Deep Learning Based Real-Time Body Condition Score Classification System

Abstract

The number of animals worldwide is increasing day by day to meet the increasing animal protein needs. Depending on the increase in animal production, yield amount which can be obtained from per unit area can be increased by increasing the number of animals. In dairy cattle farms, it is necessary to group the animals according to their body condition score (BCS) and to care and feed the animals at certain times. Under normal conditions, these processes should be conducted by animal caregivers or experts coming to the enterprise. BCS ratings conducted by experts on farms based on visual examination may give unreliable results and may include misinterpretations. Therefore, technology-supported systems are required. In this study, the prediction of BCS, which is the most important indicator of proper feeding of dairy cattle, is aimed. In addition, by adapting the designed system to simple, fast and user-friendly mobile software, it will be possible to provide tests in enterprise environments in a shorter time. In the design of the system, deep learning models, which have been used frequently in recent years in computer science, have been used. The CNN model, which was trained with a 94.69% success rate through these data, has been converted into a mobile-friendly format for real-time tests. It is aimed to make real-time tests and provide easy access for dairy producers with the help of the designed mobile software. In order to increase the success of the CNN architecture, pre-trained networks have been utilized. In the study, VGG19 pre-trained network, whose success rate has been proved in the previous studies conducted in the literature, was used in model design. The 78.0% performance results obtained from the study indicate that pre-trained CNN architectures based on deep learning are successful for the real-time BCS classification problem.

Impact Evaluation of Score Classes and Annotation Regions in Deep Learning-Based Dairy Cow Body Condition Prediction

“…Several possible approaches to estimating cattle body conditions using neural networks exist [21][22][23]. The more common approach in the literature is to use recordings of animals scored with high-resolution scoring, such as in 0.25 or 0.5 unit increments, to train the neural network and to test how the trained network performs in terms of prediction reliability at the same scale.…”

Impact evaluation of score classes and annotation regions in deep learning-based dairy cow body condition prediction

“…Several possible approaches to estimating cattle body conditions using neural networks exist [14][15][16]. The more common approach in the literature is to use recordings of animals scored with high-resolution scoring, for example, 0.25 or 0.5-unit increments, to train the neural network and to test how the trained network performs in terms of prediction reliability at the same scale.…”

Two-stages of segmentation to improve accuracy of deep learning models based on dairy cow morphology

“…The previous studies that have predicted body conditions with cow side view using deep learning include [14]: i) the detection of cows using a deep learning framework [15], ii) cow's detection with video cementation mask R-CNN and faster R-CNN [16], iii) identification of dairy cow classification using CNN-long short-term memory (LSTM) with a top view [17], and iv) CORF3D contour map with the application of Holstein cow recognition for red, green, blue (RGB) and cow top view thermal image [18].…”