Readability of visual and electronic leg tags versus rumen boluses and electronic ear tags for the permanent identification of dairy goats

Lightweight individual cow identification based on Ghost combined with attention mechanism

Individual cow identification is a prerequisite for intelligent dairy farming management, and is important for achieving accurate and informative dairy farming. Computer vision-based approaches are widely considered because of their non-contact and practical advantages. In this study, a method based on the combination of Ghost and attention mechanism is proposed to improve ReNet50 to achieve non-contact individual recognition of cows. In the model, coarse-grained features of cows are extracted using a large sensory field of cavity convolution, while reducing the number of model parameters to some extent. ResNet50 consists of two Bottlenecks with different structures, and a plug-and-play Ghost module is inserted between the two Bottlenecks to reduce the number of parameters and computation of the model using common linear operations without reducing the feature map. In addition, the convolutional block attention module (CBAM) is introduced after each stage of the model to help the model to give different weights to each part of the input and extract the more critical and important information. In our experiments, a total of 13 cows' side view images were collected to train the model, and the final recognition accuracy of the model was 98.58%, which was 4.8 percentage points better than the recognition accuracy of the original ResNet50, the number of model parameters was reduced by 24.85 times, and the model size was only 3.61 MB. In addition, to verify the validity of the model, it is compared with other networks and the results show that our model has good robustness. This research overcomes the shortcomings of traditional recognition methods that require human extraction of features, and provides theoretical references for further animal recognition.

Individual dairy cow identification based on lightweight convolutional neural network

In actual farms, individual livestock identification technology relies on large models with slow recognition speeds, which seriously restricts its practical application. In this study, we use deep learning to recognize the features of individual cows. Alexnet is used as a skeleton network for a lightweight convolutional neural network that can recognise individual cows in images with complex backgrounds. The model is improved for multiple multiscale convolutions of Alexnet using the short-circuit connected BasicBlock to fit the desired values and avoid gradient disappearance or explosion. An improved inception module and attention mechanism are added to extract features at multiple scales to enhance the detection of feature points. In experiments, side-view images of 13 cows were collected. The proposed method achieved 97.95% accuracy in cow identification with a single training time of only 6 s, which is one-sixth that of the original Alexnet. To verify the validity of the model, the dataset and experimental parameters were kept constant and compared with the results of Vgg16, Resnet50, Mobilnet V2 and GoogLenet. The proposed model ensured high accuracy while having the smallest parameter size of 6.51 MB, which is 1.3 times less than that of the Mobilnet V2 network, which is famous for its light weight. This method overcomes the defects of traditional methods, which require artificial extraction of features, are often not robust enough, have slow recognition speeds, and require large numbers of parameters in the recognition model. The proposed method works with images with complex backgrounds, making it suitable for actual farming environments. It also provides a reference for the identification of individual cows in images with complex backgrounds.

Comparison of visual and electronic devices for individual identification of dromedary camels under different farming conditions

The camel industry uses traditional (i.e., iron brands and ear tags) and modern (i.e., microchips) identification (ID) systems without having performance results of reference. Previously iron-branded ( = 45; 1 yr) and microchipped ( = 59; 7 yr) camels showed problems of healing (8.6% of brands) and reading (only 42.9% of brands and 69.5% of microchips were readable), which made their use inadvisable. With the aim of proposing suitable ID systems for different farming conditions, an on-field study was performed using a total of 528 dromedaries at 4 different locations (Egypt, = 83; Spain, = 304; Saudi Arabia, = 90; and Tunisia, = 51). The ID devices tested were visual (button ear tags, 28.5 mm diameter, = 178; double flag ear tags, 50 by 15 mm, = 83; both made of polyurethane) and electronic (ear tags, = 90, and rumen boluses, = 555). Electronic ear tags were polyurethane-loop type (75 by 9 mm) with a container in which a 22-mm transponder of full-duplex technology was lodged. Electronic boluses of 7 types, varying in dimensions (50 to 76 mm length, 11 to 21 mm width, and 12.7 to 82.1 g weight) and specific gravity (SG; 1.49 to 3.86) and each of them containing a 31-mm transponder of half-duplex technology, were all administered to the dromedaries at the beginning of the study. When a low-SG bolus was lost, a high-SG bolus was readministered. Readability rates of each ID system were evaluated during 1 to 3 yr, according to device and location, and yearly values were estimated for comparison. On a yearly basis, visual ear tag readability was not fully satisfactory; it was lower for rectangular ear tags (66.3%) than for button ear tags (80.9%). Yearly readability of electronic ear tags was 93.7%. Bolus readability dramatically varied according to their SG; the SG < 2.0 boluses were fully lost after 8 mo. In contrast, the SG > 3.0 boluses were efficiently retained (99.6 to 100%) at all locations. In conclusion, according to the expected long lifespan of camels, low ID performances were observed for iron brands, injectable microchips, and ear tags (visual and electronic), making their use inadvisable as unique ID systems in camels. The high readability of dense electronic boluses recommended their use as a permanent ID device of reference in camels.

Performance of electronic and visual ear tags in lambs under extensive conditions in Turkey

Abstract. The objective of this study was to evaluate the effectiveness of electronic and visual ear tags in animal traceability, and to investigate the effect of placement site on ear-tag retention in Akkaraman lambs under rural conditions. A total of 380 lambs were identified with electronic and visual ear tags. Electronic and visual ear tags displayed 98.9 and 98.7 % readability at the end of 7 months, and 98.0 and 98.0 % readability at the end of the first year after tagging, respectively. Regarding the placement site, it was observed that there was more loss in ear tags placed on the mid-point part of the ear than the first-quarter part from the head side, but the difference was not statistically significant (P > 0.05). Breakages and electronic failures were not recorded during this study. In conclusion, electronic and visual ear tags demonstrated similar on-farm efficiency for the identification of Akkaraman lambs and fulfilled the minimum efficiency of 98 % required by the International Committee for Animal Recording (ICAR) for an official animal identification device at the end of the first year after tagging. Based on the findings of the study, placement of the ear tag in a cranial position and near the base of the ear would be advised.