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 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.

Precise Estimation of Sugarcane Yield at Field Scale with Allometric Variables Retrieved from UAV Phantom 4 RTK Images

“…Several studies have shown that sugarcane yield parameters such as tiller number, plant height, and stalks could be efficiently estimated using UAV LIDAR and UAV multispectral image data for accurate plot scale sugarcane yield estimation [7][8][9]. Akbarian et al [10] and Barbosa-Júnior et al [11] examined the combination of multispectral UAV imagery with several machine learning methods for plot-scale sugarcane yields estimation and showed that the random forest algorithm had the best accuracy for the estimation. Though these studies demonstrated the possibility of accurate plot-scale estimation of sugarcane yield and production, problems such as workload reduction, lossless sampling, and fast and accurate estimation are still remained as challenges which required effective solutions [6,10].…”