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• W4320719776 abstract "Aflatoxin poisoning can harm broiler health and cause economic loss. Traditional diagnoses rely on human assessment, which is laborious and time-consuming. The objective of this study was to identify aflatoxin-poisoned broilers by analyzing three-dimensional accelerations collected by wearable accelerometers using five machine learning models, including K-Nearest Neighbor (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT). A total of 75 yellow feather broilers were reared in 3 pens (25 birds/pen) provided with 3 types of feed, including Normal (no Aflatoxin B1, control), AF50 (50 μg/kg Aflatoxin B1 “AFB1”), and AF100 (100 μg/kg AFB1). Lightweight tri-axial accelerometers were attached to 9 randomly selected birds (3 birds/pen) to continuously monitor the movement of broilers. Broiler behaviors were manually labelled as six categorises (sitting, standing, drinking, feeding, walking, and other) by observing the videos recorded by the camera installed in each pen. Different behavioral patterns were observed for AFB1 poisoned birds as compared to that for sound birds. The poisoned broilers performed less standing, walking, feeding, and drinking behaviors, but more sitting behaviors. Five models could identify poisoned birds (AF100) with good accuracies (>86%). Higher misclassification rates were observed for all models in classifying AF50 and Normal groups. Among different models, the GBDT model performed best (97%) in differentiating Normal and AF50 broilers. In conclusion, three-dimensional accelerations and machine learning models could be used to identify aflatoxin-poisoned broilers with high accuracies. The findings could help in the development of viable systems that can identify diseased birds." @default.
• W4320719776 created "2023-02-15" @default.
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• W4320719776 date "2023-03-01" @default.
• W4320719776 modified "2023-10-16" @default.
• W4320719776 title "Identification of aflatoxin-poisoned broilers based on accelerometer and machine learning" @default.
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• W4320719776 doi "https://doi.org/10.1016/j.biosystemseng.2023.01.021" @default.
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