CLASSIFICATION OF THE OCCURRENCE OF BROADLEAF WEEDS IN NARROW-LEAF CROPS
| Autor: | Cenneya L. Martins, Agda L. G. Oliveira, Isabella A. da Cunha, Henrique Oldoni, Juliana C. Pereira, Lucas R. do Amaral |
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| Jazyk: | English<br />Spanish; Castilian<br />Portuguese |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Engenharia Agrícola, Vol 44 (2024) |
| Druh dokumentu: | article |
| ISSN: | 0100-6916 1809-4430 |
| DOI: | 10.1590/1809-4430-eng.agric.v44e20230148/2024 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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