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In poultry farming, the systematic choice, update, and implementation ofperiodic (t) action plans define the feed conversion rate (FCR[t]), which is anacceptable measure for successful production. Appropriate action plans providetailored resources for broilers, allowing them to grow within the so-calledthermal comfort zone, without wast or lack of resources. Although theimplementation of an action plan is automatic, its configuration depends on theknowledge of the specialist, tending to be inefficient and error-prone, besidesto result in different FCR[t] for each poultry house. In this article, we claimthat the specialist's perception can be reproduced, to some extent, bycomputational intelligence. By combining deep learning and genetic algorithmtechniques, we show how action plans can adapt their performance over the time,based on previous well succeeded plans. We also implement a distributed networkinfrastructure that allows to replicate our method over distributed poultryhouses, for their smart, interconnected, and adaptive control. A supervisionsystem is provided as interface to users. Experiments conducted over real datashow that our method improves 5% on the performance of the most productivespecialist, staying very close to the optimal FCR[t].

Estimating action plans for smart poultry houses