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DATA TYPE AND DATA SOURCES FOR AGRICULTURAL BIG DATA AND MACHINE LEARNING

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dc.creatorMÓNICA ALEJANDRA CANIUPÁN MARILEO
dc.date2022
dc.date.accessioned2025-01-10T15:34:43Z
dc.date.available2025-01-10T15:34:43Z
dc.date.issued2022
dc.description.abstractSUSTAINABLE AGRICULTURE IS CURRENTLY BEING CHALLENGED UNDER CLIMATE CHANGE SCENARIOS SINCE EXTREME ENVIRONMENTAL PROCESSES DISRUPT AND DIMINISH GLOBAL FOOD PRODUCTION. FOR EXAMPLE, DROUGHT-INDUCED INCREASES IN PLANT DISEASES AND RAINFALL CAUSED A DECREASE IN FOOD PRODUCTION. MACHINE LEARNING AND AGRICULTURAL BIG DATA ARE HIGH-PERFORMANCE COMPUTING TECHNOLOGIES THAT ALLOW ANALYZING A LARGE AMOUNT OF DATA TO UNDERSTAND AGRICULTURAL PRODUCTION. MACHINE LEARNING AND AGRICULTURAL BIG DATA ARE HIGH-PERFORMANCE COMPUTING TECHNOLOGIES THAT ALLOW THE PROCESSING AND ANALYSIS OF LARGE AMOUNTS OF HETEROGENEOUS DATA FOR WHICH INTELLIGENT IT AND HIGH-RESOLUTION REMOTE SENSING TECHNIQUES ARE REQUIRED. HOWEVER, THE SELECTION OF ML ALGORITHMS DEPENDS ON THE TYPES OF DATA TO BE USED. THEREFORE, AGRICULTURAL SCIENTISTS NEED TO UNDERSTAND THE DATA AND THE SOURCES FROM WHICH THEY ARE DERIVED. THESE DATA CAN BE STRUCTURED, SUCH AS TEMPERATURE AND HUMIDITY DATA, WHICH ARE USUALLY NUMERICAL (E.G., FLOAT); SEMI-STRUCTURED, SUCH AS THOSE FROM SPREADSHEETS AND INFORMATION REPOSITORIES, SINCE THESE DATA TYPES ARE NOT PREVIOUSLY DEFINED AND ARE STORED IN NO-SQL DATABASES; AND UNSTRUCTURED, SUCH AS THOSE FROM FILES SUCH AS PDF, TIFF, AND SATELLITE IMAGES, SINCE THEY HAVE NOT BEEN PROCESSED AND THEREFORE ARE NOT STORED IN ANY DATABASE BUT IN REPOSITORIES (E.G., HADOOP). THIS STUDY PROVIDES INSIGHT INTO THE DATA TYPES USED IN AGRICULTURAL BIG DATA ALONG WITH THEIR MAIN CHALLENGES AND TRENDS. IT ANALYZES 43 PAPERS SELECTED THROUGH THE PROTOCOL PROPOSED BY KITCHENHAM AND CHARTERS AND VALIDATED WITH THE PRISMA CRITERIA. IT WAS FOUND THAT THE PRIMARY DATA SOURCES ARE DATABASES, SENSORS, CAMERAS, GPS, AND REMOTE SENSING, WHICH CAPTURE DATA STORED IN PLATFORMS SUCH AS HADOOP, CLOUD COMPUTING, AND GOOGLE EARTH ENGINE. IN THE FUTURE, DATA LAKES WILL ALLOW FOR DATA INTEGRATION ACROSS DIFFERENT PLATFORMS, AS THEY PROVIDE REPRESENTATION MODELS OF OTHER DATA TYPES AND THE RELATIONSHIPS BETWEEN THEM, IMPROVING
dc.formatapplication/pdf
dc.identifier.doi10.3390/su142316131
dc.identifier.issn2071-1050
dc.identifier.issn2071-1050
dc.identifier.urihttps://repositorio.ubiobio.cl/handle/123456789/12675
dc.languagespa
dc.publisherSustainability
dc.relation.uri10.3390/su142316131
dc.rightsPUBLICADA
dc.subjectTIPO DE DATOS
dc.subjectORIGEN DE DATOS
dc.subjectMACRODATOS
dc.subjectAPRENDIZAJE AUTOMATICO
dc.subjectAGRICULTURA
dc.subjectMACHINE LEARNING
dc.subjectDATA TYPE
dc.subjectDATA SOURCE
dc.subjectBIG DATA
dc.subjectAGRICULTURE
dc.titleDATA TYPE AND DATA SOURCES FOR AGRICULTURAL BIG DATA AND MACHINE LEARNING
dc.title.alternativeTIPO DE DATOS Y FUENTES DE DATOS PARA BIG DATA AGRÍCOLA Y APRENDIZAJE AUTOMÁTICO
dc.typeARTÍCULO DE REVISIÓN
dspace.entity.typePublication
ubb.EstadoPUBLICADA
ubb.Otra ReparticionDEPARTAMENTO DE SISTEMAS DE INFORMACION
ubb.SedeCONCEPCIÓN
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