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Background <p>Pastures are the main feed source for cattle in Brazil, and pasture-based production systems require frequent measurements and monitoring of forage productivity and quality to optimize animal performance. In this context, this study aimed to develop and evaluate machine learning models for the quantitative estimation of dry matter (DM), crude protein (CP), and neutral detergent fiber (NDF) in tropical pastures using multispectral and photogrammetric data acquired by an airborne sensor mounted on a remotely piloted aircraft (RPA), as well as to generate maps of the spatiotemporal variability of these parameters.</p> Methods <p>The assessment was conducted in a commercial pasture area of approximately 200 hectares, divided into 19 paddocks, cultivated with Urochloa brizantha cv. Marandu and managed under rotational grazing during the year 2023. A total of 190 field samples were collected and associated with multispectral reflectance data, vegetation indices, digital surface models, and pasture management information. Regression models based on random forest (RF), combined with recursive feature elimination (RFE), were developed to estimate forage productivity and nutritional attributes.</p> Results <p>The general DM prediction model showed good performance (R² = 0.74), indicating strong potential for estimating pasture productivity. Moderate and low predictive performances were obtained for NDF (R² = 0.56) and CP (R² = 0.48), respectively. External validation revealed reduced model transferability (R² &lt; 0.2), highlighting the influence of temporal variability and the intrinsic complexity of forage nutritional parameters. Spatial analyses revealed pronounced spatiotemporal variability in DM, CP, and NDF across the paddocks.</p> Conclusions <p>Overall, the results demonstrate that airborne multispectral sensing combined with machine learning is a promising approach for large-scale monitoring of pasture productivity and quality, supporting decision-making in pasture-based livestock systems.</p>

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Airborne multispectral sensing and machine learning for large-scale assessment of forage biomass and nutritive value in tropical pastures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Matheus Luís Caron,
  • Carlos Augusto Alves Cardoso Silva,
  • Rodnei Rizzo,
  • Ana Karla da Silva Oliveira,
  • Carlos Guilherme Silveira Pedreira,
  • Bin Yang,
  • Peterson Ricardo Fiorio

摘要

Background

Pastures are the main feed source for cattle in Brazil, and pasture-based production systems require frequent measurements and monitoring of forage productivity and quality to optimize animal performance. In this context, this study aimed to develop and evaluate machine learning models for the quantitative estimation of dry matter (DM), crude protein (CP), and neutral detergent fiber (NDF) in tropical pastures using multispectral and photogrammetric data acquired by an airborne sensor mounted on a remotely piloted aircraft (RPA), as well as to generate maps of the spatiotemporal variability of these parameters.

Methods

The assessment was conducted in a commercial pasture area of approximately 200 hectares, divided into 19 paddocks, cultivated with Urochloa brizantha cv. Marandu and managed under rotational grazing during the year 2023. A total of 190 field samples were collected and associated with multispectral reflectance data, vegetation indices, digital surface models, and pasture management information. Regression models based on random forest (RF), combined with recursive feature elimination (RFE), were developed to estimate forage productivity and nutritional attributes.

Results

The general DM prediction model showed good performance (R² = 0.74), indicating strong potential for estimating pasture productivity. Moderate and low predictive performances were obtained for NDF (R² = 0.56) and CP (R² = 0.48), respectively. External validation revealed reduced model transferability (R² < 0.2), highlighting the influence of temporal variability and the intrinsic complexity of forage nutritional parameters. Spatial analyses revealed pronounced spatiotemporal variability in DM, CP, and NDF across the paddocks.

Conclusions

Overall, the results demonstrate that airborne multispectral sensing combined with machine learning is a promising approach for large-scale monitoring of pasture productivity and quality, supporting decision-making in pasture-based livestock systems.