Year: 2025
Type: article
Abstract: Accurate detection of apple stems is crucial for robotic cutting. This study proposed an improved YOLOv8-stem method for apple stem detection in overhead imagery under occlusion conditions. First, several im-provements were made to the YOLOv8 neural network: the conventional convolutional process within the in-termediate neck layer was substituted with the AK Convolution mechanism, a small object detection head was added, and ResBlock+CBAM attention mechanism was incorporated. Second, stem occlusion was determined by analyzing the positional relationship between the detected bounding boxes of stems and apples. The exper-imental results showed that compared to the original YOLOv8, this method improved apple stem detection ac-curacy by 6.0% (from 79.9% to 85.9%) and increased harvesting completeness from 84.2% to 93.2%.
Source: INMATEH Agricultural Engineering
Authors: Li WANG, Yanqi SUN, Tianle ZHANG, Panpan YAN, Qiangqiang YAO, Zhen MA, Degui MA, Xingdong SUN
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
Awards: G708578046, G7556756958
OpenAlex ID: W4413372585