Study of monitoring biometric parameters and prediction productivity in sugarcane using drone-borne synthetic aperture radar

Author(s)

Gian Carlos Oré Huacles

• Document type: Master's Dissertation
• Press: Campinas, SP.
• Institution: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação
• Defense date: 2020-12-21

Examining board members

Hugo Enrique Hernández Figueroa; Inacio Henrique Yano; Lucas Heitzmann Gabrielli

• Advisor: Barbara Janet Teruel Mederos; Hugo Enrique Hernández Figueroa

Abstract

Currently, in Brazil, the production of sugarcane is one of the most important activities in the agricultural sector. Sugarcane crops are used to produce ethanol, sugar, bagasse, sugarcane liquor, dry yeast, among others. Due to the growing demand for sugarcane deliverables, crop management has become increasingly complicated, so new tools are needed to obtain optimal crop monitoring. Tools for monitoring sugarcane crops based on remote sensing, such as synthetic aperture radar (SAR) and optical systems, have been proposed in recent years. In this work, different methodologies for monitoring sugarcane crops based on SAR systems are developed. For this, a drone-borne SAR system operating in L, P and C bands was used to monitor a sugarcane crop located at FEAGRI - UNICAMP. Data obtained by the SAR system together with biometric measurements are used to develop methodologies for the estimation of biometric parameters such as biomass, height and growth in a sugarcane crop. In addition, a basic methodology for the prediction of harvesting date and productivity of sugarcane is proposed. The developed methodologies are based on processing techniques for radar data such as SAR imaging, interferometric SAR (InSAR) and differential interferometric SAR (DInSAR). In the case of estimating biometric parameters in sugarcane, a normalized root-mean-square error (RMSE) of less than 10 % is obtained. Additionally, the history of biomass was used for the development of prediction methodologies, which were tested in different sugarcane crops in the city of Iracemápolis, São Paulo, resulting in an average error of 8 days and 10.73 % for the prediction of harvesting date and productivity, respectively. (AU)

• FAPESP's process: 18/14690-2 - Drone-borne radar for sugar cane precision agriculture
• Grantee: Gian Carlos Oré Huacles
• Support Opportunities: Scholarships in Brazil - Master