CoFly-WeedDB: A UAV image dataset for weed detection and species identification
Marios Krestenitis,
Emmanuel K. Raptis,
Athanasios Ch. Kapoutsis,
Konstantinos Ioannidis,
Elias B. Kosmatopoulos,
Stefanos Vrochidis,
Ioannis Kompatsiaris
Affiliations
Marios Krestenitis
Information Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, Greece; Corresponding author.
Emmanuel K. Raptis
Information Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, Greece; Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi 67100, Greece
Athanasios Ch. Kapoutsis
Information Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, Greece
Konstantinos Ioannidis
Information Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, Greece
Elias B. Kosmatopoulos
Information Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, Greece; Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi 67100, Greece
Stefanos Vrochidis
Information Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, Greece
Ioannis Kompatsiaris
Information Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, Greece
The CoFly-WeedDB contains 201 RGB images (∼436 MB) from the attached camera of DJI Phantom Pro 4 from a cotton field in Larissa, Greece during the first stages of plant growth. The 1280 × 720 RGB images were collected while the Unmanned Aerial Vehicle (UAV) was performing a coverage mission over the field's area. During the designed mission, the camera angle was adjusted to –87°, vertically with the field. The flight altitude and speed of the UAV were equal to 5 m and 3 m/s, respectively, aiming to provide a close and clear view of the weed instances. All images have been annotated by expert agronomists using the LabelMe annotation tool, providing the exact boundaries of 3 types of common weeds in this type of crop, namely (i) Johnson grass, (ii) Field bindweed, and (iii) Purslane. The dataset can be used alone and in combination with other datasets to develop AI-based methodologies for automatic weed segmentation and classification purposes.
