Search Results (76)

Accurate estrus detection is essential for dairy farm productivity, yet traditional manual and wearable methods remain limited by high labor costs, delayed responses, and animal stress. To address these challenges, we propose CE-YOLO, a lightweight YOLOv11n-based vision model tailored for edge deployment, which detects mounting behavior by integrating a Channel-Aware Downsampling (CA-Down) module to preserve small-scale features, a SimSPPF module for efficient contextual fusion, and a DySample module for dynamic spatial alignment. Experiments on a curated estrus behavior dataset demonstrate that CE-YOLO achieves a precision of 94.9% and an mAP50 of 98.2%, significantly outperforming the baseline by 3.9% and 4.6% respectively. These results validate the model as an efficient, non-intrusive solution for real-time estrus monitoring, strongly supporting the advancement of smart livestock management. Full article

Technological advancements have significantly improved cattle farming, particularly in sensor-based activity monitoring for health management, estrus detection, and overall herd supervision. However, such a sensor-based monitoring framework often illustrates several issues, such as high cost, animal discomfort, and susceptibility to false measurement. This study introduces a vision-based cattle activity monitoring approach deployed in a commercial Nestlé dairy farm, specifically one that is estrus-focused, where overhead cameras capture unconstrained herd behavior under variable lighting, occlusions, and crowding. A custom dataset of 2956 Images are collected and then annotated into four fine-grained behaviors—standing, lying, grazing, and estrus—enabling detailed analysis beyond coarse activity categories commonly used in prior livestock monitoring studies. Furthermore, computer vision-based deep learning algorithms are deployed on this dataset to classify the aforementioned classes. A comparative analysis of YOLOv8 and YOLOv9 is provided, which clearly illustrates that YOLOv8-L achieved a mAP of 91.11%, whereas YOLOv9-E achieved a mAP of 90.23%. Full article

Background: Sex differences in physiology have garnered significant interest of late; however, comparatively little is known about the effects of sex on the function of the peripheral taste system. Previously, we have shown that fat taste functions in a sexually dimorphic manner using molecular, cellular, and behavioral assays, and that a subtype of estrogen receptor (ER) proteins is highly expressed in Type II (receptor) cells. The underlying mechanisms of estrogen’s action, though, remain unknown. Objective: Here, we sought to better understand estrogen’s role in fat taste transduction at the molecular level by initially focusing on the transient receptor potential channel types M4 (Trpm4) and M5 (Trpm5), which we have shown to play roles in estrogen-sensitive fatty acid signaling in taste cells. Methods/Results: Using a multidisciplinary approach, using Trpm5-deficient mice, electrophysiological and calcium imaging assays revealed that there are significantly reduced FA responses in both males and females in the estrus phase, whereas females in the proestrus phase did not show this, suggesting that there may be E2-dependent TRPM5-independent FA signaling in Type II cells. During periods of high levels of circulating estrogen, there was no significant difference in cellular responses to fatty acid (FA) stimuli between Trpm5^−/− mice and their wild-type counterparts. Moreover, supplemental estradiol enhanced linoleic acid (LA)-induced TRPM5-mediated taste cell activation. Finally, while Type II cells depend on TRPM4 and TRPM5 for FA taste cell activation, proestrus (high-estrogen) females showed a greater dependence on a TRPM5-independent pathway for fatty acid responsiveness. Conclusions: Together, these results underscore the substantial regulatory role of estrogen in the taste system, particularly for fatty acid signaling. Given that the taste system guides food preferences and intake, these findings may have important implications for understanding sex-specific differences in diet and, ultimately, metabolic health. Full article

Cryptorchidism is a genital defect in which ram testicles fail to descend, causing azoospermia, while maintaining normal behavior towards females. We investigated whether cryptorchid rams can induce a ram-effect in ewes that would then be subjected to artificial insemination (AI). Therefore, ewes were isolated from any contact with rams for 6 weeks, then exposed to cryptorchid rams for 14 days. From day 15 to day 24, estrus was checked using a cryptorchid teaser four times daily (at 08:00, 12:00, 16:00, 20:00). Ewes detected in estrus were inseminated 24 h later. Experiment 1 included ewes (n = 31) all exposed to the cryptorchid ram-effect (CRE): 70.9% showed estrus, lambing rate after AI was 45.5%, and prolificacy was 1.40. Experiment 2 compared CRE (n = 80) with a control group with no prior exposure to males (n = 39). Estrus occurrence differed significantly (75.0% vs. 23.1%, respectively, p ≤ 0.001). Lambing rate from AI was 44.1% and prolificacy 1.27. These results show that cryptorchid rams effectively induce and synchronize estrus in Sarda ewes. AI fertility results on natural estrus following CRE yields outcomes comparable to those previously reported after hormonal synchronization for this breed. Full article

This study aimed to characterize behavioral differences between true estrus (TE) and false estrus (FE) in cows using neck-mounted six-axis inertial measurement unit sensors to reduce false positives in automated detection systems. A retrospective analysis was conducted on 1464 validated estrus alerts from 414 Hanwoo cows across 13 commercial farms in South Korea. Alerts were classified as TE (625 alerts) or FE (839 alerts) based on comprehensive validation criteria, including standing heat observation, artificial insemination records, ovulation confirmation, and pregnancy outcomes. Mounting activity, rumination time, and lying time were analyzed. True estrus exhibited significantly higher (p < 0.0001) total number of mounts and maximum mounting duration compared to FE over the entire observation period. Notably, the maximum number of mounts per hour was higher (p < 0.0001) in FE before alert generation but higher (p < 0.0001) in TE afterward, with FE declining rapidly. Coefficients of variation for rumination and lying time were significantly higher (p < 0.0001) in TE than in FE, indicating greater behavioral disruption. These findings revealed that secondary behavioral signs exhibit distinct quantitative and temporal patterns between TE and FE, suggesting potential criteria that could be integrated into automated detection algorithms to reduce false-positive rates. Full article

The giant panda (Ailuropoda melanoleuca) is a vulnerable animal in China, and it is crucial to improve the reproduction efficiency of the giant panda. Mate preference is an important part of natural mating. We hypothesized that AGS metabolites differ according to their mate preference. In this study, we determined estrus-associated hormone levels in the urine of 19 female giant pandas. After confirming estrus via hormone levels and behavioral observation, we collected environmental biomarkers for metabolomics analysis. A total of 19 samples were divided to two groups according to the mating preference of female giant pandas. Metabolomics analysis by LC-MS/MS showed that a total of 115 differentially expressed metabolites were identified, including 97 upregulated metabolites and 18 downregulated metabolites. We found that prostaglandin B2, palmitoylcarnitine, prostaglandin G2, and estrone may be the potential markers of female mate preference. Pathway enrichment analysis showed that steroid hormone biosynthesis, phenylalanine metabolism, and tropane, piperidine, and pyridine alkaloid biosynthesis were the top three pathways. These results revealed the physiological changes in female giant pandas during mate preference trials, providing a perspective for understanding their chemical communication system reliant on anal gland secretions and improving the success rate of natural mating of giant pandas. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

The identification of estrus at the right moment allows for a higher success of fecundity with artificial insemination. Evaluating changes in body surface temperature of sows during the estrus period using an infrared thermography camera (ITC) can provide an accurate model to predict these changes. This pilot study comprised nine crossbred Large White x Landrace sows, providing 59 data records for analysis. Observed changes in the behavior and physiological signs of the sows signaled the identification of estrus. Images of the ocular area, ear tips, breast, back, vulva, and perianal area were collected with the ITC. The images were analyzed using the FLIR Thermal Studio Starter software. Infrared mean temperatures were reported and compared using ANOVA and Tukey–Kramer tests (p < 0.05). Supervised machine learning models were tested using random forest (RF), Conditional inference trees (Ctree), Partial least squares (PLS), and K-nearest neighbors (KNN), and the method performance was measured using a confusion matrix. The orbital region showed significant differences between estrus and non-estrus states in sows. In the confusion matrix, the algorithm predicted estrus with 87% accuracy in the test set, which contained 40% of the data, when agitated behavior was combined with orbital area temperature. These findings suggest the potential for integrating behavioral and physiological observations with orbital thermography and machine learning to detect estrus in sows under field conditions accurately. Full article

