一个带有x波段同轴波导适配器的自由空间介电系统,用于未孵化鸡蛋的无损生育检测:优化频谱、方向、特征和分类器

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Niloufar Akbarzadeh , Seyed Ahmad Mireei , Gholam Reza Askari , Mohammad Sedghi , Abbas Hemmat
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引用次数: 0

摘要

在孵化前检测不育卵可以显著提高孵化率,减少与数十亿个不育卵相关的损失。本研究采用带有x波段同轴波导适配器的自由空间介电装置,在孵化前评估卵子的生育能力。在8-12 GHz微波频谱范围内,以反射和透射两种模式分析了散射参数,并在两个不同的方向上检测了鸡蛋。每个样品取向产生8个光谱,这些光谱使用各种技术进行预处理。采用7个分类器对可育卵和不育卵进行分类,得到576个分类模型,旨在确定区分未孵化可育卵和不育卵的最佳光谱、样品取向、预处理方法和分类器。确定了垂直方向上S21模式下的插入损耗谱(IL_S21)为最佳条件。然后评估几种特征选择方法以确定最具信息量的频率。利用选定的有效频率,利用人工神经网络(ANN)、随机森林(RF)和增强树(BT)开发了预测模型。值得注意的是,竞争自适应重加权抽样(CARS)方法始终优于其他方法,产生具有优异f1分数100%的鲁棒BT模型。在BT模型中,基于cart的特征灵敏度为96.00%,特异度为93.55%,精度为92.31%,准确度为94.64%,f1评分为94.12%,与基于全光谱数据的BT模型(f1评分为98.04%)相当。在基于car的方法中,在car选择特征的更高精度和更局部化的频率选择之间存在权衡。这项研究强调了自由空间介电装置在孵化前可靠地区分可育蛋和不育蛋方面的有效性,为家禽业提供了实质性的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A free-space dielectric system with X-band coaxial-to-waveguide adapters for nondestructive fertility detection in unincubated chicken eggs: Optimizing spectrum, orientation, features, and classifiers
Detecting infertile eggs before incubation can significantly improve hatch rates and reduce losses associated with billions of infertile eggs. This study employed a free-space dielectric setup with X-band coaxial-to-waveguide adapters to assess egg fertility prior to incubation. Scattering parameters within the 8–12 GHz microwave spectrum were analyzed in both reflectance and transmittance modes, with eggs examined in two distinct orientations. Each sample orientation generated eight spectra, which were preprocessed using various techniques. Seven classifiers were applied to differentiate fertile from infertile eggs, resulting in 576 classification models aimed at identifying the optimal spectrum, sample orientation, preprocessing method, and classifier for distinguishing unincubated fertile and infertile eggs. The insertion loss spectrum in S21 mode (IL_S21) in the vertical orientation was identified as the optimal condition. Several feature selection methods were then evaluated to determine the most informative frequencies. Predictive models were developed using artificial neural networks (ANN), random forest (RF), and boosted trees (BT), leveraging the selected effective frequencies. Notably, the competitive adaptive reweighted sampling (CARS) approach consistently outperformed other methods, yielding robust BT models with an exceptional F1-score of 100 %. In the BT model, CART-based features achieved a sensitivity of 96.00 %, specificity of 93.55 %, precision of 92.31 %, accuracy of 94.64 %, and an F1-score of 94.12 %, comparable to the BT model based on full spectral data (F1-score of 98.04 %). A trade-off exists between the higher accuracy of CARS-selected features and the more localized frequency selection in the CART-based approach. This study highlights the effectiveness of free-space dielectric setups in reliably distinguishing fertile from infertile eggs prior to incubation, offering substantial implications for the poultry industry.
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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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