Repositorio Universidad del Cauca

Sistema de control de calidad de datos agroclimatológicos para agricultura de precisión

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dc.contributor.author Vivas Cantero, Fulvio Yesid
dc.date.accessioned 2019-11-05T16:40:08Z
dc.date.available 2019-11-05T16:40:08Z
dc.date.issued 2016
dc.identifier.uri http://repositorio.unicauca.edu.co:8080/xmlui/handle/123456789/1364
dc.description.abstract The supply of agricultural products in Colombia depends on small farmers, with their traditional practices are highly vulnerable to economic, technological, environmental changes; that threatens food security of our regions, and it is in between that the productivity and quality of crops such producers by its major deficiencies and gaps between the major producers which export their products are kind and yields per hectare are higher. Whether for small and large producers, nowadays the technology involved in the cultivation, guide the strategic decision making in many agricultural issues, such as: the prevention of pests and diseases, agricultural planning, personalized technical assistance, choice of crops, adapting agricultural practices to adverse climatic changes. All this has a common denominator, the information or data of the agricultural parcel, such as: soil, climate and agricultural crop, which is why the capture of relevant information from certain environmental and physical modeling parameters to achieve an agricultural situation, it is of most importance and particular interest in key technologies for the field as precision agriculture, which involves stages of data collection, information processing and decision making. After an extensive review of the literature, it appears that the control of data quality is an important precision agriculture can be considered in the data collection process. Consequently the proposal to define a series of mechanisms of quality control data, which are a major component in the architecture for a system of data acquisition SAD, as well as performed at the observing site should take into account the information It generated in the sensors that takes data from environmental and climate phenomena that are very relevant to a system of quality control and knowledge of the surrounding environment. This approach can provide the SAD the ability to understand the situations of their environment in order to improve the quality of data for decision-making. As a result of this proposal has a validated architecture in a real environment to share data with an acceptable level of quality, using quality control mechanisms defined and evaluated in a simulated environment and then be a major component in the engine quality data, plus the inclusion of contextual information at the manufacturer level, site observation and data generated from the sensors. All this combined with reinforcement learning environment performed within the acquisition system to convert the quality control process in a cycle of continuous improvement. As a conclusion a number of quality control mechanisms identified, evaluated and implemented to be accessible by all public defined. In addition to the inclusion of contextual information for open standards modeled according to SWE initiative, in addition to its contribution to the process implementation and quality control in the selection and definition of parameters to be a changing environment can generate a continuous learning impact generate more data in better quality every time, the method used is a reinforcement learning running locally. The most important conclusion is the definition of a reference architecture that includes a quality motor, motor learning and motor of global and local knowledge to improve the quality of the data captured by an acquisition system SAD. en
dc.language.iso spa es
dc.publisher Universidad del Cauca es
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data quality control en
dc.subject Precision agriculture en
dc.subject Metadata en
dc.subject Data acquisition systems en
dc.subject Contextual model en
dc.subject SWE en
dc.subject Reinforcement learning en
dc.title Sistema de control de calidad de datos agroclimatológicos para agricultura de precisión es
dc.type Tesis maestría es
dc.rights.creativecommons https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.type.driver info:eu-repo/semantics/masterThesis
dc.type.coar http://purl.org/coar/resource_type/c_bdcc
dc.publisher.faculty Facultad de Ingeniería Electrónica y Telecomunicaciones es
dc.publisher.program Maestría en Ingeniería Telemática es
dc.rights.accessrights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion
dc.coar.version http://purl.org/coar/version/c_970fb48d4fbd8a85


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