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Predicting Sugarcane Biometric Parameters by UAV Multispectral Images and Machine Learning

Authors :
Romário Porto de Oliveira
Marcelo Rodrigues Barbosa Júnior
Antônio Alves Pinto
Jean Lucas Pereira Oliveira
Cristiano Zerbato
Carlos Eduardo Angeli Furlani
Source :
Agronomy, Vol 12, Iss 9, p 1992 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

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.

Details

Language :
English
ISSN :
20734395
Volume :
12
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Agronomy
Publication Type :
Academic Journal
Accession number :
edsdoj.1b4da8be3a104cb99a440447fc22e3c4
Document Type :
article
Full Text :
https://doi.org/10.3390/agronomy12091992