Cow behavior recognition constitutes a fundamental element of effective cow health monitoring and intelligent farming systems. Within large-scale cow farming environments, several critical challenges persist, including the difficulty in accurately capturing behavioral feature information, substantial variations in multi-scale features, and high inter-class similarity among different cow behaviors. To address these limitations, this study introduces an enhanced target detection algorithm for cow behavior recognition, termed RFR-YOLO, which is developed upon the YOLOv11n framework. A well-structured dataset encompassing nine distinct cow behaviors—namely, lying, standing, walking, eating, drinking, licking, grooming, estrus, and limping—is constructed, comprising a total of 13,224 labeled samples. The proposed algorithm incorporates three major technical improvements: First, an Inverted Dilated Convolution module (Region Semantic Inverted Convolution, RsiConv) is designed and seamlessly integrated with the C3K2 module to form the C3K2_Rsi module, which effectively reduces computational overhead while enhancing feature representation. Second, a Four-branch Multi-scale Dilated Attention mechanism (Four Multi-Scale Dilated Attention, FMSDA) is incorporated into the network architecture, enabling the scale-specific features to align with the corresponding receptive fields, thereby improving the model’s capacity to capture multi-scale characteristics. Third, a Reparameterized Generalized Residual Feature Pyramid Network (Reparameterized Generalized Residual-FPN, RepGRFPN) is introduced as the Neck component, allowing for the features to propagate through differentiated pathways and enabling flexible control over multi-scale feature expression, thereby facilitating efficient feature fusion and mitigating the impact of behavioral similarity. The experimental results demonstrate that RFR-YOLO achieves precision, recall, mAP50, and mAP50:95 values of 95.9%, 91.2%, 94.9%, and 85.2%, respectively, representing performance gains of 5.5%, 5%, 5.6%, and 3.5% over the baseline model. Despite a marginal increase in computational complexity of 1.4G, the algorithm retains a high detection speed of 147.6 frames per second. The proposed RFR-YOLO algorithm significantly improves the accuracy and robustness of target detection in group cow farming scenarios. Full article

Determining sow (Sus scrofa domesticus Erxleben) estrus status requires considerable labor investment, and continuous real-time monitoring is impractical. Workers typically identify estrus at scheduled intervals and determine artificial insemination timing based on experience. However, experience-based methods are subjective, vary with operator expertise, and impede standardized management in large-scale farms. This study employs cameras and deep learning to detect sows and analyze postural changes, enabling estrus detection and optimal insemination timing prediction. Experimental results indicate that the proposed method achieved an accuracy of 70% (42/60), where the recommended insemination timing differed by less than 24 h from human decisions. This approach facilitates data-driven estrus detection and insemination scheduling, potentially reducing labor intensity and improving reproductive outcomes, particularly beneficial for labor-intensive and large-scale swine production systems. Full article

This study aimed to evaluate the accuracy of the accelerometer-equipped collar RUMI to detect estrus in dairy cows, establish a recommendation for the optimal timing for artificial insemination (AI) when using this device, and characterize the blood flow of the dominant follicle (F) and the corpus luteum (CL) as ovulation approaches. Forty-seven cycling cows were monitored following synchronization with a modified G6G protocol, allowing for spontaneous ovulation. Ultrasound examinations were conducted every 12 h, starting 48 h after the second PGF2α dose, to monitor uterine and ovarian changes. Blood samples were also collected to determine serum progesterone (P4) levels. Each cow was fitted with a RUMI collar, which continuously monitored behavioral changes to identify the onset, offset, and peak of activity of estrus. One-way ANOVA assessed the relationship between physiological parameters and time before ovulation. Results showed that the RUMI collar demonstrated high specificity (100%), sensitivity (90.90%), and accuracy (93.62%) for estrus detection. The optimal AI window was identified as between 11.4 and 15.5 h after heat onset. Increased blood flow to the F and reduced luteal activity were observed in the 48 h prior to ovulation. Further research is needed to assess the influence of this AI window on conception rates, and if it should be modified considering external factors. Full article

Modern beef cattle reproductive management faces increasing challenges due to the growing global demand for beef. Reproductive efficiency is a critical factor determining the productivity and profitability of beef cattle operations. Optimal reproductive performance in a beef cattle herd is achieved when each cow produces one calf per year, maintaining a calving interval of 365 days. However, this goal is difficult to achieve, as the gestation period in beef cows lasts approximately 280 days, leaving only 80–85 days for successful conception. Traditional methods, such as visual estrus detection, are becoming increasingly unreliable due to expanding herd sizes and the subjectivity of visual observation. Additionally, silent estrus—where ovulation occurs without noticeable behavioral changes—further complicates the accurate estrous-based identification of the optimal insemination period. To enhance reproductive efficiency, advanced technologies are increasingly being integrated into cattle management. Sensor-based monitoring systems, including accelerometers, pedometers, and ruminoreticular boluses, enable the precise tracking of activity changes associated with the estrous cycle. Furthermore, infrared thermography offers a non-invasive method for detecting body temperature fluctuations, allowing for more accurate estrus identification and optimized timing of insemination. The use of these innovative technologies has the potential to significantly improve reproductive efficiency in beef cattle herds and contribute to overall farm productivity and sustainability. The objective of this review is to examine advancements in smart technologies applied to beef cattle reproductive management, presenting commercially available technologies and recent scientific studies on innovative systems. The focus is on sensor-based monitoring systems and infrared thermography for optimizing reproduction. Additionally, the challenges associated with these technologies and their potential to enhance reproductive efficiency and sustainability in the beef cattle industry are discussed. Despite the benefits of advanced technologies, their implementation in cattle farms is hindered by financial and technical challenges. High initial investment costs and the complexity of data analysis may limit their adoption, particularly in small and medium-sized farms. However, the continuous development of these technologies and their adaptation to farmers’ needs may significantly contribute to more efficient and sustainable reproductive management in beef cattle production. Full article

