Image processing to assess the spatial variability of weeds in no-tillage

Authors

  • Darly Geraldo Sena Jr. Universidade Federal de Goiás
  • Marcelo Marques Costa Universidade Federal de Viçosa
  • Vilmar Antonio Ragagnin Universidade Federal de Goiás
  • Karolina Fernandes Costa Gobbi Universidade Federal de Goiás
  • Francisco de Assis de Carvalho Pinto Universidade Federal de Viçosa
  • Onilio Venâncio de Oliveira Neto Universidade Federal de Goiás

Abstract

The aim of this work was to describe the weed spatial variability in a no-tillage system area in Jataí, GO, Brazil. A regular grid was used on a 22 hectares field accomplishing 29 sample points. The total shoot dry matter of weeds was determined on an area of 0.5 m2 and also separated on broadleaf and grassy types. Images of the sample area were classified using a supervised classifier into three classes: straw, leaves and uncovered. The soybean leaves were manually segmented from the leave class. The images were also processed using an automatic threshold method, separating the leaves from the background. On the processed images were calculated the covered areas by each class. All variables were submitted to correlation and geostatistical analysis. A significant correlation was verified between covered area by plants and the shoot dry matter. The supervised classification and the automatic threshold method achieved similar results. When the soybean leaves were segmented, the broadleaf weeds cover area determination improved, but had no influence on the correlation with total dry matter of weeds and cover area Spatial dependence was only verified when the two types of weeds were studied separately.

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Published

2011-08-30

How to Cite

SENA JR., D.G., COSTA, M.M., RAGAGNIN, V.A., GOBBI, K.F.C., PINTO, F. de A. de C. and OLIVEIRA NETO, O.V. de, 2011. Image processing to assess the spatial variability of weeds in no-tillage . Bioscience Journal [online], vol. 27, no. 4, pp. 536–543. [Accessed29 November 2022]. Available from: https://seer.ufu.br/index.php/biosciencejournal/article/view/8098.

Issue

Section

Agricultural Sciences