UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane.

ABSTRACT: Predicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of full-scale production. °Brix and Purity can offer significant and reliable indicators of high-quality raw material for industrial processing for food and fuel. However, their analysis in a relevant laboratory can be costly, time-consuming, and not scalable. We, therefore, analyzed whether merging multispectral images and machine learning (ML) algorithms can develop a non-invasive, predictive framework to map canopy reflectance to °Brix and Purity. We acquired multispectral images data of a sugarcane-producing area via unmanned aerial vehicl

SUBMITTER: Barbosa Junior MR 

PROVIDER: S-EPMC9929953 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature