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.

Cattle’s individual identification plays a crucial role in effectively managing large farms. To enhance agricultural efficiency, promote the digital transformation of animal husbandry, and improve animal welfare, it is essential to employ advanced identification technologies capable of real-time monitoring of cattle individual. This paper introduces a novel network called Open Pose Mask R-CNN (OP-Mask R-CNN) for individual cattle identification, which combines Open Pose with the Mask R-CNN network. Three key strategies are presented to improve the identification of individual cattle. First, optimize the number of convolutional layers in the Mask R-CNN backbone network, i.e., ResNet101. Second, introduce an Open Pose-based bovine skeleton feature extraction method. Finally, construct a fusion mechanism that combines the attention module, the convolutional block attention module (CBAM), the open pose module, and the ResNet101. Experimental results demonstrate that our proposed method achieves a 5.6% increase in recognition accuracy and improves recognition speed compared to the original Mask R-CNN model. This work strikes a balance between accuracy and complexity, facilitating the development of a lightweight bovine individual recognition technique.

With the rapid advancements in computer vision, using deep learning for strawberry disease recognition has emerged as a new trend. However, traditional identification methods heavily rely on manual discernment, consuming valuable time and imposing significant financial losses on growers. To address these challenges, this paper presents BerryNet-Lite, a lightweight network designed for precise strawberry disease identification. First, a comprehensive dataset, encompassing various strawberry diseases at different maturity levels, is curated. Second, BerryNet-Lite is proposed, utilizing transfer learning to expedite convergence through pre-training on extensive datasets. Subsequently, we introduce expansion convolution into the receptive field expansion, promoting more robust feature extraction and ensuring accurate recognition. Furthermore, we adopt the efficient channel attention (ECA) as the attention mechanism module. Additionally, we incorporate a multilayer perceptron (MLP) module to enhance the generalization capability and better capture the abstract features. Finally, we present a novel classification head design approach which effectively combines the ECA and MLP modules. Experimental results demonstrate that BerryNet-Lite achieves an impressive accuracy of 99.45%. Compared to classic networks like ResNet34, VGG16, and AlexNet, BerryNet-Lite showcases superiority across metrics, including loss value, accuracy, precision, F1-score, and parameters. It holds significant promise for applications in strawberry disease identification.

With the rapid development of machine learning and cloud computing, deep learning methods based on big data have been widely applied in the assessment of lithium-ion battery health status. However, enhancing the accuracy and robustness of assessment models remains a challenge. This study introduces an innovative T-LSTM prediction network. Initially, a one-dimensional convolutional neural network (1DCNN) is employed to effectively extract local and global features from raw battery data, providing enriched inputs for subsequent networks. Subsequently, LSTM and transformer models are ingeniously combined to fully utilize their unique advantages in sequence modeling, further enhancing the accurate prediction of battery health status. Experiments were conducted using both proprietary and open-source datasets, and the results validated the accuracy and robustness of the proposed method. The experimental results on the proprietary dataset show that the T-LSTM-based estimation method exhibits excellent performance in various evaluation metrics, with MSE, RMSE, MAE, MAPE, and MAXE values of 0.43, 0.66, 0.53, 0.58, and 1.65, respectively. The performance improved by 30–50% compared to that of the other models. The method demonstrated superior performance in comparative experiments, offering novel insights for optimizing intelligent battery management and maintenance strategies.

In the context of cattle farm environments, intricate environmental interferences have presented challenges that impede seamless data acquisition. This paper introduces a novel approach, the integration of a fusion gate transformation mechanism and a variational autoencoder GAN, which we term GCT-VAE-GAN, aimed at enhancing low-light images from cattle farm settings. Initially, our approach involves the design of an encoding network tasked with augmenting the original images. Subsequently, we advance our methodology by formulating a generative network to effectively address the challenges of image diversification and poor image quality. Notably, the inclusion of an attention mechanism block within the FFN layer facilitates the fusion of these extracted features, resulting in the generation of high-quality images. Furthermore, to achieve proficient image discrimination, we implement a dual-discriminator structured discriminative network for the conclusive image discrimination task. The culmination of our approach involves the formulation of a comprehensive joint loss function, thereby constituting the core of the model’s loss module. Moreover, through comparative experiments, we aptly demonstrate the remarkable superiority of the GCT-VAE-GAN approach. The conducted experiments reveal the model’s consistent performance and resilience under varying illumination scenarios. The outcomes of our study underscore its significant relevance in elevating the quality of low-light images within cattle farm contexts. Furthermore, our approach exhibits the potential to enhance the efficacy of computer vision tasks, signifying a notable stride toward improved agricultural imaging techniques.

With the rapid development of machine learning and deep learning technology, cow face detection technology has achieved remarkable results. Traditional contact cattle identification methods are costly; are easy to lose and tamper with; and can lead to a series of security problems, such as untimely disease prevention and control, incorrect traceability of cattle products, and fraudulent insurance claims. In order to solve these problems, this study explores the application of cattle face detection technology in cattle individual detection to improve the accuracy of detection, an approach that is particularly important in smart animal husbandry and animal behavior analysis. In this paper, we propose a novel cow face detection network based on YOLOv7 improvement, named CFR-YOLO. First of all, the method of extracting the features of a cow’s face (including nose, eye corner, and mouth corner) is constructed. Then, we calculate the frame center of gravity and frame size based on these feature points to design the cow face detection CFR-YOLO network model. To optimize the performance of the model, the activation function of FReLU is used instead of the original SiLU activation function, and the CBS module is replaced by the CBF module. The RFB module is introduced in the backbone network; and in the head layer, the CBAM convolutional attention module is introduced. The performance of CFR-YOLO is compared with other mainstream deep learning models (including YOLOv7, YOLOv5, YOLOv4, and SSD) on a self-built cow face dataset. Experiments indicate that the CFR-YOLO model achieves 98.46% accuracy (precision), 97.21% recall (recall), and 96.27% average accuracy (mAP), proving its excellent performance in the field of cow face detection. In addition, comparative analyses with the other four methods show that CFR-YOLO exhibits faster convergence speed while ensuring the same detection accuracy; and its detection accuracy is higher under the condition of the same model convergence speed. These results will be helpful to further develop the cattle identification technique.