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90. Wang, A., Chen, G., Wu, X., Liu, L., An, N., Chang, C.Y.: Towards human activity recognition:
a hierarchical feature selection framework. Sensors 18(11), 3629 (2018)
91. Lu˘ strek, M., Kalu˘ za, B.: Fall detection and activity recognition with machine learning. Informatica 33(2) (2009)
92. Ustev, Y. E., Durmaz Incel, O., Ersoy, C.: User, device and orientation independent human
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70. Alvanou, G., Lytra, I., Petersen, N.: An MTConnect Ontology for Semantic Industrial Machine
Sensor Analytics
71. Kootbally, Z., Kramer, T.R., Schleno , C., Gupta, S.K.: Overview of an ontology- based
approach for kit building applications. In: 2017 IEEE 11th International Conference Semantic
Computing (ICSC), pp. 520–525. IEEE (2017)
72. Bonacin, R., Nabuco, O.F., Junior, I.P.: Ontology models of the impacts of agriculture and
climatechanges on water resources: scenarios on interoperability and information recovery.
Future Gener. Comput. Syst. 54, 423–434 (2016)
73. Shrestha, R., Davenport, G.F., Bruskiewich, R., Arnaud, E.: Development of crop ontology
for sharing crop phenotypic information. In: Drought Phenotyping in Crops: From Theory to
Practice, pp. 167–176 (2011)
74. Shrestha, R., Senger, M., Ramil, M., Davenport, G., Arnaud, E.: Development of gcp ontology
for sharing crop information. Nat. Prec. (2010)
75. Jonquet, C.: Agroportal: an ontology repository for agronomy. In: European Conference
Dedicated to the Future Use of ICT in the Agri-Food Sector, Bioresource and Biomass Sector,
EFITA’17, Demonstration Session (2017)
76. International Food Policy Research Institute: Linked Open Data—Agricultural Technology
Ontology (2017). http://data.ifpri.org/lod/at. Cited by 3 2020
77. Wang, Y., Wang, Y., Wang, J., Yuan, Y., Zhang, Z.: An ontology-based approach to integration
of hilly citrus production knowledge. Comput. Electron. Agric. 113, 24–43 (2015)
78. Joo, S., Koide, S., Takeda, H., Horyu, D., Takezaki, A., Yoshida, T.: Agriculture activity
ontology: an ontology for core vocabulary of agriculture activity. In International Semantic
Web Conference (Posters & Demos), vol. 33 (2016)
79. Hu, S., Wang, H., She, C., Wang, J.: AgOnt: ontology for agriculture internet of things.
In: International Conference on Computer and Computing Technologies in Agriculture, pp.
131–137. Springer, Berlin, Heidelberg (2010)
80. Aubert C., Buttigieg P.L., Laporte M.A., Devare M., Arnaud E.: CGIAR Agronomy Ontology
(2017). http://purl.obolibrary.org/obo/agro.owl. Cited by 3 2020
81. CTA: Agrovoc Multilingual Agricultural Thesaurus. http://aims.fao.org/standards/agrovoc/
concept-scheme. Cited by 3 2020
82. Agriculture Semantics. https://agrisemantics.org/. Cited by 3 2020
83. Cab thesaurus. https://www.cabi.org/cabthesaurus/. Cited by 3 2020
84. Agriculture Class. https://agclass.nal.usda.gov/. Cited by 4 2020
85. FAO: Agricultural Metadata Element set (agmes) (2018). http://aims.fao.org/standards/
agmes. Cited by 3 2020
86. Martini, D., Schmitz, M., Mietzsch, E.: agrordf as a semantic overlay to agroxml: a general
model for enhancing interoperability in agrifood data standards. In: CIGR conference on
Sustainable Agriculture Through ICT Innovation (2013)
87. Drury, B., Fernandes, R., Moura, M.F.: A survey of semantic web technology for agriculture.
In: Information Processing in Agriculture (2019)
88. Atzori, L., Iera, A., Morabito, G., Nitti, M.: The social internet of things (siot)-when social
networks meet the internet of things: concept, architecture and network characterization.
Comput. Netw. 56(16), 3594–3608 (2012)
89. Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE
Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(6), 790–808 (2012)
90. Wang, A., Chen, G., Wu, X., Liu, L., An, N., Chang, C.Y.: Towards human activity recognition:
a hierarchical feature selection framework. Sensors 18(11), 3629 (2018)
91. Lu˘ strek, M., Kalu˘ za, B.: Fall detection and activity recognition with machine learning. Informatica 33(2) (2009)
92. Ustev, Y. E., Durmaz Incel, O., Ersoy, C.: User, device and orientation independent human
activity recognition on mobile phones: Challenges and a proposal. In; Proceedings of the
2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, pp.
1427–1436. ACM (2013)