Estimating and tuning adaptive action plans for the control of smart interconnected poultry houses

• Ano de defesa: 2020
• Autor(a) principal: Klotz, Darlan Felipe
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Pato Branco; Brasil; Programa de Pós-Graduação em Engenharia Elétrica; UTFPR
• Palavras-chave em Português: Redes neurais (Computação); Controle automático; Sistemas de controle inteligente; Supervisão; Neural networks (Computer science); Automatic control; Intelligent control systems; Supervision; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA; Engenharia/Tecnologia/Gestão
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/5150

In poultry farming, the systematic choice, update, and implementation of periodic (t) action plans define the feed conversion rate (〖FCR〗^(<t>)), which is an acceptable measure for successful production. Appropriate action plans provide tailored resources for broilers, allowing them to grow within the so-called thermal comfort zone, without wast or lack of resources. Although the implementation of an action plan is automatic, its configuration depends on the knowledge of the specialist, tending to be inefficient and error-prone, besides to result in diferente 〖FCR〗^(<t>) for each poultry house. In this article, we claim that the specialist’s perception can be reproduced, to some extent, by computational intelligence. By combining deep learning and genetic algorithm techniques, we show how action plans can adapt their performance over the time, based on previous well succeeded plans. We also implement a distributed network infrastructure that allows to replicate our method over distributed poultry houses, for their smart, interconnected, and adaptive control. A supervision system is provided as interface to users. Experiments conducted over real data show that our method improves 5% on the performance of the most productive specialist, staying very close to the optimal 〖FCR〗^(<t>).