Using sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content

Author(s)

Fernandes, Marcia Helena Machado da Rocha ; Fernandes Jr, Jalme de Souza ; Adams, Jordan Melissa ; Lee, Mingyung ; Reis, Ricardo Andrade ; Tedeschi, Luis Orlindo

• Document type: Journal article
• Source: SCIENTIFIC REPORTS; v. 14, n. 1, p. 14-pg., 2024-04-15.

Abstract

Grasslands cover approximately 24% of the Earth's surface and are the main feed source for cattle and other ruminants. Sustainable and efficient grazing systems require regular monitoring of the quantity and nutritive value of pastures. This study demonstrates the potential of estimating pasture leaf forage mass (FM), crude protein (CP) and fiber content of tropical pastures using Sentinel-2 satellite images and machine learning algorithms. Field datasets and satellite images were assessed from an experimental area of Marandu palisade grass (Urochloa brizantha sny. Brachiaria brizantha) pastures, with or without nitrogen fertilization, and managed under continuous stocking during the pasture growing season from 2016 to 2020. Models based on support vector regression (SVR) and random forest (RF) machine-learning algorithms were developed using meteorological data, spectral reflectance, and vegetation indices (VI) as input features. In general, SVR slightly outperformed the RF models. The best predictive models to estimate FM were those with VI combined with meteorological data. For CP and fiber content, the best predictions were achieved using a combination of spectral bands and meteorological data, resulting in R2 of 0.66 and 0.57, and RMSPE of 0.03 and 0.04 g/g dry matter. Our results have promising potential to improve precision feeding technologies and decision support tools for efficient grazing management. (AU)

• FAPESP's process: 15/16631-5 - Management strategies to reduce environmental impacts of beef cattle production systems
• Grantee: Ricardo Andrade Reis
• Support Opportunities: Research Program on Global Climate Change - Thematic Grants
• FAPESP's process: 17/18750-7 - Impact of sustainable intensification of beef production in tropical areas on climate change mitigation
• Grantee: Márcia Helena Machado da Rocha Fernandes
• Support Opportunities: Regular Research Grants
• FAPESP's process: 20/14367-7 - Decision support system for precision livestock: prediction of forage mass and nutritional attributes using machine learning algorithms
• Grantee: Márcia Helena Machado da Rocha Fernandes
• Support Opportunities: Scholarships abroad - Research