A survey on smart farming data, applications and techniques

The Internet of Things (IoT) and the relevant technologies have had a significant impact on smart farming as a major sub-domain within the field of agriculture. Modern technology supports data collection from IoT devices through several farming processes. The extensive amount of collected smart farm...
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Gespeichert in:

Autor*in:	Alwis, Sandya De [verfasserIn] Hou, Ziwei [verfasserIn] Zhang, Yishuo [verfasserIn] Na, Myung Hwan [verfasserIn] Ofoghi, Bahadorreza [verfasserIn] Sajjanhar, Atul [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Informationstechnik / Industrie 4.0 / Automatisierte Produktion / Computerunterstützung
Schlagwörter:	Smart farming Data analysis Big data Machine learning Digital farming Predictive farming Farming industry

Übergeordnetes Werk:	Enthalten in: Computers in industry - Amsterdam [u.a.] : Elsevier Science, 1979, 138
Übergeordnetes Werk:	volume:138

DOI / URN:	10.1016/j.compind.2022.103624

Katalog-ID:	ELV007685394

Internformat


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520			\|a The Internet of Things (IoT) and the relevant technologies have had a significant impact on smart farming as a major sub-domain within the field of agriculture. Modern technology supports data collection from IoT devices through several farming processes. The extensive amount of collected smart farming data can be utilized for daily decision making and analysis such as yield prediction, growth analysis, quality maintenance, animal and aquaculture, as well as farm management. This survey focuses on three major aspects of contemporary smart farming. First, it highlights various types of big data generated through smart farming and makes a broad categorization of such data. Second, this paper discusses a comprehensive set of typical applications of big data in smart farming. Third, it identifies and introduces the principal big data and machine learning techniques that are utilized in smart farming data analysis. In doing so, this survey also identifies some of the major, current challenges in smart farming big data analysis.This paper provides a discussion on potential pathways toward more effective smart farming through relevant analytics-guided decision making.
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700	1		\|a Sajjanhar, Atul \|e verfasserin \|4 aut
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allfields	10.1016/j.compind.2022.103624 doi (DE-627)ELV007685394 (ELSEVIER)S0166-3615(22)00019-7 DE-627 ger DE-627 rda eng 54.80 bkl 50.03 bkl Alwis, Sandya De verfasserin aut A survey on smart farming data, applications and techniques 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Internet of Things (IoT) and the relevant technologies have had a significant impact on smart farming as a major sub-domain within the field of agriculture. Modern technology supports data collection from IoT devices through several farming processes. The extensive amount of collected smart farming data can be utilized for daily decision making and analysis such as yield prediction, growth analysis, quality maintenance, animal and aquaculture, as well as farm management. This survey focuses on three major aspects of contemporary smart farming. First, it highlights various types of big data generated through smart farming and makes a broad categorization of such data. Second, this paper discusses a comprehensive set of typical applications of big data in smart farming. Third, it identifies and introduces the principal big data and machine learning techniques that are utilized in smart farming data analysis. In doing so, this survey also identifies some of the major, current challenges in smart farming big data analysis.This paper provides a discussion on potential pathways toward more effective smart farming through relevant analytics-guided decision making. 1.1\x Informationstechnik (DE-2867)16757-5 stw 1.2\x Industrie 4.0 (DE-2867)30177-6 stw 1.3\x Automatisierte Produktion (DE-2867)12730-3 stw 1.4\x Computerunterstützung (DE-2867)19661-3 stw Smart farming Data analysis Big data Machine learning Digital farming Predictive farming Farming industry Hou, Ziwei verfasserin aut Zhang, Yishuo verfasserin aut Na, Myung Hwan verfasserin aut Ofoghi, Bahadorreza verfasserin aut Sajjanhar, Atul verfasserin aut Enthalten in Computers in industry Amsterdam [u.a.] : Elsevier Science, 1979 138 Online-Ressource (DE-627)320415767 (DE-600)2001900-2 (DE-576)094056633 0166-3615 nnns volume:138 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.80 Angewandte Informatik 50.03 Methoden und Techniken der Ingenieurwissenschaften BIZ-09001 SKW AR 138
spelling	10.1016/j.compind.2022.103624 doi (DE-627)ELV007685394 (ELSEVIER)S0166-3615(22)00019-7 DE-627 ger DE-627 rda eng 54.80 bkl 50.03 bkl Alwis, Sandya De verfasserin aut A survey on smart farming data, applications and techniques 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Internet of Things (IoT) and the relevant technologies have had a significant impact on smart farming as a major sub-domain within the field of agriculture. Modern technology supports data collection from IoT devices through several farming processes. The extensive amount of collected smart farming data can be utilized for daily decision making and analysis such as yield prediction, growth analysis, quality maintenance, animal and aquaculture, as well as farm management. This survey focuses on three major aspects of contemporary smart farming. First, it highlights various types of big data generated through smart farming and makes a broad categorization of such data. Second, this paper discusses a comprehensive set of typical applications of big data in smart farming. Third, it identifies and introduces the principal big data and machine learning techniques that are utilized in smart farming data analysis. In doing so, this survey also identifies some of the major, current challenges in smart farming big data analysis.This paper provides a discussion on potential pathways toward more effective smart farming through relevant analytics-guided decision making. 1.1\x Informationstechnik (DE-2867)16757-5 stw 1.2\x Industrie 4.0 (DE-2867)30177-6 stw 1.3\x Automatisierte Produktion (DE-2867)12730-3 stw 1.4\x Computerunterstützung (DE-2867)19661-3 stw Smart farming Data analysis Big data Machine learning Digital farming Predictive farming Farming industry Hou, Ziwei verfasserin aut Zhang, Yishuo verfasserin aut Na, Myung Hwan verfasserin aut Ofoghi, Bahadorreza verfasserin aut Sajjanhar, Atul verfasserin aut Enthalten in Computers in industry Amsterdam [u.a.] : Elsevier Science, 1979 138 Online-Ressource (DE-627)320415767 (DE-600)2001900-2 (DE-576)094056633 0166-3615 nnns volume:138 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.80 Angewandte Informatik 50.03 Methoden und Techniken der Ingenieurwissenschaften BIZ-09001 SKW AR 138
allfields_unstemmed	10.1016/j.compind.2022.103624 doi (DE-627)ELV007685394 (ELSEVIER)S0166-3615(22)00019-7 DE-627 ger DE-627 rda eng 54.80 bkl 50.03 bkl Alwis, Sandya De verfasserin aut A survey on smart farming data, applications and techniques 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Internet of Things (IoT) and the relevant technologies have had a significant impact on smart farming as a major sub-domain within the field of agriculture. Modern technology supports data collection from IoT devices through several farming processes. The extensive amount of collected smart farming data can be utilized for daily decision making and analysis such as yield prediction, growth analysis, quality maintenance, animal and aquaculture, as well as farm management. This survey focuses on three major aspects of contemporary smart farming. First, it highlights various types of big data generated through smart farming and makes a broad categorization of such data. Second, this paper discusses a comprehensive set of typical applications of big data in smart farming. Third, it identifies and introduces the principal big data and machine learning techniques that are utilized in smart farming data analysis. In doing so, this survey also identifies some of the major, current challenges in smart farming big data analysis.This paper provides a discussion on potential pathways toward more effective smart farming through relevant analytics-guided decision making. 1.1\x Informationstechnik (DE-2867)16757-5 stw 1.2\x Industrie 4.0 (DE-2867)30177-6 stw 1.3\x Automatisierte Produktion (DE-2867)12730-3 stw 1.4\x Computerunterstützung (DE-2867)19661-3 stw Smart farming Data analysis Big data Machine learning Digital farming Predictive farming Farming industry Hou, Ziwei verfasserin aut Zhang, Yishuo verfasserin aut Na, Myung Hwan verfasserin aut Ofoghi, Bahadorreza verfasserin aut Sajjanhar, Atul verfasserin aut Enthalten in Computers in industry Amsterdam [u.a.] : Elsevier Science, 1979 138 Online-Ressource (DE-627)320415767 (DE-600)2001900-2 (DE-576)094056633 0166-3615 nnns volume:138 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.80 Angewandte Informatik 50.03 Methoden und Techniken der Ingenieurwissenschaften BIZ-09001 SKW AR 138
allfieldsGer	10.1016/j.compind.2022.103624 doi (DE-627)ELV007685394 (ELSEVIER)S0166-3615(22)00019-7 DE-627 ger DE-627 rda eng 54.80 bkl 50.03 bkl Alwis, Sandya De verfasserin aut A survey on smart farming data, applications and techniques 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Internet of Things (IoT) and the relevant technologies have had a significant impact on smart farming as a major sub-domain within the field of agriculture. Modern technology supports data collection from IoT devices through several farming processes. The extensive amount of collected smart farming data can be utilized for daily decision making and analysis such as yield prediction, growth analysis, quality maintenance, animal and aquaculture, as well as farm management. This survey focuses on three major aspects of contemporary smart farming. First, it highlights various types of big data generated through smart farming and makes a broad categorization of such data. Second, this paper discusses a comprehensive set of typical applications of big data in smart farming. Third, it identifies and introduces the principal big data and machine learning techniques that are utilized in smart farming data analysis. In doing so, this survey also identifies some of the major, current challenges in smart farming big data analysis.This paper provides a discussion on potential pathways toward more effective smart farming through relevant analytics-guided decision making. 