College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an 271018, China.
Shandong Provincial Key Laboratory of Horticultural Machineries and Equipment, Tai'an 271018, China.
Sensors (Basel). 2022 Apr 24;22(9):3271. doi: 10.3390/s22093271.
The feeding behaviour of cows is an essential sign of their health in dairy farming. For the impression of cow health status, precise and quick assessment of cow feeding behaviour is critical. This research presents a method for monitoring dairy cow feeding behaviour utilizing edge computing and deep learning algorithms based on the characteristics of dairy cow feeding behaviour. Images of cow feeding behaviour were captured and processed in real time using an edge computing device. A DenseResNet-You Only Look Once (DRN-YOLO) deep learning method was presented to address the difficulties of existing cow feeding behaviour detection algorithms' low accuracy and sensitivity to the open farm environment. The deep learning and feature extraction enhancement of the model was improved by replacing the CSPDarknet backbone network with the self-designed DRNet backbone network based on the YOLOv4 algorithm using multiple feature scales and the Spatial Pyramid Pooling (SPP) structure to enrich the scale semantic feature interactions, finally achieving the recognition of cow feeding behaviour in the farm feeding environment. The experimental results showed that DRN-YOLO improved the accuracy, recall, and mAP by 1.70%, 1.82%, and 0.97%, respectively, compared to YOLOv4. The research results can effectively solve the problems of low recognition accuracy and insufficient feature extraction in the analysis of dairy cow feeding behaviour by traditional methods in complex breeding environments, and at the same time provide an important reference for the realization of intelligent animal husbandry and precision breeding.
奶牛的进食行为是奶牛养殖健康状况的重要标志。为了准确快速评估奶牛的进食行为，对奶牛的进食行为进行精确快速的评估至关重要。本研究提出了一种利用边缘计算和深度学习算法监测奶牛进食行为的方法，该方法基于奶牛进食行为的特点。利用边缘计算设备实时捕获和处理奶牛进食行为的图像。针对现有奶牛进食行为检测算法精度低、对开放式农场环境的敏感性差等问题，提出了一种基于 DenseResNet-You Only Look Once（DRN-YOLO）的深度学习方法。通过使用多尺度特征和空间金字塔池化（SPP）结构替换 CSPDarknet 骨干网络，提高了模型的深度学习和特征提取能力，丰富了尺度语义特征的交互，最终实现了对农场饲养环境中奶牛进食行为的识别。实验结果表明，与 YOLOv4 相比，DRN-YOLO 的准确率、召回率和 mAP 分别提高了 1.70%、1.82%和 0.97%。本研究结果可以有效解决传统方法在复杂养殖环境下分析奶牛进食行为时识别精度低、特征提取不足的问题，同时为实现智能畜牧业和精准养殖提供了重要参考。
