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20240305鸽子养殖智能投料小车设计

‖  文章供稿:黄千瑞 张日红 陈轩杰 谢文迪 肖鸿旭 苏楚妍
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黄千瑞  张日红  陈轩杰  谢文迪  肖鸿旭  苏楚妍

(仲恺农业工程学院机电工程学院,广东 广州 510225)

摘要:随着现代畜禽业的快速发展,畜禽养殖场对养殖技术机械化、自动化的需求不断增加。鸽子精准投料技术不仅能够有效地提高饲料利用率、降低劳动力成本,还能提高生产效率和经济效益。该文以鸽子的智能精准投料为目标,设计鸽子养殖智能投料小车。首先,介绍鸽子养殖智能投料小车的硬件部分;然后,运用YOLOv7深度学习模型识别3层不同高度鸽笼内鸽子数量;接着,开发料仓内饲料剩余量超声波检测功能、路径跟踪与导航的红外循迹功能;最后,针对鸽子养殖智能投料小车的投料效率、运行精度和最佳投料距离进行实验分析。实验结果表明,在室内正常光照下,鸽子和喂料槽的识别准确率为93.36%,检测和投料的平均时间为2 s,饲料浪费率在1%以下,有效提高了鸽子养殖场的作业效率。

关键词:鸽子养殖;智能投料小车;视觉识别;深度学习;超声波检测;红外循迹

中图分类号:TP183; S817.3        文献标志码:A        文章编号:1674-2605(2024)03-0005-07

DOI:10.3969/j.issn.1674-2605.2024.03.005

Design of Intelligent Feeding Cart for Pigeon Breeding

HUANG Qianrui  ZHANG Rihong  CHEN Xuanjie                                    XIE Wendi  XIAO Hongxu  SU Chuyan

(College of Mechanical and Electrical Engineering, Zhongkai University of                         Agriculture and Engineering, Guangzhou 510225, China)

Abstract: With the rapid development of modern livestock and poultry industry, the demand for mechanization and automation of breeding technology in livestock and poultry farms is constantly increasing. The precise feeding technology of pigeons can not only effectively improve feed utilization and reduce labor costs, but also improve production efficiency and economic benefits. This article aims to design an intelligent feeding cart for pigeon breeding with the goal of intelligent and precise feeding. Firstly, introduce the hardware of the intelligent feeding cart for pigeon breeding; Then, use the YOLOv7 deep learning model to identify the number of pigeons in three different height pigeon cages; Next, develop ultrasonic detection function for remaining feed in the silo, infrared tracking function for path tracking and navigation; Finally, experimental analysis was conducted on the feeding efficiency, operational accuracy, and optimal feeding distance of the intelligent feeding cart for pigeon breeding. The results showed that under normal indoor lighting, the accuracy of identifying pigeons and feeding troughs was 93.36%, the average time for detection and feeding was 2 seconds, and the feed rate was below 1%, effectively improving the operational efficiency of the pigeon breeding farm.

Keywords: pigeon breeding; intelligent feeding cart; visual recognition; deep learning; ultrasonic testing; infrared tracking

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