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Sudars K, Jasko J, Namatevs I, et al. Dataset of annotated food crops and weed images for robotic computer vision control. Data Brief. 2020;31:105833doi: 10.1016/j.dib.2020.105833.
Sudars, K., Jasko, J., Namatevs, I., Ozola, L., & Badaukis, N. (2020). Dataset of annotated food crops and weed images for robotic computer vision control. Data in brief, 31105833. https://doi.org/10.1016/j.dib.2020.105833
Sudars, Kaspars, et al. "Dataset of annotated food crops and weed images for robotic computer vision control." Data in brief vol. 31 (2020): 105833. doi: https://doi.org/10.1016/j.dib.2020.105833
Sudars K, Jasko J, Namatevs I, Ozola L, Badaukis N. Dataset of annotated food crops and weed images for robotic computer vision control. Data Brief. 2020 Jun 11;31:105833. doi: 10.1016/j.dib.2020.105833. eCollection 2020 Aug. PMID: 32577458; PMCID: PMC7305380.
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Data Brief. 2020 Jun 11;31:105833. doi: 10.1016/j.dib.2020.105833. eCollection 2020 Aug.

Dataset of annotated food crops and weed images for robotic computer vision control.

Data in brief

Kaspars Sudars, Janis Jasko, Ivars Namatevs, Liva Ozola, Niks Badaukis

Affiliations

  1. Institute of Electronics and Computer Science, Dz?rbenes str.14, Riga LV-1006, Latvia.
  2. Institute for Plant Protection Research `Agrihorts', Latvia University of Life Sciences and Technologies, P. Leji?a str. 2, LV-3004 Jelgava, Latvia.

PMID: 32577458 PMCID: PMC7305380 DOI: 10.1016/j.dib.2020.105833

Abstract

Weed management technologies that can identify weeds and distinguish them from crops are in need of artificial intelligence solutions based on a computer vision approach, to enable the development of precisely targeted and autonomous robotic weed management systems. A prerequisite of such systems is to create robust and reliable object detection that can unambiguously distinguish weed from food crops. One of the essential steps towards precision agriculture is using annotated images to train convolutional neural networks to distinguish weed from food crops, which can be later followed using mechanical weed removal or selected spraying of herbicides. In this data paper, we propose an open-access dataset with manually annotated images for weed detection. The dataset is composed of 1118 images in which 6 food crops and 8 weed species are identified, altogether 7853 annotations were made in total. Three RGB digital cameras were used for image capturing: Intel RealSense D435, Canon EOS 800D, and Sony W800. The images were taken on food crops and weeds grown in controlled environment and field conditions at different growth stages.

© 2020 The Author(s).

Keywords: Computer vision; Crop growth and development; Image annotation; Object detection; Precision agriculture

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Sensors (Basel). 2018 Aug 14;18(8): - PubMed
  2. Front Plant Sci. 2019 Jul 23;10:941 - PubMed
  3. Sensors (Basel). 2020 May 10;20(9): - PubMed

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