| Autor: |
Patrick Bezerra Fernandes, Camila Alves dos Santos, Antonio Leandro Chaves Gurgel, Lucas Ferreira Gonçalves, Natália Nogueira Fonseca, Rafaela Borges Moura, Kátia Aparecida de Pinho Costa, Tiago do Prado Paim |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
AgriEngineering, Vol 5, Iss 3, Pp 1614-1629 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2624-7402 |
| DOI: |
10.3390/agriengineering5030100 |
| Popis: |
The quantification of forage availability in tropical grasses is generally done in a destructive and time-consuming manner, involving cutting, weighing, and waiting for drying. To expedite this process, non-destructive methods can be used, such as unmanned aerial vehicles (UAVs) equipped with high-definition cameras, mobile device images, and the use of the normalized difference vegetation index (NDVI). However, these methods have been underutilized in tropical pastures. A literature review was conducted to present the current state of remote tools’ use in predicting forage availability and quality in tropical pastures. Few publications address the use of non-destructive methods to estimate forage availability in major tropical grasses (Megathyrsus maximus; Urochloa spp.). Additionally, these studies do not consider the fertility requirements of each cultivar and the effect of management on the phenotypic plasticity of tillers. To obtain accurate estimates of forage availability and properly manage pastures, it is necessary to integrate remote methods with in situ collection of soil parameters. This way, it will be possible to train machine learning models to obtain precise and reliable estimates of forage availability for domestic ruminant production. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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