**3. Results and Discussion**

#### *3.1. Image Slice Results*

The flower images were sliced using the SAHI algorithm and combined with the original images to create a hybrid dataset (Table 4). The relevant feature changes were recorded before and after the slicing (Table 5).

**Table 4.** Annotated data information after slicing using the SAHI algorithm.



**Table 5.** Changes in annotated data information after SAHI algorithm slicing.

Following the SAHI algorithm slice, the percentages of flowers in each category grew by 170.21%, 170.11%, 182.41%, and 176.16%, respectively, resulting in 109,813 high-quality labeled data (a 150% increase) to the network. While allowing for a higher batch size for training, the changes in the aspect ratio and average area were within 3.00% and 5.00%, and the corresponding change in the area ratio was at least 1268.89%. In addition, the fully open stage had the largest average single flower area, which was 7.43 times bigger than the bud stage, 4.46 times bigger than the half-open stage, and 3.06 times bigger than the end-open stage. The high area of full blooms prompted the SAHI algorithm to split the fully open flowers that were at the boundary of the cut area into multiple parts and to consider them as newly fully open. This split came at the cost of a 3.90% reduction in the average areas, prompting the most significant increase in the number of fully open flowers. Fully open was the only flower growth stage with a negative average area increase.
