YOLOv8-ABW based method for detecting Hemerocallis citrina Baroni maturity

Here, rapid and high-precision detection was proposed for Hemerocallis citrina Baroni maturity using YOLO v8-ABW. Current challenges were overcome on the similar features and mutual occlusions during recognition. The precision and efficiency of detection were improved to provide crucial technical support to intelligent real-time harvesting. Feature extraction and utilization were also greatly improved in the detection process of Hemerocallis citrina Baroni maturity. Attention-based Intra-scale Feature Interaction (AIFI) was incorporated into the feature extraction network. Feature information was interacted with and combined from various scales. The detection precision was enhanced to more effectively extract the information about Hemerocallis citrina Baroni. Specifically, the AIFI was used to weigh the features using the attention mechanism. More key areas were focused on extracting the features, in order to reduce the interference of noise and redundancy. Meanwhile, the scale feature interaction was used to fully utilize the feature information of different scales, thereby enhancing the precision and robustness of feature extraction. A weighted bidirectional feature pyramid feature fusion network (BiFPN) was used in the feature fusion network. This network structure was achieved in the complementarity and enhancement of various layers of feature information using cross-channel feature fusion. Compared with the traditional Feature Pyramid Network, BiFPN was used to retain more original feature information and fuse the features of different layers in a weighted manner, thus enriching the fused features. In addition, Bi FPN had effectively reduced the feature redundancy in the channels, thereby enhancing the speed and efficiency of detection. Moreover, WIoUv3 was used in the selection of the loss function. The loss function was specifically optimized for the standard quality anchor frames, in order to focus more on the location of targets during training. The WIoUv3 loss function was introduced to successfully enhance the localization performance, leading to more accurate and reliable detection. The experiment validated that the YOLOv8-ABW model was performed better to detect the maturity of Hemerocallis citrina Baroni. The precision reached 82.32%, the recall was 83.71%, mAP@0.5 and mAP@0.5:0.95 were 88.44% and 74.84%, respectively. The harmonic mean was improved to 0.86, and the real-time detection speed even reached 214.5 frames per second (f/s). Compared with the original one, the YOLOv8-ABW model showed improvements in the precision, recall, mAP@0.5, and mAP@0.5:0.95 by 1.41, 0.75, 1.54, and 1.42 percentage points, respectively. Compared to the rest, YOLOv8-ABW shared the least number of parameters, only 3.65×10⁶. The floating-point operations were 96.3 G less than the YOLOv7. The high precision YOLOv8-ABW also exhibited high computational efficiency for the real-time detection tasks related to Hemerocallis Citrina Baroni maturity. In summary, the YOLOv8-ABW model demonstrated excellent performance for the detection. The simplified complexity was also obtained with the detection precision and speed. The finding can offer robust technical support to the intelligent real-time harvesting of Hemerocallis citrina Baroni.