The width-to-height ratio of mountain T.

Kirilowii fruits within the sample images. Therefore, this experiment proposes adding a small object detection layer to enhance the base algorithm. The specific implementation method is as follows: Firstly, within the existing detection layers, an additional detection head of size 160 × 160 is introduced to achieve a 4 × 4pixel receptive field, enabling the capture of finer details for smaller objects. Next, a set of feature layers composed of CBS convolutional modules, upsampling layers, Concat concatenation layers, and an ELAN-A layer is added to the original model, inserted between the 17th and 18th layers. Therefore, the original algorithm’s three-scale layers are elevated to four scales: 80 × 80, 40 × 40, 20 × 20, and the newly added 160 × 160, as shown in the red dashed area in Fig.3. Finally, an experiment is demonstrated to determine the most suitable detection scale for T.Kirilowii. The improvement reduces model parameters and computational burden while minimizing the loss of shallow information for small objects. Consequently, it enhanced the small objects of T.Kirilowii recognition under occlusion.