CoFly-WeedDB: A UAV image dataset for weed detection and species identification
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235234092200782X |
| _version_ | 1811190105599115264 |
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| author | Marios Krestenitis Emmanuel K. Raptis Athanasios Ch. Kapoutsis Konstantinos Ioannidis Elias B. Kosmatopoulos Stefanos Vrochidis Ioannis Kompatsiaris |
| author_facet | Marios Krestenitis Emmanuel K. Raptis Athanasios Ch. Kapoutsis Konstantinos Ioannidis Elias B. Kosmatopoulos Stefanos Vrochidis Ioannis Kompatsiaris |
| author_sort | Marios Krestenitis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T14:44:46Z |
| format | Article |
| id | doaj.art-30bc8db26b0944e387e89a519b9a6d20 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-04-11T14:44:46Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-30bc8db26b0944e387e89a519b9a6d202022-12-22T04:17:40ZengElsevierData in Brief2352-34092022-12-0145108575CoFly-WeedDB: A UAV image dataset for weed detection and species identificationMarios Krestenitis0Emmanuel K. Raptis1Athanasios Ch. Kapoutsis2Konstantinos Ioannidis3Elias B. Kosmatopoulos4Stefanos Vrochidis5Ioannis Kompatsiaris6Information Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, Greece; Corresponding author.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, GreeceInformation Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, GreeceInformation Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, GreeceInformation Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, Greece; Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi 67100, GreeceInformation Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, GreeceInformation Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, GreeceThe 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.http://www.sciencedirect.com/science/article/pii/S235234092200782XPrecision agricultureUAV datasetWeed detectionDeep convolutional neural networksSemantic segmentation |
| spellingShingle | Marios Krestenitis Emmanuel K. Raptis Athanasios Ch. Kapoutsis Konstantinos Ioannidis Elias B. Kosmatopoulos Stefanos Vrochidis Ioannis Kompatsiaris CoFly-WeedDB: A UAV image dataset for weed detection and species identification Data in Brief Precision agriculture UAV dataset Weed detection Deep convolutional neural networks Semantic segmentation |
| title | CoFly-WeedDB: A UAV image dataset for weed detection and species identification |
| title_full | CoFly-WeedDB: A UAV image dataset for weed detection and species identification |
| title_fullStr | CoFly-WeedDB: A UAV image dataset for weed detection and species identification |
| title_full_unstemmed | CoFly-WeedDB: A UAV image dataset for weed detection and species identification |
| title_short | CoFly-WeedDB: A UAV image dataset for weed detection and species identification |
| title_sort | cofly weeddb a uav image dataset for weed detection and species identification |
| topic | Precision agriculture UAV dataset Weed detection Deep convolutional neural networks Semantic segmentation |
| url | http://www.sciencedirect.com/science/article/pii/S235234092200782X |
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