and a novel data sensing strategy for the entire crop lifecycle is proposed in this paper. The proposed strategy includes four phases. In the first phase, the crop growth stage is divided by Gath-Geva (GG) fuzzy clustering, and the key growth parameters corresponding to the growth stage are extracted. In the second phase, based on the current crop growth information, a prediction method of the current crop growth stage is constructed by using a Tkagi-Sugneo (T-S) fuzzy neural network. In the third phase, based on Deng\u2019s grey relational analysis method, the environmental sensing parameters of the corresponding crop growth stage are optimized. In the fourth phase, an adaptive sensing method of sensing nodes with effective sensing area constraints is established. Finally, based on the actual crop growth history data, the whole crop life cycle dataset is established to test the performance and prediction accuracy of the proposed method for crop growth stage division. Based on the historical data, the simulation data sensing environment is established. Then, the proposed algorithm is tested and compared with the traditional algorithms. The comparison results show that the proposed strategy can divide and predict a crop growth cycle with high accuracy. The proposed strategy can significantly reduce the sensing and data collection times and energy consumption and significantly improve the value of sensing data.<\/jats:p>","DOI":"10.3390\/s21227502","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T23:04:46Z","timestamp":1636671886000},"page":"7502","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Edge Computing Driven Data Sensing Strategy in the Entire Crop Lifecycle for Smart Agriculture"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2627-6040","authenticated-orcid":false,"given":"Rihong","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7587-0543","authenticated-orcid":false,"given":"Xiaomin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e261","DOI":"10.1002\/fes3.261","article-title":"Impacts of land use, population, and climate change on global food security","volume":"10","author":"Molotoks","year":"2021","journal-title":"Food Energy Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s40066-020-00261-x","article-title":"The effect of acute and chronic food shortage on human population equilibrium in a subsistence setting","volume":"9","author":"Tomiyama","year":"2020","journal-title":"Agric. Food Secur."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, X., Ma, Z., Zheng, J., Liu, Y., Zhu, L., and Zhou, N. (2020). An effective edge-assisted data collection approach for critical events in the SDWSN-based agricultural internet of things. Electronics, 9.","DOI":"10.3390\/electronics9060907"},{"key":"ref_4","first-page":"102274","article-title":"A framework for registering UAV-based imagery for crop-tracking in Precision Agriculture","volume":"97","author":"Jurado","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4398061","DOI":"10.1155\/2020\/4398061","article-title":"Edge computing-enabled wireless sensor networks for multiple data collection tasks in smart agriculture","volume":"2020","author":"Li","year":"2020","journal-title":"J. Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3990","DOI":"10.1007\/s10489-020-01744-x","article-title":"Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines","volume":"50","author":"Gupta","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_7","first-page":"5846232","article-title":"A cloud-assisted region monitoring strategy of mobile robot in smart greenhouse","volume":"2019","author":"Li","year":"2019","journal-title":"Mob. Inf. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1016\/j.proeng.2011.08.1131","article-title":"Applications of wireless sensor network in the agriculture environment monitoring","volume":"16","author":"Zhu","year":"2011","journal-title":"Procedia Eng."},{"key":"ref_9","first-page":"7-E","article-title":"Monitoring for precision agriculture using wireless sensor network\u2014A review","volume":"13","author":"Awasthi","year":"2013","journal-title":"Glob. J. Comput. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cao, H., Liu, Y., Yue, X., and Zhu, W. (2017). Cloud-assisted UAV data collection for multiple emerging events in distributed WSNs. Sensors, 17.","DOI":"10.3390\/s17081818"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/TRO.2016.2603528","article-title":"Sensor planning for a symbiotic UAV and UGV system for precision agriculture","volume":"32","author":"Tokekar","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_12","first-page":"428","article-title":"A survey on energy efficient coverage protocols in wireless sensor networks","volume":"29","author":"More","year":"2017","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1109\/JSYST.2018.2873591","article-title":"A strategy for elimination of data redundancy in internet of things (IoT) based wireless sensor network (wsn)","volume":"13","author":"Kumar","year":"2018","journal-title":"IEEE Syst. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1109\/MCOM.2017.1700120","article-title":"Bringing computation closer toward the user network: Is edge computing the solution?","volume":"55","author":"Ahmed","year":"2017","journal-title":"IEEE Commun. