Tan Xueliang, Yuan Junjie, Ying Shijia, Wang Jizhang
School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
Institute of Animal Science, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China.
Animals (Basel). 2025 May 16;15(10):1440. doi: 10.3390/ani15101440.
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 R 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.
料槽中剩余的饲料会影响平养肉鸭的饲养管理。地面接触式检测方法都涉及对料槽的改造,但平养肉鸭的养殖环境不允许进行现场电气布线安装。此外,现有的非接触式方法不能直接检测料槽中剩余的饲料量。为了解决这个问题,本研究采用了一种新颖的方法,首先使用RGB-D传感器拍摄料槽的图像。随后,利用YOLOv8识别料槽的位置,并通过点云处理确定剩余饲料的体积。使用各种类型的料槽和饲料颗粒尺寸对这种检测方法的准确性进行了评估。实验结果表明,计算出的饲料体积与实际饲料体积之间具有很强的相关性(R值超过0.86,表明趋势一致),并且预测误差很低,以均方根误差(RMSE)量化。对校正系数和相应RMSE值的分析表明,校正系数与料槽的曲率之间存在正相关,而与料槽直径或颗粒尺寸没有相关性,RMSE值保持较低。本研究的结果证明了所提出的检测料槽中剩余饲料方法的有效性。这种方法有助于精确的饲料管理,减少剩余饲料,并改善肉鸭的生活条件。