Omkar2118/datasets huggingface dataset:
Pear640

Dataset contains annotated photographs of pear orchard for object detection tasks using YOLO architecture.
It contains only one class "Pear fruits".  This dataset is distributed under license (CC BY 4.0).

Source: <a href="https://www.kaggle.com/datasets/projectlzp201910094/pear640">https://www.kaggle.com/datasets/projectlzp201910094/pear640</a>

Presented in paper: Sergejs Kodors, Marks Sondors, Gunārs Lācis, Edgars Rubauskis, Ilmārs Apeināns, Imants Zarembo. "RAPID PROTOTYPING OF PEAR DETECTION NEURAL NETWORK WITH YOLO ARCHITECTURE IN PHOTOGRAPHS," In Proceedings of the International Scientific and Practical Conference "Environment. Technology. Resources.", Rezekne, Latvia, June 15-16, 2023, vol. 1, pp. 81-85, doi: <a href="https://doi.org/10.17770/etr2023vol1.7293">10.17770/etr2023vol1.7293</a>
PFruitlet640
Full name: PearFruitlet640

Dataset contains annotated photographs of pear fruitlets for object detection tasks using YOLO architecture. It has only one class "Pear fruitlet".
The digital images of pear fruitlets were collected in the experimental site of the Institute of Horticulture (LatHort) with cultivars ‘Suvenirs’, ‘Vasarine Sviestine’ and ‘Mramornaya’ on seedling rootstocks ‘Kazraushu’ with planting distances 4×5 m (500 trees per 1 ha). (Krimūnu parish, Dobeles district, Latvia: 56.610169, 23.305956). The collection of fruitlet images of ‘Suvenirs’, ‘Vasarine Sviestine’ and ‘Mramornaya’ was done at the beginning of August (79 days after full bloom). The collection of digital images was carried out using a photo camera of mobile device Huawei P 40: 50 MP Ultra Vision Camera (Wide Angle, f/1.9 aperture) + 16 MP Ultra-Wide Angle Camera (f/2.2 aperture) + 8 MP Telephoto Camera (f/2.4 aperture, OIS), the image size: 3000×4000 px; 5.0 MP. The collection of images was carried out in field conditions in 2022, in the orchard at the distance from the tree planting point 2.5 m (middle of alleyway). The whole canopy of trees was photographed as separate objects. The images were taken in the front of a tree (a tree trunk, a planting point), perpendicularly the tree row from the west side of rows (the rows of pear trees oriented from north to south) before noon (10:00–12:00) at natural light conditions. The dataset was annotated for YOLO architecture by team of Rezekne Academy of Technologies.

Source: <a href="https://www.kaggle.com/datasets/projectlzp201910094/pfruitlet640">https://www.kaggle.com/datasets/projectlzp201910094/pfruitlet640</a>

Presented in <a href="https://doi.org/10.15159/ar.24.012">S. Kodors, M. Sondors, I. Apeinans, I. Zarembo, G. Lacis, E. Rubauskis and K. Karklina. "Importance of mosaic augmentation for agricultural image dataset", Agronomy Research, 2024.</a>

License CC BY 4.0
CherryBBCH72
Full name: Cherry fruitlets BBCH72

The photo fixation of cherry fruitlets was done in the LatHort orchard in Dobele, at the development of fruit (BBCH stage 72). BBCH-scale describes the phenological development of grapes: 7 - development of fruit; 72 - fruit size up to 20 mm. Two photo images were taken for each tree – perpendicularly, in a tree-facing view and in an oblique view. The images were annotated using the tool makesense.ai. Then the annotated images 3008x2000 were automatically cropped out on 640x640 images with overlap 30% and validated manually. The images were saved in YOLO format.

