Editorial: Technological strategies to improve animal health and production

Hernández-Patlán, Daniel; Téllez–Isaías, Guillermo; Hernández-Velasco, Xóchitl; Solís-Cruz, Bruno

doi:10.3389/fvets.2023.1206170

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…Notably, these systems contribute to heightened productivity while concurrently reducing labor costs. The integration of data analysis and machine learning plays a pivotal role in refining feed composition, ultimately boosting feed income and mitigating adverse environmental impacts [28,29].…”

Section: Smart Agriculture In Animal Productionmentioning

confidence: 99%

Enhancing Animal Production through Smart Agriculture: Possibilities, Hurdles, Resolutions, and Advantages

Dayoub,

Shnaigat,

Tarawneh

et al. 2024

Ruminants

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Smart livestock farming utilizes technology to enhance production and meet food demand sustainably. This study employs surveys and case studies to gather data and information, subsequently analyzing it to identify opportunities and challenges. The proposed solutions encompass remote sensing, technology integration, farmer education, and stakeholder engagement. The research delves into smart technologies in animal production, addressing opportunities, challenges, and potential solutions. Smart agriculture employs modern technology to improve efficiency, sustainability, and animal welfare in livestock farming. This includes remote monitoring, GPS-based animal care, robotic milking, smart health collars, predictive disease control, and other innovations. Despite the great promise of smart animal production, there are existing challenges such as cost, data management, and connectivity. To overcome these challenges, potential solutions involve remote sensing, technology integration, and farmer education. Smart agriculture provides opportunities for increased efficiency, improved animal welfare, and enhanced environmental conservation. A well-planned approach is crucial to maximize the benefits of smart livestock production while ensuring its long-term sustainability. This study confirms the growing adoption of smart agriculture in livestock production, with the potential to support the sustainable development goals and deliver benefits such as increased productivity and resource efficiency. To fully realize these benefits and ensure the sustainability of livestock farming, addressing cost and education challenges is essential. Therefore, this study recommends promoting a positive outlook among livestock stakeholders and embracing smart agriculture to enhance farm performance.

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Section: Smart Agriculture In Animal Productionmentioning

confidence: 99%

Enhancing Animal Production through Smart Agriculture: Possibilities, Hurdles, Resolutions, and Advantages

Dayoub,

Shnaigat,

Tarawneh

et al. 2024

Ruminants

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“…The importance of each neuron can be obtained through 1 e t * ⁄ . Equation (3) shows that the lower the energy e t * , the more distinct the target neuron t is from its surrounding neurons, indicating its higher importance for visual processing. The entire process can be represented as:…”

Section: Simam Attention Mechanismmentioning

confidence: 99%

“…Pose estimation refers to accurately locating the keypoints of a target instance and connecting them according to a predefined logic to reconstruct a complete skeletal structure. In animal husbandry, the health of animals directly impacts production efficiency [3]. Accurate animal pose estimation is a crucial component of behavior recognition and lameness detection, such as poultry behavior recognition [4] and lameness detection in cattle [5].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Cattle Pose Estimation Based on Improved RTMPose

Li,

Sun,

Fan

et al. 2023

Agriculture

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Accurate cattle pose estimation is essential for Precision Livestock Farming (PLF). Computer vision-based, non-contact cattle pose estimation technology can be applied for behaviour recognition and lameness detection. Existing methods still face challenges in achieving fast cattle pose estimation in complex scenarios. In this work, we introduce the FasterNest Block and Depth Block to enhance the performance of cattle pose estimation based on the RTMPose model. First, the accuracy of cattle pose estimation relies on the capture of high-level image features. The FasterNest Block, with its three-branch structure, effectively utilizes high-level feature map information, significantly improving accuracy without a significant decrease in inference speed. Second, large kernel convolutions can increase the computation cost of the model. Therefore, the Depth Block adopts a method based on depthwise separable convolutions to replace large kernel convolutions. This addresses the insensitivity to semantic information while reducing the model’s parameter. Additionally, the SimAM module enhances the model’s spatial learning capabilities without introducing extra parameters. We conducted tests on various datasets, including our collected complex scene dataset (cattle dataset) and the AP-10K public dataset. The results demonstrate that our model achieves the best average accuracy with the lowest model parameters and computational requirements, achieving 82.9% on the cattle test set and 72.0% on the AP-10K test set. Furthermore, in conjunction with the object detection model RTMDet-m, our model reaches a remarkable inference speed of 39FPS on an NVIDIA GTX 2080Ti GPU using the PyTorch framework, making it the fastest among all models. This work provides adequate technical support for fast and accurate cattle pose estimation in complex farm environments.

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“…Genetic selection and breeding programmes enhance improvements in animal health and output [ 3 ]. Genomic selection has been developing rapidly in recent decades, which is an ensemble of methods to estimate the breeding values of individual animals on the basis of genome-wide single-nucleotide polymorphism genotype information [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Udder, Claw, and Reproductive Health in Genomic Selection of the Czech Holstein

Krupová,

Kašná,

Zavadilová

et al. 2024

Animals

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The aim of this study was to construct an enhanced selection index using the genomic and economic parameters of new health disorders and current production and functional traits. Genomic evaluation for the incidence of clinical mastitis (CM), three claw disease traits, retained placenta (RET), metritis (MET), and cystic ovaries (CYS) was performed using linear animal models based on producer-recorded data. Good correlations among the health disorders were found, and their heritability estimates did not exceed 7%. Economic weights (EWs) for the health disorders were EUR −132.10 for CM, EUR −128.87 for overall claw diseases, EUR −52.10 for RET, EUR −80.48 for MET, and EUR −16.16 for CYS. These EWs indicate changes in the present value of the annual profit per cow when increasing the incidence of the traits by one case per cow year. Selection using the enhanced index resulted in favourable responses for most of the new health disorders (e.g., −0.001 and −0.006 cases of RET and MET per cow year, respectively), and also in the current breeding objective traits (+49 kg of milk, −0.02% of calf losses). An index contribution of 7% for the new health disorders was assessed as acceptable for the breeders.

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Editorial: Technological strategies to improve animal health and production

Cited by 9 publications

References 31 publications

Enhancing Animal Production through Smart Agriculture: Possibilities, Hurdles, Resolutions, and Advantages

Enhancing Animal Production through Smart Agriculture: Possibilities, Hurdles, Resolutions, and Advantages

Real-Time Cattle Pose Estimation Based on Improved RTMPose

Udder, Claw, and Reproductive Health in Genomic Selection of the Czech Holstein

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