PDF(Pubmed)
Abstract：
Automated milking systems (AMSs) already incorporate a variety of milk monitoring and sensing equipment, but the sensitivity, specificity, and positive predictive value of clinical mastitis (CM) detection remain low. A typical symptom of CM is the presence of clots in the milk during fore-stripping. The objective of this study was the development and evaluation of a deep learning model with image recognition capabilities, specifically a convolutional neural network (NN), capable of detecting such clots on pictures of the milk filter socks of the milking system, after the phase in which the first streams of milk have been discarded. In total, 696 pictures were taken with clots and 586 pictures without. These were randomly divided into 60/20/20 training, validation, and testing datasets, respectively, for the training and validation of the NN. A convolutional NN with residual connections was trained, and the hyperparameters were optimized based on the validation dataset using a genetic algorithm. The integrated gradients were calculated to explain the interpretation of the NN. The accuracy of the NN on the testing dataset was 100%. The integrated gradients showed that the NN identified the clots. Further field validation through integration into AMS is necessary, but the proposed deep learning method is very promising for the inline detection of CM on AMS farms.
摘要：
自动挤奶系统（AMSs）已经集成了各种牛奶监测和传感设备，但是灵敏度，特异性，临床乳腺炎（CM）检测的阳性预测值仍然很低。CM的典型症状是在前剥离期间牛奶中存在凝块。这项研究的目的是开发和评估具有图像识别功能的深度学习模型，特别是卷积神经网络(NN)，能够在挤奶系统的牛奶过滤袜的图片上检测到这种凝块，在第一批牛奶被丢弃的阶段之后。总的来说,696张照片与凝块和586张照片没有。这些被随机分为60/20/20训练，验证,和测试数据集，分别,用于训练和验证NN。训练了具有残差连接的卷积NN，并使用遗传算法基于验证数据集对超参数进行优化。计算积分梯度以解释NN的解释。在测试数据集上的NN的准确度为100%。整合的梯度显示NN识别了凝块。需要通过集成到AMS进行进一步的现场验证，但是所提出的深度学习方法对于AMS农场的CM在线检测非常有前途。