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2322","DOI":"10.1109\/COMST.2017.2745201","article-title":"A survey on mobile edge computing: The communication perspective","volume":"19","author":"Mao","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/s13677-017-0097-9","article-title":"Multi-access edge computing: Open issues, challenges and future perspectives","volume":"6","author":"Shahzadi","year":"2017","journal-title":"J. Cloud Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1109\/JPROC.2020.3034808","article-title":"Artificial-intelligence-driven customized manufacturing factory: Key technologies, applications, and challenges","volume":"109","author":"Wan","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Caria, M., Schudrowitz, J., Jukan, A., and Kemper, N. (2017, January 22\u201326). Smart farm computing systems for animal welfare monitoring. Proceedings of the 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO.2017.7973408"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ferr\u00e1ndez-Pastor, F.J., Garc\u00eda-Chamizo, J.M., Nieto-Hidalgo, M., and Mora-Mart\u00ednez, J. (2018). Precision agriculture design method using a distributed computing architecture on internet of things context. Sensors, 18.","DOI":"10.3390\/s18061731"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.future.2019.04.041","article-title":"A smart agriculture IoT system based on deep reinforcement learning","volume":"99","author":"Bu","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1016\/j.procs.2020.07.076","article-title":"Edge computing and artificial intelligence for real-time poultry monitoring","volume":"175","author":"Debauche","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/MCOM.2018.1700882","article-title":"Mobile Edge Computing and Networking for Green and Low-Latency Internet of Things","volume":"56","author":"Zhang","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"012015","DOI":"10.1088\/1755-1315\/191\/1\/012015","article-title":"The application of mobile edge computing in agricultural water monitoring system","volume":"191","author":"Fan","year":"2018","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3405","DOI":"10.1007\/s12083-021-01191-6","article-title":"Cloud-connected flying edge computing for smart agriculture","volume":"14","author":"Uddin","year":"2021","journal-title":"Peer-to-Peer Netw. Appl."},{"key":"ref_25","first-page":"42","article-title":"Edge computing: A tractable model for smart agriculture?","volume":"3","author":"Langton","year":"2019","journal-title":"Artif. Intell. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Akhtar, M.N., Shaikh, A.J., Khan, A., Awais, H., Bakar, E.A., and Othman, A.R. (2021). Smart Sensing with Edge Computing in Precision Agriculture for Soil Assessment and Heavy Metal Monitoring: A Review. Agriculture, 11.","DOI":"10.3390\/agriculture11060475"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/MCE.2021.3049623","article-title":"Smart Sensing for Agriculture: Applications, Advancements, and Challenges","volume":"10","author":"Kumar","year":"2021","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_28","first-page":"4133","article-title":"Smart Agriculture System Towards Iot Based Wireless Sensor Network","volume":"12","author":"Gomathi","year":"2021","journal-title":"Turk. J. Comput. Math. Educ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pallavi, S., Mallapur, J.D., and Bendigeri, K.Y. (2017, January 20\u201322). Remote sensing and controlling of greenhouse agriculture parameters based on IoT. Proceedings of the 2017 International Conference on Big Data, IoT and Data Science (BID), Pune, India.","DOI":"10.1109\/BID.2017.8336571"},{"key":"ref_30","first-page":"664","article-title":"Smart Agriculture System Using IoT for Sensing and Surveillance of Crops","volume":"25","author":"Happila","year":"2021","journal-title":"Ann. Rom. Soc. Cell Biol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cubero, S., Marco-Noales, E., Aleixos, N., Barb\u00e9, S., and Blasco, J. (2020). Robhortic: A field robot to detect pests and diseases in horticultural crops by proximal sensing. Agriculture, 10.","DOI":"10.3390\/agriculture10070276"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Marchese, M., Moheddine, A., and Patrone, F. (2019). IoT and UAV integration in 5G hybrid terrestrial-satellite networks. Sensors, 19.","DOI":"10.3390\/s19173704"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6691571","DOI":"10.1155\/2021\/6691571","article-title":"Intelligent and Smart Irrigation System Using Edge Computing and IoT","volume":"2021","author":"Munir","year":"2021","journal-title":"Complexity"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"115550","DOI":"10.1016\/j.eswa.2021.115550","article-title":"A possibilistic fuzzy Gath-Geva clustering algorithm using the exponential distance","volume":"184","author":"Wu","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, S., Jiang, H., Yin, Y., Xiao, W., and Zhao, B. (2018). The Prediction of the Gas Utilization Ratio based on TS Fuzzy Neural Network and Particle Swarm Optimization. Sensors, 18.","DOI":"10.3390\/s18020625"},{"key":"ref_37","first-page":"30","article-title":"Research on the Model and Application Progress Based on Grey Relational Analysis Theory","volume":"5","author":"Fangfang","year":"2021","journal-title":"Adv. Educ. Technol. Psychol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1109\/JIOT.2016.2517405","article-title":"RMER: Reliable and energy-efficient data collection for large-scale wireless sensor networks","volume":"3","author":"Dong","year":"2016","journal-title":"IEEE Internet Things J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7502\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:28:49Z","timestamp":1760167729000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7502"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,11]]},"references-count":38,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21227502"],"URL":"https:\/\/doi.org\/10.3390\/s21227502","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,11]]}}}