Spectral differentiation of sugarcane from weeds

• Published in: Biosystems engineering 2020-02, Vol.190, p.41-46
• Main Authors: Souza, Micael Felipe de, Amaral, Lucas Rios do, Oliveira, Stanley Robson de Medeiros, Coutinho, Marcos Antonio Neris, Ferreira Netto, Camila
• Format: Article
• Language: English
• Subjects: Classification models; Post-emergent herbicides; Precision agriculture; Site-specific management
• ISSN: 1537-5110; 1537-5129
• DOI: 10.1016/j.biosystemseng.2019.11.023

Site-specific application of herbicides is highly desirable for optimising its usage and reducing environmental damages. Thus, developing techniques for identification and mapping of weeds is necessary for a proper precision agriculture adoption. Such weed identification for site-specific management is difficult when the main crop is already established in the field. This study shows the possibility of differentiating sugarcane plants from weeds by the spectral behaviour of the leaves. The performance of two modelling methods, SIMCA (soft independent modelling by class analogy) and the RF algorithm (random forest) was compared. The simplification of the Vis-NIR spectrum into only four bands of interest (500–550 nm; 650–750 nm; 1300–1450 nm; and 1800–1900 nm) was verified by demonstrating they had the same differentiation ability as the full visible-near infra-red spectrum. Thus, it was shown that performing the proper band selection and local calibration using a spectral classification approach may allow weed mapping and facilitate localised herbicide application. •Crop differentiation from weeds using Vis-NIR spectroscopy is feasible.•Few spectral bands allow proper plant differentiation.•A few-band sensor can be developed to assist in site-specific weed management.