DOI: 10.5281/zenodo.18224814
title: Actinidia chinensis Phenology Balanced Dataset (Portugal, 2025)

description: The Actinidia chinensis Phenology Balanced Dataset is a stratified and class-balanced subset derived from the Multi-Modal Actinidia chinensis Phenology Dataset, prepared explicitly for training hierarchical detection models that target female flower phenological staging.

The source material comprises the Labelled Images component from the original dataset, which contains smartphone-acquired imagery of kiwifruit reproductive structures annotated in Pascal VOC format according to a 17-class hierarchical taxonomy organised across three levels: structure (bud, flower, fruit), gender (female, male), and BBCH-adapted phenological stage.

The balanced dataset was generated through a two-stage processing pipeline:



Split stage: The source imagery was partitioned into test (99 images) and train+validation (1 556 images) subsets using stratified sampling to ensure proportional class representation in the test set across all hierarchical classification levels.

Balancing stage: Images exhibiting disproportionate annotation density for over-represented classes were systematically removed from the train+validation subset to facilitate subsequent balancing operations (1 311 images retained). Augmentation operations were applied iteratively until target class distributions were achieved (1 311 original + 649 augmented). Composite operations were constructed by combining one geometric transformation (flip, scale_rotate and downscale) with one appearance transformation (bright_contrast, grid_distortion, grid_dropout, unsharp and motion_blur).  All augmentations were implemented using the Albumentations library, which includes bounding box coordinate transformation.





Operation
Description


flip
Horizontal and vertical reflection.


scale_rotate
Affine transformation with shift (±6.25%), scale (±10%), and rotation (±15°).


bright_contrast
Brightness and contrast modulation (±40%).


downscale
Resolution reduction (50%) with interpolation.


grid_distortion
Elastic grid-based spatial distortion.


grid_dropout
Grid-based region dropout (20% ratio).


unsharp
Unsharp masking for edge enhancement.


motion_blur
Directional motion blur simulation.




Phenological stage labels for bud and male flower classes were consolidated to their parent categories, reducing the taxonomy from 17 to 9 object classes while preserving full phenological granularity for the female flower pathway.




Original label
New label


bud_53
bud


bud_55
bud


bud_56
bud


bud_57
bud


bud
bud


flower_female_60
flower_female_60


flower_female_61
flower_female_61


flower_female_67
flower_female_67


flower_female_68
flower_female_68


flower_female_69
flower_female_69


flower_female
flower_female


flower_male_60
flower_male


flowermale_61
flower_male


flower_male_67
flower_male


flower_male
flower_male


flower
flower


fruit
-




The processed dataset is organised into two directories (test and train+validation), each containing an Images folder with JPEG files and an Annotations folder with corresponding Pascal VOC XML files.




Set
Images
Annotations
Classes


test
99
942
9


train + validation
1 960
16 694
9




The hierarchical detection pathway comprises three sequential classification tasks. The following tables present the class distribution for each level, demonstrating the balance achieved through the processing pipeline. Image counts indicate images containing at least one annotation of that class; individual images may contain multiple classes.

The first level performs structure detection, distinguishing between bud and flower structures:




Class
test
train+validation


bud
67 images with 473 annotations
1,256 images with 7,771 annotations


flower
174 images with 460 annotations
2,892 images with 8,827 annotations


background
11 images 
98 images 




The second level performs gender classification on detected flowers. Images containing only bud, fruit, or no annotations serve as background for this classification task:




Class
test
train+validation


flower_female 
128 images with 228 annotations
1 694 images with 3 497 annotations


flower_male
27 images with 199 annotations
704 images with 4 295 annotations


flower 
19 images with 33 annotations
494 images with 1 035 annotations


background
83 images 
1 373 images 




The third level performs phenological stage classification on detected female flowers. Images containing only male flowers, generic flowers, buds, fruit, or no annotations serve as background for this classification task:




Class
test
train+validation


flower_female
23 images with 32 annotations
332 images with 537 annotations


flower_female_60
23 images with 38 annotations
318 images with 485 annotations


flower_female_61
29 images with 45 annotations
360 images with 683 annotations


flower_female_67
19 images with 35 annotations
297 images with 797 annotations


flower_female_68
14 images with 26 annotations
213 images with 489 annotations


flower_female_69
20 images with 52 annotations
174 images with 506 annotations


background
130 images 
1 559 images




The train+validation subset should be used with k-fold cross-validation for model development and hyperparameter optimisation. The test subset is reserved for final model evaluation and should not be used during training or validation. Users requiring the complete 17-class taxonomy or male flower phenological staging should refer to the original dataset.