The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 551–555, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-551-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 551–555, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-551-2020

  06 Nov 2020

06 Nov 2020

DEEP CONVOLUTIONAL NEURAL NETWORKS FOR WEED DETECTION IN AGRICULTURAL CROPS USING OPTICAL AERIAL IMAGES

W. Ramirez, P. Achanccaray, L. F. Mendoza, and M. A. C. Pacheco W. Ramirez et al.
  • Applied Computational Intelligence Lab, Pontifical Catholic University of Rio de Janeiro, Brazil

Keywords: Semantic Segmentation, Remote Sensing, Deep Neural Network, Precision Farming

Abstract. The presence of weeds in agricultural crops has been one of the problems of greatest interest in recent years as they consume natural resources and negatively affect the agricultural process. For this purpose, a model has been implemented to segment weed in aerial images. The proposed model relies on DeepLabv3 architecture trained upon patches extracted from high-resolution aerial imagery. The dataset employed consisted in 5 high-resolution images that describes a sugar beet agricultural field in Germany. SegNet and U-Net architectures were selected for comparison purposes. Our results demonstrate that balancing of data, together with a greater spatial context leads better results with DeepLabv3 achieving up to 0.89 and 0.81 in terms of AUC and F1-score, respectively.