A survey on the process of knowledge representation of the small banana farmers (Musa spp.) in Mangaratiba, RJ
Keywords:Technology, arduino, intelligent systems, agricultural production.
The banana, the world's most widely produced and commercialized fruit, is grown in all tropical regions of the world, being strongly present in local businesses and subsistence crops serving as an important source of nutrients for the poorest populations. In the state of Rio de Janeiro it is commonly found in hillside and difficult access areas, where most other crops would not be able to settle and, because of this, is grown with inadequate management or insufficient, resulting in low productivity in the areas of Rio de Janeiro. The objective of the present work is to carry out a survey of smallholder information from the Vale do Rio Sahy Association in Mangaratiba, RJ, to enable the representation of knowledge in this domain. From the data collected in this research, it was realized that producers have been engaged in this activity for a long time. However, it was found that the knowledge used to production is extremely tacit, without systematization. The variety of banana species (Musa spp.) grown in the production area of the association's small farmers. The knowledge transfer process knowledge to the knowledge base of an expert system is called knowledge acquisition, where it involves extract all the knowledge from the source of the specialists to systematically represent in a coded form the domain information in an appropriate medium. It was observed, even if preliminarily, that this knowledge are not represented in a database for consultation. Thus, there is a need to define human expertise or producers capable of representing in a technological way data that can be conveniently accessed for Problem solving. In view of the evidence presented in the research, the use of representation of human knowledge (small local producers) to feed and train the system according to the domain presented. Thus, enabling the prototype to help understand climate and soil variables and collaborate in decision making.
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