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Supporting rotational grazing systems with virtual fencing: paddock transitions, beef heifer performance, and stress response.
Animal
December 2024
Department of Crop Sciences, Grassland Science, Georg-August-University Göttingen, Von-Siebold-Strasse 8, 37075 Göttingen, Germany; Centre for Biodiversity and Sustainable Land Use, Büsgenweg 1, 37075 Göttingen, Germany.
Animal welfare is integral to sustainable livestock production, and pasture access for cattle is known to enhance welfare. Despite positive welfare impacts, high labour requirements hinder the adoption of sustainable grazing practices such as rotational stocking management. Virtual fencing (VF) is an innovative technology for simplified, less laborious grazing management and remote animal monitoring, potentially facilitating the expansion of sustainable livestock production.
View Article and Find Full Text PDFLow-Complexity Timing Correction Methods for Heart Rate Estimation Using Remote Photoplethysmography.
Sensors (Basel)
January 2025
Department of Biomedical and Robotics Engineering, Incheon National University, Incheon 22012, Republic of Korea.
With the rise of modern healthcare monitoring, heart rate (HR) estimation using remote photoplethysmography (rPPG) has gained attention for its non-contact, continuous tracking capabilities. However, most HR estimation methods rely on stable, fixed sampling intervals, while practical image capture often involves irregular frame rates and missing data, leading to inaccuracies in HR measurements. This study addresses these issues by introducing low-complexity timing correction methods, including linear, cubic, and filter interpolation, to improve HR estimation from rPPG signals under conditions of irregular sampling and data loss.
View Article and Find Full Text PDFIntegrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US.
Sensors (Basel)
January 2025
United States Department of Agriculture-Agriculture Research Service, Grassland Soil and Water Research Laboratory, Temple, TX 76502, USA.
Efficient and reliable corn ( L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this study targets a production-scale area, better representing real-world agricultural conditions and offering more practical relevance for farmers.
View Article and Find Full Text PDFAll-sky 1 km land surface temperature (LST) data are urgently needed. Two widely applied approaches to derive such LST data are merging thermal infrared remote sensing (TIR)-passive microwave remote sensing (PMW) observations and merging TIR reanalysis data. However, as only the Moderate Resolution Imaging Spectroradiometer (MODIS) is adopted as the TIR source for merging, current 1 km all-sky LST products are limited to the MODIS observation time.
View Article and Find Full Text PDFA Review of CNN Applications in Smart Agriculture Using Multimodal Data.
Sensors (Basel)
January 2025
Institut de Recherche en Informatique de Toulouse, IRIT UMR5505 CNRS, 31400 Toulouse, France.
This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency.
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