Heat stress (HS) is a major environmental factor negatively impacting the reproductive performance of livestock. This study investigates the molecular mechanisms of heat stress on the hypothalamic–pituitary–ovarian (HPO) axis in Hu sheep. A heat-stressed animal model was established, and high-throughput RNA sequencing (RNA-seq) was employed to analyze gene expression in the hypothalamus, pituitary, and ovarian tissues of both control and heat-stressed groups. The results revealed significant changes in estrus behavior, hormone secretion, and reproductive health in heat-stressed sheep, with a shortened estrus duration, prolonged estrous cycles, and decreased levels of FSH, LH, E₂, and P4. A total of 520, 649, and 482 differentially expressed genes (DEGs) were identified in the hypothalamus, pituitary, and ovary, respectively. The DEGs were enriched in pathways related to hormone secretion, neurotransmission, cell proliferation, and immune response, with significant involvement of the p53 and cAMP signaling pathways. Tissue-specific responses to heat stress were observed, with distinct regulatory roles in each organ, including GPCR activity and cytokine signaling in the hypothalamus, calcium-regulated exocytosis in the pituitary, and cilium assembly and ATP binding in the ovary. Key genes such as SYN3, RPH3A, and IGFBP2 were identified as central to the coordinated regulation of the HPO axis. These findings provide new insights into the molecular basis of heat stress-induced impairments in reproductive function—manifested by altered estrous behavior, reduced hormone secretion (FSH, LH, E₂, and P4), and disrupted gene expression in the hypothalamic–pituitary–ovarian (HPO) axis—and offer potential targets for improving heat tolerance and reproductive regulation in sheep. Full article

Accurate monitoring of animal behaviors enables improved management in precision livestock farming (PLF), supporting critical applications including health assessment, estrus detection, parturition monitoring, and feed intake estimation. Although both contact and non-contact sensing modalities are utilized, wearable devices with embedded sensors (e.g., accelerometers, pressure sensors) offer unique advantages through continuous data streams that enhance behavioral traceability. Focusing specifically on contact sensing techniques, this review examines sensor characteristics and data acquisition challenges, methodologies for processing behavioral data and implementing identification algorithms, industrial applications enabled by recognition outcomes, and prevailing challenges with emerging research opportunities. Current behavior classification relies predominantly on traditional machine learning or deep learning approaches with high-frequency data acquisition. The fundamental limitation restricting advancement in this field is the difficulty in maintaining high-fidelity recognition performance at reduced acquisition rates, particularly for integrated multi-behavior identification. Considering that the computational demands and limited adaptability to complex field environments remain significant constraints, Tiny Machine Learning (Tiny ML) could present opportunities to guide future research toward practical, scalable behavioral monitoring solutions. In addition, algorithm development for functional applications post behavior recognition may represent a critical future research direction. Full article

Reproductive efficiency is a critical determinant of productivity and profitability in sheep farming. Traditional selection methods have largely relied on phenotypic traits and historical reproductive records, which are often limited by subjectivity and delayed feedback. Recent advancements in artificial intelligence (AI), including video tracking, wearable sensors, and machine learning (ML) algorithms, offer new opportunities to identify behavior-based indicators linked to key reproductive traits such as estrus, lambing, and maternal behavior. This review synthesizes the current research on AI-powered behavioral monitoring tools and proposes a conceptual model, ReproBehaviorNet, that maps age- and sex-specific behaviors to biological processes and AI applications, supporting real-time decision-making in both intensive and semi-intensive systems. The integration of accelerometers, GPS systems, and computer vision models enables continuous, non-invasive monitoring, leading to earlier detection of reproductive events and greater breeding precision. However, the implementation of such technologies also presents challenges, including the need for high-quality data, a costly infrastructure, and technical expertise that may limit access for small-scale producers. Despite these barriers, AI-assisted behavioral phenotyping has the potential to improve genetic progress, animal welfare, and sustainability. Interdisciplinary collaboration and responsible innovation are essential to ensure the equitable and effective adoption of these technologies in diverse farming contexts. Full article