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Please use this identifier to cite or link to this item:
http://hdl.handle.net/10174/42535
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| Title: | Sensor-Based Pasture Quality Monitoring: Supporting Grazing Management and Preventing Nutritional and Metabolic Disorders in Ruminants |
| Authors: | Pinto, Henrique
Santos, Ricardo
Defalque, Guilherme
Moral, Francisco
Serrano, João |
| Editors: | Liu, Yufei |
| Keywords: | precision animal nutrition
biomass estimation
machine learning
sward heterogeneity
decision support systems
precision livestock farming |
| Issue Date: | Aug-2026 |
| Publisher: | MDPI |
| Citation: | Pinto, H.; Santos, R.; Defalque, G.; Moral, F. Serrano, J. (2026). Sensor-Cased Pasture Quality Monitoring: Supporting Grazing Management and Preventing Nutritional and Metabolic Disorders in Ruminants. Sensors, 26, 5472.
https://doi.org/10.3390/s26175472. |
| Abstract: | Pasture quality monitoring is essential for optimizing grazing management and reducing the incidence of nutritional and metabolic disorders in ruminants, yet conventional
field-based measurements remain labor-intensive and limited in spatial coverage. This review examines how remote sensing (RS) technologies can support pasture-based livestock systems by providing timely, scalable assessments of biomass, botanical composition, and nutritive attributes. Data from multispectral, hyperspectral, radio detection and ranging (RADAR), and light detection and ranging (LiDAR) sensors, acquired via satellite, unmanned aerial vehicle (UAV), and proximal platforms, are combined with machine learning (ML) methods and radiative transfer models to derive pasture biophysical and quality indicators. The reviewed evidence shows that RS reliably estimates pasture biomass and structural traits, while advances in spectral unmixing, data fusion, and artificial intelligence (AI) improve the characterization of heterogeneous swards and support emerging indicators related to forage quality. Integrating these remotely sensed metrics into grassland decision-support frameworks can enhance grazing allocation, inform fertilization and irrigation decisions, and help detect conditions associated with nutritional imbalances.
Overall, the synthesis demonstrates that RS, particularly when combined with advanced
modelling and cloud-based processing, offers a robust pathway for improving pasture monitoring and strengthening the nutritional management of ruminants, thereby supporting
more sustainable and animal welfare-focused grazing systems. |
| URI: | http://hdl.handle.net/10174/42535 |
| Type: | article |
| Appears in Collections: | MED - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica
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MED - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica /
