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利用图像分析检测商品肉鸡胴体中的木胸状况。

Detection of woody breast condition in commercial broiler carcasses using image analysis.

作者信息

Caldas-Cueva Juan P, Mauromoustakos A, Sun X, Owens Casey M

机构信息

Department of Poultry Science, University of Arkansas, Fayetteville 72701, USA.

Agricultural Statistics Laboratory, University of Arkansas, Fayetteville 72701, USA.

出版信息

Poult Sci. 2021 Apr;100(4):100977. doi: 10.1016/j.psj.2020.12.074. Epub 2021 Jan 14.

Abstract

Image analysis could be an objective and rapid method to identify woody breast (WB) myopathy and benefit the global poultry industry. The objective of this study was to determine if there are conformational changes that can be used to detect WB characteristics in commercial broiler carcasses across strains, gender, and ages using image analysis. A total of 900 images of male and female broiler carcasses from commercial standard and high breast-yielding strains and 5 ages (6 through 10 wk) were captured before evisceration. These images were processed and analyzed using ImageJ software. Conformational measurements were M0: breast length; M1: breast width in the cranial region; M2: vertical line from the tip of keel to 1/5th of breast length; M3: breast width at the end of M2; M4: angle formed at the tip of keel and extending to outer points of M3; M5: area of the triangle formed by M3 and lines generated by M4; M6: area of the breast above M3; M7: M6 minus M5. Ratios of these measurements were also considered. Intact breast fillets were scored for WB severity based on tactile evaluation. Regardless of strain, sex, and age, M11 (M1/M0), M9 (M3/M2), and M4 had the highest correlation to WB score (r ≥ 0.65; P < 0.01). Overall, the best validated model (Gen. R = 0.61) to predict WB included M1, M2, and M3. Using this model, 91% of broiler carcasses were properly classified as normal or WB along with a sensitivity of 71% to detect affected carcasses. Although the predictive performance of models for detecting the WB condition using these measurements was associated with the broiler strain, sex, and age or live weight, these data also support the feasibility of using image analysis to predict WB defect in broiler carcasses. The possible integration of these image measurements into commercial noncontact, nondestructive, and fast in-line vision grading systems would allow processors to identify broilers with WB and potentially sort, provide large-scale information downstream to further processing operations and upstream to live production.

摘要

图像分析可能是一种识别木胸(WB)肌病的客观且快速的方法,对全球家禽业有益。本研究的目的是确定是否存在构象变化,可利用图像分析来检测不同品系、性别和年龄的商业肉鸡胴体中的WB特征。在去内脏之前,共采集了来自商业标准品系和高胸肉产量品系、5个年龄(6至10周)的900张雄性和雌性肉鸡胴体图像。这些图像使用ImageJ软件进行处理和分析。构象测量指标为:M0:胸长;M1:胸部颅侧区域宽度;M2:从龙骨尖端到胸长的1/5处的垂直线;M3:M2末端的胸部宽度;M4:在龙骨尖端形成并延伸至M3外部点的角度;M5:由M3和M4生成的线所形成的三角形面积;M6:M3上方的胸部面积;M7:M6减去M5。还考虑了这些测量值的比率。根据触觉评估对完整的胸肉进行WB严重程度评分。无论品系、性别和年龄如何,M11(M1/M0)、M9(M3/M2)和M4与WB评分的相关性最高(r≥0.65;P<0.01)。总体而言,预测WB的最佳验证模型(总体R=0.61)包括M1、M2和M3。使用该模型,91%的肉鸡胴体被正确分类为正常或WB,检测受影响胴体的灵敏度为71%。尽管使用这些测量值检测WB状况的模型的预测性能与肉鸡品系、性别、年龄或活重有关,但这些数据也支持利用图像分析预测肉鸡胴体中WB缺陷的可行性。将这些图像测量值可能整合到商业非接触、无损和快速在线视觉分级系统中,将使加工者能够识别患有WB的肉鸡,并有可能进行分类,为下游进一步加工操作和上游活禽生产提供大规模信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be0f/8046952/f55a40f9b5cd/gr1.jpg

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