Affiliation:
1. Department of Agronomy, Federal University of Viçosa, Viçosa 36570-900, Brazil
2. Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture (ESALQ), University of São Paulo, Piracicaba 13418-900, Brazil
Abstract
In-field quality prediction in agricultural products is mainly based on near-infrared spectroscopy (NIR). However, initiatives applied to sugarcane quality are only observed under laboratory-controlled conditions. This study proposed a framework for NIR spectroscopy sensing to measure sugarcane quality during a real harvest operation. A platform was built to support the system composed of the NIR sensor and external lighting on the elevator of a sugarcane harvester. Real-time data were acquired in commercial fields. Georeferenced samples were collected for calibration, validation, and adjustment of the multivariate models by partial least squares (PLS) regression. In addition, subsamples of defibrated cane were NIR-acquired for the development of calibration transfer models by piecewise direct standardization (PDS). The method allowed the adjustment of the spectra collected in real time to predict the quality properties of soluble solids content (Brix), apparent sucrose in juice (Pol), fiber, cane Pol, and total recoverable sugar (TRS). The results of the relative mean square error of prediction (RRMSEP) were from 1.80 to 2.14%, and the ratio of interquartile performance (RPIQ) was from 1.79 to 2.46. The PLS-PDS models were applied to data acquired in real-time, allowing estimation of quality properties and identification of the existence of spatial variability in quality. The results showed that it is possible to monitor the spatial variability of quality properties in sugarcane in the field. Future studies with a broader range of quality attribute values and the evaluation of different configurations for sensing devices, calibration methods, and data processing are needed. The findings of this research will enable a valuable spatial information layer for the sugarcane industry, whether for agronomic decision-making, industrial operational planning, or financial management between sugar mills and suppliers.
Funder
São Paulo Research Foundation
Subject
Engineering (miscellaneous),Horticulture,Food Science,Agronomy and Crop Science
Reference56 articles.
1. Precision Agriculture and the Digital Contributions for Site-Specific Management of the Fields;Molin;Rev. Ciência Agronômica,2020
2. Monitoring Strategies for Quality Control of Agricultural Products Using Visible and Near-Infrared Spectroscopy: A Review;Blasco;Trends Food Sci. Technol.,2019
3. Sugarcane Harvester for In-Field Data Collection: State of the Art, Its Applicability and Future Perspectives;Canata;Sugar Tech.,2020
4. Lessons from Nearly 20 Years of Precision Agriculture Research, Development, and Adoption as a Guide to Its Appropriate Application;Bramley;Crop Pasture Sci.,2009
5. Predicting the Sugarcane Yield in Real-Time by Harvester Engine Parameters and Machine Learning Approaches;Maldaner;Comput. Electron. Agric.,2021