Precise identification of the individual cow can serve as one of the most fundamental prerequisites for the downstream tasks in smart animal husbandry, including precision feeding, health monitoring, and accurate estrus detection. The conventional contact identification—such as the electronic ear tags and sensory collars—has been widely adopted in recent years. Nevertheless, their application can be limited to the high maintenance costs, susceptibility to damage, and the stress response to animal welfare. Fortunately, the non-contact computer vision has emerged as a promising alternative. However, the current mainstream vision models have shared significant challenges in real-world breeding environments, such as the variable illumination in barns, diverse cow postures during movement, and the fine-grained nature of cow face features. It is often required to balance the high recognition accuracy and lightweight architecture. In this study, a lightweight cow face identification model (named LCFI-Net, Lightweight Cow Face Identification Network) was proposed using an improved DenseNet121 architecture. A three-stage structural optimization was carried out to balance between model performance and computational efficiency. Firstly, the backbone of the standard DenseNet121 was structurally pruned and then streamlined to create a DenseNet_Lite module. The redundant parameters were effectively removed to retain the essential feature extraction. Secondly, a Multi-Scale Attention Dense Layer (MSAD-Layer) was introduced to replace the standard dense blocks. A synergistic combination of the multi-scale feature fusion and attention mechanisms was enhanced to perceive the key fine-grained features—such as the specific facial patterns—against the complex, cluttered backgrounds. Thirdly, an Inverted Bottleneck Transition Layer (IBT-Layer) was utilized to further optimize the transmission of the information over the layers. Efficient dimensionality reduction and down-sampling were realized to preserve the integrity of the image feature information, thereby preventing the loss of critical details during the feature map compression. A high-quality dataset was collected from the visible light images of the cow faces in natural and complex breeding environments. The improved model was trained and then evaluated within a metric learning framework. Experimental results demonstrate that the superior performance of the architecture was achieved after optimization. On the test set, the LCFI-Net achieved a recognition accuracy of 93.54%, which was improved by 2.04 percentage points over the baseline DenseNet121 model. More significantly, the computational cost was substantially reduced in the LCFI-Net. Among them, the parameter of the LCFI-Net was only 1.02 M, which was reduced by 6.07 M, compared with the original DenseNet121. Furthermore, the comparative experiments were performed on the rest of the mainstream lightweight and heavy-duty models in order to validate the robustness of the LCFI-Net. The accuracy of the LCFI-Net was improved by 4.50, 4.46, 4.08, 2.75, and 2.29 percentage points, respectively, compared with the MobileNetv2, ShuffleNetv2, MobileFaceNet, ResNet50, and ResNet18. The LCFI-Net was introduced into the optimal structure for high accuracy and speed. In conclusion, the LCFI-Net was achieved in an optimal equilibrium between recognition precision and computational efficiency. Consequently, the finding can provide a robust technical foundation to deploy the high-precision cow identity recognition on the resource-constrained edge devices, such as the mobile inspection robots and intelligent barn equipment.
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Health knowledge of the cattle (both beef and dairy production) can represent one of the most important components in the intelligent and data-driven livestock farming. Multiple dimensions can be involved, such as the housing environment, disease prevention, breeding, nutrition, as well as the feed and water regulation, all of which are closely related to animal welfare and productivity in sustainable production. However, it is still lacking in high-quality Chinese textual resources in cattle health research. Particularly, the annotated corpora for the named entity recognition (NER) have limited the knowledge extraction, intelligent monitoring, and decision making in precision livestock farming. Compared with the general text, the NER of the cattle health data is characterized by a highly diverse entity type, complex and nested entity structure, uneven data distribution, and frequent occurrence of the domain-specific terminology. The general-purpose models, such as BERT, cannot fully meet the requirement of accurate entity recognition. It is often required for domain adaptation and high performance in order to identify the long-tail or low-frequency entities. In this study, a Chinese NER corpus was constructed for cattle health. The dataset also covered 17 entity categories, including diseases, drugs, feed, physiological indicators, operations, and environmental factors. A multi-feature fusion NER model was proposed using the Livestock Enhanced Representation for Text (LERT). At the representation layer, the LERT was employed as a pre-trained language model to enhance the Chinese semantic comprehension and effectively capture the long-range contextual dependencies specific to the cattle domain. At the feature extraction layer, a Bi-directional Long Short-Term Memory (BiLSTM) network and an Iterated Dilated Convolutional Neural Network (IDCNN) were utilized to integrate the global and local context during representation learning, where the BiLSTM was used for the long-range dependencies, while the IDCNN was used to efficiently extract the local features. Furthermore, a Scaled Dot-Product Multi-Head Attention mechanism was introduced at the feature fusion layer to strengthen the perception of the long-distance dependencies for the boundary and category identification, while a Conditional Random Field (CRF) layer was applied at the decoding stage to globally optimize the label sequences for the structural consistency of the outputs. Experimental evaluations demonstrated that the model achieved excellent performance on the corpus, with a precision of 90.45%, recall of 90.76%, and F1-score of 90.57%, outperforming baseline models, such as BERT and RoBERTa. All entity categories were achieved with a precision above 80%, indicating the strong and stable recognition. Ablation experiments verified that both the multi-head attention mechanism and the combination of BiLSTM with IDCNN contributed significantly to the feature fusion and overall performance. A high-precision and domain-adaptive approach can provide for the entity recognition of the Chinese NER resources in the field of cattle health. The valuable insights can also be offered for natural language processing in intelligent livestock farming.