Source: <a href="https://www.kaggle.com/datasets/projectlzp201910094/cherrybbch72">https://www.kaggle.com/datasets/projectlzp201910094/cherrybbch72</a>

Presented in the article: Kodors, S.; Zarembo, I.; Lācis, G.; Litavniece, L.; Apeināns, I.; Sondors, M.; Pacejs, A. Autonomous Yield Estimation System for Small Commercial Orchards Using UAV and AI. Drones 2024, 8, 734. <a href="https://doi.org/10.3390/drones8120734">https://doi.org/10.3390/drones8120734</a>
CherryBBCH81
Full name: Cherry Fruits

The photo fixation of cherry fruits was done in the LatHort orchard in Dobele, at the beginning of fruit coloration (BBCH stage 81). BBCH-scale for grapes describes the phenological development of grapes: 8 - ripening of berries; 81 - beginning of ripening: berries begin to develop variety-specific colour. Two photo images were taken for each tree – perpendicularly, in a tree-facing view and in an oblique view. The images were annotated using the tool makesense.ai. Then the annotated images 6016x4000 were automatically cropped out on 640x640 images with overlap 30% and validated manually. The images were saved in YOLO format.

Source: <a href="https://www.kaggle.com/datasets/projectlzp201910094/cfruitlets81-640">https://www.kaggle.com/datasets/projectlzp201910094/cfruitlets81-640</a>

Dataset was presented in the article:  Ilmārs Apeināns, Marks Sondors, Lienīte Litavniece, Sergejs Kodors, Imants Zarembo, Daina Feldmane. "Cherry Fruitlet Detection using YOLOv5 or YOLOv8?," In Proceedings of the 15th International Scientific and Practical Conference "Environment. Technology. Resources.", Rezekne, Latvia, June 27-28, 2024, vol. 2, pp. 29-33, doi: <a href="https://doi.org/10.17770/etr2024vol2.8013">10.17770/etr2024vol2.8013</a>
AppleBBCH76
Full name: Apple Fruitlets BBCH76-78

The photo fixation of apple fruitlets was done in the LatHort orchard in Dobele, at the development of fruit (BBCH stage 76-78). BBCH-scale describes the phenological development of grapes: 7 - development of fruit; 76 - fruit about 60% final size; 78 - fruit about 80% final size. Two photo images were taken for each tree – perpendicularly, in a tree-facing view and in an oblique view. The images were annotated using the tool makesense.ai. Then the annotated images 3008x2000 were automatically cropped out on 640x640 images with overlap 30% and validated manually. The images were saved in YOLO format.

Source: <a href="https://www.kaggle.com/datasets/projectlzp201910094/applebbch76">https://www.kaggle.com/datasets/projectlzp201910094/applebbch76</a>

Dataset was presented in the article: Kodors, S., Zarembo, I., Lācis, G., Litavniece, L., Apeināns, I., Sondors, M., & Pacejs, A. (2024). Autonomous Yield Estimation System for Small Commercial Orchards Using UAV and AI. Drones, 8(12), 734. <a href="https://doi.org/10.3390/drones8120734">https://doi.org/10.3390/drones8120734</a>
AppleBBCH81
Full name: Apple Fruits BBCH 81-85

The photo fixation of apple fruits was done in the LatHort orchard in Dobele, at the maturity of fruit and seed (BBCH stage 81-85). BBCH-scale describes the phenological development of grapes: 8 - maturity of fruit and seed; 81 - beginning of ripening; 85 - advanced ripening. Two photo images were taken for each tree – perpendicularly, in a tree-facing view and in an oblique view. The images were annotated using the tool makesense.ai. Then the annotated images 3008x2000 were automatically cropped out on 640x640 images with overlap 30% and validated manually. The images were saved in YOLO format.

Source: <a href="https://www.kaggle.com/datasets/projectlzp201910094/applebbch81">https://www.kaggle.com/datasets/projectlzp201910094/applebbch81</a>

Dataset was presented in the article: Kodors, S., Zarembo, I., Lācis, G., Litavniece, L., Apeināns, I., Sondors, M., & Pacejs, A. (2024). Autonomous Yield Estimation System for Small Commercial Orchards Using UAV and AI. Drones, 8(12), 734. <a href="https://doi.org/10.3390/drones8120734">https://doi.org/10.3390/drones8120734</a>