Validation of a Commercial Collar-Based Sensor for Monitoring Eating and Ruminating Behaviour of Dairy Cows

New approaches to selecting a scan-sampling method for chicken behavioral observations and their practical implications

The use of the scan-sampling method, especially when a large amount of data is collected, has become widespread in behavioral studies. However, there are no specific guidelines regarding the choice of the sampling interval in different conditions. Thus, establishing a standard approach for video analysis represents an important step forward within the scientific community. In the present work, we hypothesized that the length of the sampling interval could influence the results of chicken behavioral study, for which we evaluated the reliability, accuracy, and validity of three different sampling intervals (10, 15 and 30 min). The Bland-Altman test was proposed as an innovative approach to compare sampling intervals and support researcher choices. Moreover, these sampling intervals were applied to compare the behavior of 4 chicken genotypes kept under free-range conditions. The Bland-Altman plots suggested that sampling intervals greater than 10 min lead to biases in the estimation of rare behaviors, such as "Attacking". In contrast, the 30-min sampling interval was able to detect differences among genotypes in high-occurrence behaviors, such as those associated with locomotory activity. Thus, from a practical viewpoint, when a broad characterization of chicken genotypes is required, the 30-min scan-sampling interval might be suggested as a good compromise between resources and results.

CNN-Bi-LSTM: A Complex Environment-Oriented Cattle Behavior Classification Network Based on the Fusion of CNN and Bi-LSTM

Cattle behavior classification technology holds a crucial position within the realm of smart cattle farming. Addressing the requisites of cattle behavior classification in the agricultural sector, this paper presents a novel cattle behavior classification network tailored for intricate environments. This network amalgamates the capabilities of CNN and Bi-LSTM. Initially, a data collection method is devised within an authentic farm setting, followed by the delineation of eight fundamental cattle behaviors. The foundational step involves utilizing VGG16 as the cornerstone of the CNN network, thereby extracting spatial feature vectors from each video data sequence. Subsequently, these features are channeled into a Bi-LSTM classification model, adept at unearthing semantic insights from temporal data in both directions. This process ensures precise recognition and categorization of cattle behaviors. To validate the model's efficacy, ablation experiments, generalization effect assessments, and comparative analyses under consistent experimental conditions are performed. These investigations, involving module replacements within the classification model and comprehensive analysis of ablation experiments, affirm the model's effectiveness. The self-constructed dataset about cattle is subjected to evaluation using cross-entropy loss, assessing the model's generalization efficacy across diverse subjects and viewing perspectives. Classification performance accuracy is quantified through the application of a confusion matrix. Furthermore, a set of comparison experiments is conducted, involving three pertinent deep learning models: MASK-RCNN, CNN-LSTM, and EfficientNet-LSTM. The outcomes of these experiments unequivocally substantiate the superiority of the proposed model. Empirical results underscore the CNN-Bi-LSTM model's commendable performance metrics: achieving 94.3% accuracy, 94.2% precision, and 93.4% recall while navigating challenges such as varying light conditions, occlusions, and environmental influences. The objective of this study is to employ a fusion of CNN and Bi-LSTM to autonomously extract features from multimodal data, thereby addressing the challenge of classifying cattle behaviors within intricate scenes. By surpassing the constraints imposed by conventional methodologies and the analysis of single-sensor data, this approach seeks to enhance the precision and generalizability of cattle behavior classification. The consequential practical, economic, and societal implications for the agricultural sector are of considerable significance.

Precision technologies for the management of reproduction in dairy cows

Precision livestock farming (PLF) utilizes information and communication technology (ICT) to continuously monitor, control, and enhance the productivity, reproduction, health, welfare, and environmental impact of livestock. Technological advancements have facilitated the seamless flow of information from animals to humans, enabling practical decision-making processes concerning health, reproduction management, and calving surveillance. With the increasing population of livestock per farm, it has become impractical for farmers to individually track every animal within these large groups. Historically, cattle management decisions heavily relied on human observation, judgment, and experience. However, it is impossible for a single individual to gather reliable audio-visual monitoring data round the clock. Presently, dairy cows exhibit subtler indicators of estrus, resulting in a substantial chance of missing an estrus cycle. Furthermore, calving complications sometimes go unnoticed on farms, resulting in a higher number of culled cattle. In addition, an increasing number of crossbred cows experience delayed return to estrus after calving due to low body condition scores (BCS). The decline in BCS during the dry period is associated with a reduced likelihood of pregnancy following the first and second postpartum inseminations. Precision technologies enable the monitoring and tracking of an individual cow's physiological behavior and reproductive parameters, thereby optimizing management practices and farm performance. Despite the exploration of various technologies, there are still some common challenges that need to be addressed, including battery lifespan, transmission range, specificity and sensitivity, storage capacity, and economic affordability. Nonetheless, the demand for these tools from farmers and researchers is growing, and the implementation of PLF in grazing systems can yield positive outcomes in terms of animal reproductive welfare and labor optimization. This review primarily focuses on the different aspects of reproduction management in dairy using sensors, automated cameras, and various computer software.

Assessment of a UWB Real Time Location System for Dairy Cows' Monitoring

In the field of precision livestock farming, many systems have been developed to identify the position of each cow of the herd individually in a specific environment. Challenges still exist in assessing the adequacy of the available systems to monitor individual animals in specific environments, and in the design of new systems. The main purpose of this research was to evaluate the performance of the SEWIO ultrawide-band (UWB) real time location system for the identification and localisation of cows during their activity in the barn through preliminary analyses in laboratory conditions. The objectives included the quantification of the errors performed by the system in laboratory conditions, and the assessment of the suitability of the system for real time monitoring of cows in dairy barns. The position of static and dynamic points was monitored in different experimental set-ups in the laboratory by the use of six anchors. Then, the errors related to a specific movement of the points were computed and statistical analyses were carried out. In detail, the one-way analysis of variance (ANOVA) was applied in order to assess the equality of the errors for each group of points in relation to their positions or typology, i.e., static or dynamic. In the post-hoc analysis, the errors were separated by Tukey's honestly significant difference at p > 0.05. The results of the research quantify the errors related to a specific movement (i.e., static and dynamic points) and the position of the points (i.e., central area, perimeter of the investigated area). Based on the results, specific information is provided for the installation of the SEWIO in dairy barns as well as the monitoring of the animal behaviour in the resting area and the feeding area of the breeding environment. The SEWIO system could be a valuable support for farmers in herd management and for researchers in the analysis of animal behavioural activities.

Effects of extrinsic factors on some rumination patterns: A review

The rumen and its activity, rumination, are the characterizing traits of the suborder Ruminantia, and it is accompanied by related feeding habits and consequent animal behavior. Several extrinsic (not related to the animal itself) factors affect rumination behavior; most are reflected in rumination timing (considering overall daily duration as well as circadian differences in rumination patterns): age, environmental factors, and diet. For these reasons, great efforts have been sustained at the research level for monitoring rumination patterns. Currently, some research outcomes are applied at the farm level; others are still at the experimental level. All of these efforts are finalized mainly for the use of rumination pattern recording as an effective prediction tool for the early detection of health and welfare problems, both in a single head and at the herd level. Moreover, knowledge of the effects of extrinsic factors on rumination physiology represents a great challenge for improving the efficiency of ruminant livestock management, from the diet to the housing system, from parasites to heat stress. The present review deals mainly with the worldwide raised ruminant species.