评估智能禽舍的行动计划
Estimating action plans for smart poultry houses
摘要：
在家禽养殖中，定期（t）行动计划的系统选择、更新和实施定义了饲料转化率（FCR [t]），这是成功生产的可接受指标。适当的行动计划为肉鸡提供量身定制的资源，使它们能够在所谓的热舒适区内生长，而不会浪费或缺乏资源。虽然行动计划的实施是自动的，但其配置取决于专家的知识，往往效率低下且容易出错，此外还会导致每个禽舍的FCR [t]不同。在这篇文章中，我们声称，专家的看法可以复制，在某种程度上，由计算智能。通过结合深度学习和遗传算法技术，我们展示了行动计划如何在以前成功的计划的基础上随着时间的推移而调整其性能。我们还实现了一个分布式网络基础设施，允许在分布式禽舍上复制我们的方法，以实现智能，互联和自适应控制。提供了一个监控系统作为用户界面。在真实数据上进行的实验表明，我们的方法比最有生产力的专家的性能提高了5%，非常接近最佳FCR [t]。
英文摘要：
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 different 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].
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
