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

• Published in: Frontiers in plant science Vol. 14; p. 1114852
• Main Authors: Barbosa Júnior, Marcelo Rodrigues, Moreira, Bruno Rafael de Almeida, de Oliveira, Romário Porto, Shiratsuchi, Luciano Shozo, da Silva, Rouverson Pereira
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 26.01.2023
• Subjects: brix; Plant Science; remote sensing; ripening; Saccharum spp; smart harvest; sucrose; brix; Saccharum spp; remote sensing; sucrose; ripening; smart harvest
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2023.1114852

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 unmanned aerial vehicle (UAV) while determining °Brix and analytical Purity from juice in a routine laboratory. We then tested a suite of ML algorithms, namely multiple linear regression (MLR), random forest (RF), decision tree (DT), and support vector machine (SVM) for adequacy and complexity in predicting °Brix and Purity upon single spectral bands, vegetation indices (VIs), and growing degree days (GDD). We obtained evidence for biophysical functions accurately predicting °Brix and Purity. Those can bring at least 80% of adequacy to the modeling. Therefore, our study represents progress in assessing and monitoring sugarcane on an industrial scale. Our insights can offer stakeholders possibilities to develop prescriptive harvesting and resource-effective, high-performance manufacturing lines for by-products.