| Autor: |
Romário Porto de Oliveira, Marcelo Rodrigues Barbosa Júnior, Antônio Alves Pinto, Jean Lucas Pereira Oliveira, Cristiano Zerbato, Carlos Eduardo Angeli Furlani |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Agronomy, Vol 12, Iss 9, p 1992 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2073-4395 |
| DOI: |
10.3390/agronomy12091992 |
| Popis: |
Multispectral sensors onboard unmanned aerial vehicles (UAV) have proven accurate and fast to predict sugarcane yield. However, challenges to a reliable approach still exist. In this study, we propose to predict sugarcane biometric parameters by using machine learning (ML) algorithms and multitemporal data through the analysis of multispectral images from UAV onboard sensors. The research was conducted on five varieties of sugarcane, as a way to make a robust approach. Multispectral images were collected every 40 days and the evaluated biometric parameters were: number of tillers (NT), plant height (PH), and stalk diameter (SD). Two ML models were used: multiple linear regression (MLR) and random forest (RF). The results showed that models for predicting sugarcane NT, PH, and SD using time series and ML algorithms had accurate and precise predictions. Blue, Green, and NIR spectral bands provided the best performance in predicting sugarcane biometric attributes. These findings expand the possibilities for using multispectral UAV imagery in predicting sugarcane yield, particularly by including biophysical parameters. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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