Application of Random Forest Algorithm for Automatic Monitoring Weight of Broilers
Authors: 
Hui Zhang, Changsi Li, Heng Liu, Yang ZhangAuthors Info & Claims
Hui Zhang, Changsi Li, Heng Liu, Yang ZhangAuthors Info & ClaimsPages 64 - 69
Abstract
To improve the efficiency of broiler farming and the accuracy of automatic weight monitoring, an Internet of Things (IoT)-based automatic weight monitoring system was designed in this study. The system utilizes weighing sensors to collect broiler body weight data and tests the performance of four supervised learning algorithms: random forest (RF), support vector machine (SVM), decision tree (DT), and gradient boosted decision tree (GBDT) in the evaluation of broiler weight monitoring data. By comparing the F1-score and Area Under the ROC Curve (AUC) of these algorithms, the Random Forest algorithm performs optimally among all candidate algorithms with an F1-score of 0.9501 and an AUC value of 0.8147. The comparison of the daily average body weight data, processed using the random forest algorithm, with actual data has demonstrated that the average relative error of the IoT-based automatic broiler weight monitoring system can be kept within 5%, thereby validating the method's effectiveness.
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Index Terms
- Application of Random Forest Algorithm for Automatic Monitoring Weight of Broilers
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Published In
June 2024
1206 pages
ISBN:9798400710247
DOI:10.1145/3690407
Copyright © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
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Association for Computing Machinery
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Published: 24 October 2024
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CAIBDA 2024
CAIBDA 2024: 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms
June 21 - 23, 2024
Zhengzhou, China
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