Estrus monitoring and identification in cows is a crucial aspect of breeding management in beef and dairy cattle farming. Innovations in precise sensing and intelligent identification methods and technologies for estrus in cows are essential not only for scientific breeding, precise management, and smart breeding on a population level but also play a key supportive role in health management, productivity enhancement, and animal welfare improvement at the individual level. The aims are to provide a reference for scientific management and the study of modern production technologies in the beef and dairy cattle industry, as well as theoretical methodologies for the research and development of key technologies in precision livestock farming.
Based on describing the typical characteristics of normal and abnormal estrus in cows, this paper systematically categorizes and summarizes the recent research progress, development trends, and methodological approaches in estrus monitoring and identification technologies, focusing on the monitoring and diagnosis of key physiological signs and behavioral characteristics during the estrus period. Firstly, the paper outlines the digital monitoring technologies for three critical physiological parameters, body temperature, rumination, and activity levels, and their applications in cow estrus monitoring and identification. It analyzes the intrinsic reasons for performance bottlenecks in estrus monitoring models based on body temperature, compares the reliability issues faced by activity-based estrus monitoring, and addresses the difficulties in balancing model generalization and robustness design. Secondly, the paper examines the estrus sensing and identification technologies based on three typical behaviors: feeding, vocalization, and sexual desire. It highlights the latest applications of new artificial intelligence technologies, such as computer vision and deep learning, in estrus monitoring and points out the critical role of these technologies in improving the accuracy and timeliness of monitoring. Finally, the paper focuses on multifactor fusion technologies for estrus perception and identification, summarizing how different researchers combine various physiological and behavioral parameters using diverse monitoring devices and algorithms to enhance accuracy in estrus monitoring. It emphasizes that multi-factor fusion methods can improve detection rates and the precision of identification results, being more reliable and applicable than single-factor methods. The importance and potential of multi-modal information fusion in enhancing monitoring accuracy and adaptability are underlined. The current shortcomings of multi-factor information fusion methods are analyzed, such as the potential impact on animal welfare from parameter acquisition methods, the singularity of model algorithms used for representing multifactor information fusion, and inadequacies in research on multi-factor feature extraction models and estrus identification decision algorithms.
From the perspectives of system practicality, stability, environmental adaptability, cost-effectiveness, and ease of operation, several key issues are discussed that need to be addressed in the further research of precise sensing and intelligent identification technologies for cow estrus within the context of high-quality development in digital livestock farming. These include improving monitoring accuracy under weak estrus conditions, overcoming technical challenges of audio extraction and voiceprint construction amidst complex background noise, enhancing the adaptability of computer vision monitoring technologies, and establishing comprehensive monitoring and identification models through multi-modal information fusion. It specifically discusses the numerous challenges posed by these issues to current technological research and explains that future research needs to focus not only on improving the timeliness and accuracy of monitoring technologies but also on balancing system cost-effectiveness and ease of use to achieve a transition from the concept of smart farming to its practical implementation.
Most current agricultural robots lack the ability to adapt to complex agricultural environments and still have limitations when facing variable, uncertain and unstructured agricultural scenarios. With the acceleration of agricultural intelligent transformation, embodied intelligence, as an intelligent system integrating environment perception, information cognition, autonomous decision-making and action, is giving agricultural robots stronger autonomous perception and complex environment adaptation ability, and becoming an important direction to promote the development of agricultural intelligent robots. In this paper, the technical system and application practice of embodied intelligence are sorted out systematically in the field of agricultural robots, its important value is revealed in improving environmental adaptability, decision-making autonomy and operational flexibility, and theoretical and practical references are provided to promote the development of agricultural robots to a higher level.
Firstly, the key supporting technologies of embodied intelligent agricultural robots are systematically sorted out, focusing on four aspects, namely, multimodal fusion perception, intelligent autonomous decision-making, autonomous action control and feedback autonomous learning. In terms of multimodal fusion perception, the modular artificial intelligence (AI) algorithm architecture and multimodal large model architecture are summarised. In terms of intelligent autonomous decision-making, two types of approaches based on artificial programming and dedicated task algorithms, and on large-scale pre-trained models are outlined. In terms of autonomous action control, three types of approaches based on the fusion of reinforcement learning and mainstream transformer, large model-assisted reinforcement learning, end-to-end mapping of semantics to action and action end-to-end mapping are summarised. In the area of feedback autonomous learning, the focus is on the related technological advances in the evolution of large model-driven feedback modules. Secondly, it analysed the typical application scenarios of embodied intelligence in agriculture, constructed a technical framework with "embodied perception - embodied cognition - embodied execution - embodied evolution" as the core, and discussed the implementation paths of each module according to the agricultural scenarios. The paths of each module are classified and discussed. Finally, the key technical bottlenecks and application challenges are analysed in depth, mainly including the high complexity of system integration, the significant gap between real and virtual data, and the limited ability of cross-scene generalisation.
