Lightweight pineapple detection framework for agricultural robots via YOLO-v5sp
DOI:
https://doi.org/10.25165/ijabe.v18i3.8984Keywords:
deep learning, target detection, lightweight networks, pineapple, YOLOv5sAbstract
Ensuring the accurate detection of pineapple fruits under the high planting density and serious homogenization represents a current and significant challenge. In this study, an enhanced lightweight detection framework, derived from the improved You Only Look Once version 5s (YOLOv5sp), is investigated in terms of the rapid and precise recognition of pineapple fruit for the agricultural robot. Three Convolutional Block Attention Module (CBAM) attention modules are considered the backbone network responsible for feature extraction, and the SIoU loss function is introduced to replace the CIoU loss function to handle the orientation angle and the penalization index. Eventually, the designed YOLOv5sp detection result of the mAP@0.5 value is 94.5%, which is 6.30% higher than YOLOv4, 1.83% higher than Faster R-CNN, and 6.90% higher than classical YOLOv5s. At the same time, compared with the models SHFP-YOLO and RGDP-YOLOv7-tiny in other pineapple detection literature, the mAP@0.5 of the designed model is 4.54% and 3.5% higher, respectively. Furthermore, when it comes to the agricultural robot operating in diverse natural situations, the YOLOv5sp algorithm can maintain a successful picking rate of 90% with an average time of 15 s, exhibiting the effectiveness of the visual component in engineering scenarios. These research results can accelerate the transition of pineapple harvesting from manual to automated operations. Keywords: deep learning, target detection, lightweight networks, pineapple, YOLOv5s DOI: 10.25165/j.ijabe.20251803.8984 Citation: Li J H, Li C L, Luo X W, Chen J L P, Yang C G. Lightweight pineapple detection framework for agricultural robots via YOLO-v5sp. Int J Agric & Biol Eng, 2025; 18(3): 204–214.References
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