1.1\x Informationstechnik (DE-2867)16757-5 stw 1.2\x Industrie 4.0 (DE-2867)30177-6 stw 1.3\x Automatisierte Produktion (DE-2867)12730-3 stw 1.4\x Computerunterstützung (DE-2867)19661-3 stw Smart farming Data analysis Big data Machine learning Digital farming Predictive farming Farming industry Hou, Ziwei verfasserin aut Zhang, Yishuo verfasserin aut Na, Myung Hwan verfasserin aut Ofoghi, Bahadorreza verfasserin aut Sajjanhar, Atul verfasserin aut Enthalten in Computers in industry Amsterdam [u.a.] : Elsevier Science, 1979 138 Online-Ressource (DE-627)320415767 (DE-600)2001900-2 (DE-576)094056633 0166-3615 nnns volume:138 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.80 Angewandte Informatik 50.03 Methoden und Techniken der Ingenieurwissenschaften BIZ-09001 SKW AR 138
allfieldsSound	10.1016/j.compind.2022.103624 doi (DE-627)ELV007685394 (ELSEVIER)S0166-3615(22)00019-7 DE-627 ger DE-627 rda eng 54.80 bkl 50.03 bkl Alwis, Sandya De verfasserin aut A survey on smart farming data, applications and techniques 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Internet of Things (IoT) and the relevant technologies have had a significant impact on smart farming as a major sub-domain within the field of agriculture. Modern technology supports data collection from IoT devices through several farming processes. The extensive amount of collected smart farming data can be utilized for daily decision making and analysis such as yield prediction, growth analysis, quality maintenance, animal and aquaculture, as well as farm management. This survey focuses on three major aspects of contemporary smart farming. First, it highlights various types of big data generated through smart farming and makes a broad categorization of such data. Second, this paper discusses a comprehensive set of typical applications of big data in smart farming. Third, it identifies and introduces the principal big data and machine learning techniques that are utilized in smart farming data analysis. In doing so, this survey also identifies some of the major, current challenges in smart farming big data analysis.This paper provides a discussion on potential pathways toward more effective smart farming through relevant analytics-guided decision making. 1.1\x Informationstechnik (DE-2867)16757-5 stw 1.2\x Industrie 4.0 (DE-2867)30177-6 stw 1.3\x Automatisierte Produktion (DE-2867)12730-3 stw 1.4\x Computerunterstützung (DE-2867)19661-3 stw Smart farming Data analysis Big data Machine learning Digital farming Predictive farming Farming industry Hou, Ziwei verfasserin aut Zhang, Yishuo verfasserin aut Na, Myung Hwan verfasserin aut Ofoghi, Bahadorreza verfasserin aut Sajjanhar, Atul verfasserin aut Enthalten in Computers in industry Amsterdam [u.a.] : Elsevier Science, 1979 138 Online-Ressource (DE-627)320415767 (DE-600)2001900-2 (DE-576)094056633 0166-3615 nnns volume:138 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.80 Angewandte Informatik 50.03 Methoden und Techniken der Ingenieurwissenschaften BIZ-09001 SKW AR 138
language	English
source	Enthalten in Computers in industry 138 volume:138
sourceStr	Enthalten in Computers in industry 138 volume:138
format_phy_str_mv	Article
bklname	Angewandte Informatik Methoden und Techniken der Ingenieurwissenschaften
institution	findex.gbv.de
topic_facet	Informationstechnik Industrie 4.0 Automatisierte Produktion Computerunterstützung Smart farming Data analysis Big data Machine learning Digital farming Predictive farming Farming industry
isfreeaccess_bool	false
container_title	Computers in industry
authorswithroles_txt_mv	Alwis, Sandya De @@aut@@ Hou, Ziwei @@aut@@ Zhang, Yishuo @@aut@@ Na, Myung Hwan @@aut@@ Ofoghi, Bahadorreza @@aut@@ Sajjanhar, Atul @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	320415767
id	ELV007685394
language_de	englisch
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author	Alwis, Sandya De
spellingShingle	Alwis, Sandya De bkl 54.80 bkl 50.03 stw Informationstechnik stw Industrie 4.0 stw Automatisierte Produktion stw Computerunterstützung misc Smart farming misc Data analysis misc Big data misc Machine learning misc Digital farming misc Predictive farming misc Farming industry A survey on smart farming data, applications and techniques
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topic_title	54.80 bkl 50.03 bkl A survey on smart farming data, applications and techniques 1.1\x Informationstechnik (DE-2867)16757-5 stw 1.2\x Industrie 4.0 (DE-2867)30177-6 stw 1.3\x Automatisierte Produktion (DE-2867)12730-3 stw 1.4\x Computerunterstützung (DE-2867)19661-3 stw Smart farming Data analysis Big data Machine learning Digital farming Predictive farming Farming industry
