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

Autor(es)

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]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: Engenharia Agrícola; v. 44, 2024-04-19.

Resumo

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)

• Processo FAPESP: 17/50205-9 - Monitoramento de sistemas integrados lavoura-pecuária por meio de sensoriamento remoto e agricultura de precisão para uma produção mais sustentável - rumo à agricultura de baixo carbono
• Beneficiário: Paulo Sergio Graziano Magalhães
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 20/02223-0 - Agricultura de precisão em sistemas intensificados lavoura-pasto
• Beneficiário: Henrique Oldoni
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado