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(Reference retrieved automatically from SciELO through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

CLASSIFICATION OF THE OCCURRENCE OF BROADLEAF WEEDS IN NARROW-LEAF CROPS

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Author(s):
Cenneya L. Martins [1] ; Agda L. G. Oliveira [2] ; Isabella A. da Cunha [3] ; Henrique Oldoni [4] ; Juliana C. Pereira [5] ; Lucas R. do Amaral [6]
Total Authors: 6
Affiliation:
[1] Universidade Estadual de Campinas. School of Agricultural Engineering - Brasil
[2] Universidade Estadual de Campinas. School of Agricultural Engineering - Brasil
[3] Universidade Estadual de Campinas. School of Agricultural Engineering - Brasil
[4] Universidade Estadual de Campinas. Interdisciplinary Center of Energy Planning - Brasil
[5] Universidade Estadual de Campinas. School of Agricultural Engineering - Brasil
[6] Universidade Estadual de Campinas. School of Agricultural Engineering - Brasil
Total Affiliations: 6
Document type: Journal article
Source: Engenharia Agrícola; v. 44, 2024-04-19.
Abstract

ABSTRACT Considering the spectral differences between broadleaf weeds and narrow-leaf crops and the influence of terrain and soil variables on weed infestations, integrating such information into a machine-learning algorithm can lead to accurate weed maps. Therefore, we aim to evaluate the effectiveness of these variables in classifying the occurrence of broadleaf weeds in narrow-leaf crops. Weed data was collected at georeferenced points across two areas covering 200 ha (pasture) and 106 ha (sorghum), creating classes 0 (absence) and 1 (presence). For each sample point, we obtained 11 variables: soil clay content, cation exchange capacity, soil organic matter, terrain elevation, slope, NDVI, EVI, CIgreen, BGND (derived from PlanetScope images), and spatial information (X and Y coordinates). These variables were used as predictors of broadleaf weed presence and absence in the Random Forest classification algorithm. The presence and absence of broadleaf weeds were correctly classified in 84% and 74% of all predictions in the test sample sets for pasture and sorghum areas, respectively. This strategy represents an efficient way to map and manage the occurrence of broadleaf weeds in narrow-leaf crops. (AU)

FAPESP's process: 17/50205-9 - Monitoring integrated crop-livestock systems through remote sensing and precision agriculture for more sustainable production - towards low carbon agriculture
Grantee:Paulo Sergio Graziano Magalhães
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 20/02223-0 - Precision agriculture in integrated crop-livestock systems
Grantee:Henrique Oldoni
Support Opportunities: Scholarships in Brazil - Post-Doctoral