The future development trend of embodied intelligent agricultural robots is summarised and prospected from the construction of high-quality datasets and simulation platforms, the application of domain large model fusion, and the design of layered collaborative architectures, etc. It mainly focuses on the following aspects. Firstly, the construction of high-quality agricultural scenarios of embodied intelligence datasets is a key prerequisite to realise the embodied intelligence landing in agriculture. The development of embodied intelligent agricultural robots needs to rely on rich and accurate agricultural scene task datasets and highly realistic simulators to support physical interaction and behavioural learning. Secondly, the fusion of basic big model and agricultural domain model is the accelerator of intelligent perception and decision-making of agricultural robots. The in-depth fusion of general basic models in agricultural scenarios will bring stronger perception, understanding and reasoning capabilities to the embodied-intelligent agricultural robots. Thirdly, the "big model high-level planning + small model bottom-level control" architecture is an effective solution to balance intelligence and efficiency. Although large models have advantages in semantic understanding and global strategy planning, their reasoning latency and arithmetic demand can hardly meet the real-time and low-power requirements of agricultural robots. The use of large models for high-level task decomposition, scene semantic parsing and decision making, coupled with lightweight small models or traditional control algorithms to complete the underlying sensory response and motion control, can achieve the complementary advantages of the two.
The rapid development of artificial intelligence and automation has greatly expanded the scope of agricultural automation, with applications such as precision farming using unmanned machinery, robotic grazing in outdoor environments, and automated harvesting by orchard-picking robots. Collaborative operations among multiple agricultural robots enhance production efficiency and reduce labor costs, driving the development of smart agriculture. Multi-robot simultaneous localization and mapping (SLAM) plays a pivotal role by ensuring accurate mapping and localization, which are essential for the effective management of unmanned farms. Compared to single-robot SLAM, multi-robot systems offer several advantages, including higher localization accuracy, larger sensing ranges, faster response times, and improved real-time performance. These capabilities are particularly valuable for completing complex tasks efficiently. However, deploying multi-robot SLAM in agricultural settings presents significant challenges. Dynamic environmental factors, such as crop growth, changing weather patterns, and livestock movement, increase system uncertainty. Additionally, agricultural terrains vary from open fields to irregular greenhouses, requiring robots to adjust their localization and path-planning strategies based on environmental conditions. Communication constraints, such as unstable signals or limited transmission range, further complicate coordination between robots. These combined challenges make it difficult to implement multi-robot SLAM effectively in agricultural environments. To unlock the full potential of multi-robot SLAM in agriculture, it is essential to develop optimized solutions that address the specific technical demands of these scenarios.
Existing review studies on multi-robot SLAM mainly focus on a general technological perspective, summarizing trends in the development of multi-robot SLAM, the advantages and limitations of algorithms, universally applicable conditions, and core issues of key technologies. However, there is a lack of analysis specifically addressing multi-robot SLAM under the characteristics of complex agricultural scenarios. This study focuses on the main features and applications of multi-robot SLAM in complex agricultural scenarios. The study analyzes the advantages and limitations of multi-robot SLAM, as well as its applicability and application scenarios in agriculture, focusing on four key components: multi-sensor data fusion, collaborative localization, collaborative map building, and loopback detection. From the perspective of collaborative operations in multi-robot SLAM, the study outlines the classification of SLAM frameworks, including three main collaborative types: centralized, distributed, and hybrid. Based on this, the study summarizes the advantages and limitations of mainstream multi-robot SLAM frameworks, along with typical scenarios in robotic agricultural operations where they are applicable. Additionally, it discusses key issues faced by multi-robot SLAM in complex agricultural scenarios, such as low accuracy in mapping and localization during multi-sensor fusion, restricted communication environments during multi-robot collaborative operations, and low accuracy in relative pose estimation between robots.
To enhance the applicability and efficiency of multi-robot SLAM in complex agricultural scenarios, future research needs to focus on solving these critical technological issues. Firstly, the development of enhanced data fusion algorithms will facilitate improved integration of sensor information, leading to greater accuracy and robustness of the system. Secondly, the combination of deep learning and reinforcement learning techniques is expected to empower robots to better interpret environmental patterns, adapt to dynamic changes, and make more effective real-time decisions. Thirdly, large language models will enhance human-robot interaction by enabling natural language commands, improving collaborative operations. Finally, the integration of digital twin technology will support more intelligent path planning and decision-making processes, especially in unmanned farms and livestock management systems. The convergence of digital twin technology with SLAM is projected to yield innovative solutions for intelligent perception and is likely to play a transformative role in the realm of agricultural automation. This synergy is anticipated to revolutionize the approach to agricultural tasks, enhancing their efficiency and reducing the reliance on labor.
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