topic	bkl 54.80 bkl 50.03 stw Informationstechnik stw Industrie 4.0 stw Automatisierte Produktion stw Computerunterstützung misc Smart farming misc Data analysis misc Big data misc Machine learning misc Digital farming misc Predictive farming misc Farming industry
topic_unstemmed	bkl 54.80 bkl 50.03 stw Informationstechnik stw Industrie 4.0 stw Automatisierte Produktion stw Computerunterstützung misc Smart farming misc Data analysis misc Big data misc Machine learning misc Digital farming misc Predictive farming misc Farming industry
topic_browse	bkl 54.80 bkl 50.03 stw Informationstechnik stw Industrie 4.0 stw Automatisierte Produktion stw Computerunterstützung misc Smart farming misc Data analysis misc Big data misc Machine learning misc Digital farming misc Predictive farming misc Farming industry
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title	A survey on smart farming data, applications and techniques
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title_sort	a survey on smart farming data, applications and techniques
title_auth	A survey on smart farming data, applications and techniques
abstract	The Internet of Things (IoT) and the relevant technologies have had a significant impact on smart farming as a major sub-domain within the field of agriculture. Modern technology supports data collection from IoT devices through several farming processes. The extensive amount of collected smart farming data can be utilized for daily decision making and analysis such as yield prediction, growth analysis, quality maintenance, animal and aquaculture, as well as farm management. This survey focuses on three major aspects of contemporary smart farming. First, it highlights various types of big data generated through smart farming and makes a broad categorization of such data. Second, this paper discusses a comprehensive set of typical applications of big data in smart farming. Third, it identifies and introduces the principal big data and machine learning techniques that are utilized in smart farming data analysis. In doing so, this survey also identifies some of the major, current challenges in smart farming big data analysis.This paper provides a discussion on potential pathways toward more effective smart farming through relevant analytics-guided decision making.
abstractGer	The Internet of Things (IoT) and the relevant technologies have had a significant impact on smart farming as a major sub-domain within the field of agriculture. Modern technology supports data collection from IoT devices through several farming processes. The extensive amount of collected smart farming data can be utilized for daily decision making and analysis such as yield prediction, growth analysis, quality maintenance, animal and aquaculture, as well as farm management. This survey focuses on three major aspects of contemporary smart farming. First, it highlights various types of big data generated through smart farming and makes a broad categorization of such data. Second, this paper discusses a comprehensive set of typical applications of big data in smart farming. Third, it identifies and introduces the principal big data and machine learning techniques that are utilized in smart farming data analysis. In doing so, this survey also identifies some of the major, current challenges in smart farming big data analysis.This paper provides a discussion on potential pathways toward more effective smart farming through relevant analytics-guided decision making.
abstract_unstemmed	The Internet of Things (IoT) and the relevant technologies have had a significant impact on smart farming as a major sub-domain within the field of agriculture. Modern technology supports data collection from IoT devices through several farming processes. The extensive amount of collected smart farming data can be utilized for daily decision making and analysis such as yield prediction, growth analysis, quality maintenance, animal and aquaculture, as well as farm management. This survey focuses on three major aspects of contemporary smart farming. First, it highlights various types of big data generated through smart farming and makes a broad categorization of such data. Second, this paper discusses a comprehensive set of typical applications of big data in smart farming. Third, it identifies and introduces the principal big data and machine learning techniques that are utilized in smart farming data analysis. In doing so, this survey also identifies some of the major, current challenges in smart farming big data analysis.This paper provides a discussion on potential pathways toward more effective smart farming through relevant analytics-guided decision making.
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title_short	A survey on smart farming data, applications and techniques
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author2	Hou, Ziwei Zhang, Yishuo Na, Myung Hwan Ofoghi, Bahadorreza Sajjanhar, Atul
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up_date	2024-07-06T17:06:24.138Z
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