TinyML和物联网支持系统,用于自动鸡蛋质量分析和监测

IF 5.7 Q1 AGRICULTURAL ENGINEERING
Omoy Kombe Hélène , Martin Kuradusenge , Louis Sibomana , Ipyana Issah Mwaisekwa
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引用次数: 0

摘要

家禽业面临着在孵化期间保持最佳鸡蛋质量的挑战,其中环境因素如温度、湿度和照明起着至关重要的作用。传统的鸡蛋质量评估方法往往缺乏准确性,而且耗时且昂贵。本研究通过引入一种结合人工智能(AI)和物联网(IoT)技术的创新解决方案来应对这些挑战,为鸡蛋海市蜃楼过程的自动化和提高整体鸡蛋质量分析提供了一种变革性方法。这个基于网络的程序利用卷积神经网络(CNN)算法,提供卵子质量的实时反馈。我们的系统在Arduino Nano 33 BLE Sense上实现,TinyML分类F1-Score达到97.4%,准确率达到95.79%,为更精确高效的鸡蛋质量监测方法铺平了道路。这项研究的成功不仅彻底改变了家禽业的鸡蛋质量监测,而且为人工智能和物联网的整合开创了先例,以解决包括电源管理在内的农业实践中的复杂挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TinyML and IoT-enabled system for automated chicken egg quality analysis and monitoring
The poultry industry grapples with challenges in maintaining optimal egg quality during incubation, with environmental factors such as temperature, humidity, and lighting playing crucial roles. Traditional methods of egg quality assessment often lack precision and can be time-consuming and costly. This study addresses these challenges by introducing an innovative solution that combines Artificial Intelligence (AI) and Internet of Things (IoT) technologies, offering a transformative approach to automating the egg mirage process and improving overall egg quality analysis. The web-based program provides real-time feedback on egg quality, utilizing a Convolutional Neural Network (CNN) algorithm. Our system, implemented on Arduino Nano 33 BLE Sense, demonstrated remarkable performance with a TinyML classification F1-Score of 97.4 % and an accuracy rate of 95.79 %, paving the way for a more precise and efficient method of egg quality monitoring. The success of this research not only revolutionizes egg quality monitoring in the poultry industry but also sets a precedent for the integration of AI and IoT in addressing complex challenges in agricultural practices, including power management.
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