Detection of Remaining Feed in the Feed Troughs of Flat-Fed Meat Ducks Based on the RGB-D Sensor and YOLO V8

文献类型： 外文期刊

第一作者： Tan, Xueliang

作者： Tan, Xueliang;Yuan, Junjie;Wang, Jizhang;Ying, Shijia

作者机构：

关键词： localization of feed troughs; deep learning; point cloud processing; volumetric determination

期刊名称：ANIMALS （ 影响因子：2.7； 五年影响因子：3.2 ）

ISSN： 2076-2615

年卷期： 2025 年 15 卷 10 期

页码：

收录情况： SCI

摘要： The remaining feed in the feed troughs affects the feeding management of flat-raised meat ducks. Ground-contact detection methods all involve modifications to the feeding troughs, but the breeding setting of flat-raised meat ducks does not allow for on-site electrical wiring installation. Additionally, the existing non-contact methods do not directly detect the remaining feed quantity in the feeding troughs. To tackle this problem, this study employs a novel approach by first capturing images of the feed troughs using an RGB-D sensor. Subsequently, YOLOv8 is utilized to identify the positions of the feed troughs, and the volume of the remaining feed is determined through point cloud processing. The accuracy of this detection method was evaluated using various types of feed troughs and feed particle sizes. The experimental results reveal both a strong correlation between the calculated and actual feed volumes (with R2 values exceeding 0.86, indicating a consistent trend) and a low prediction error, as quantified by the root mean square error (RMSE). Analyses of the correction coefficients and corresponding RMSE values indicated a positive correlation between the correction coefficient and the curvature of the feeding trough, while no correlation was observed with the trough diameter or granule particle size, maintaining a low RMSE value. The findings of this research demonstrate the effectiveness of the proposed method for detecting the remaining feed in troughs. This method facilitates precise feed management, minimizes residual feed, and enhances the living conditions of meat ducks.

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