Accurate Orah fruit detection method using lightweight improved YOLOv8n model verified by optimized deployment on edge device

The replacement of personal computer terminal with edge device is recognized as a portable and cost-effective potential solution in solving equipment miniaturization and achieving high flexibility of robotic fruit harvesting at in-field scale. This study proposes a lightweight improved You Only Look Once version 8n (YOLOv8n) model for detecting Orah fruits and deploying this model on an edge device. First of all, the model size was reduced while maintaining detection accuracy via the introduction of the ADown modules. Subsequently, a Concentrated-Comprehensive Dual Convolution (C3_DualConv) module combining dual convolutional bottlenecks was proposed to enhance the model capability to capture features of Orah fruits obscured by branches and leaves; this practice further reduced the model size. Additionally, a Bidirectional Feature Pyramid Network (BiFPN) that includes a pyramid level 2 high-resolution layer was employed for more efficient multi-scale feature fusion. Besides, three Coordinate Attention (CA) mechanism modules were also added to improve the recognition and capture capability for Orah fruit features. Finally, a more focused minimum points distance intersection over union loss was adopted to boost the detection efficiency of densely occluded Orah fruits. Experimentally demonstrating that the improved YOLOv8n model accurately detected Orah fruits in complex orchard environments, achieving a 97.7 % of precision, an Average Precision at IoU threshold 0.5 ([email protected]) of 98.8 %, and a 96.69 % of F1 score, while maintaining a compact model size of 4.1 MB, under a Windows-based system terminal. This proposed model was optimally deployed on an Nvidia Jetson Orin Nano using TensorRT Python Application Programming Interface (API), the average interface speed exceeds 30 fps, indicating a real-time detection ability. This study can provide technical support for Orah fruit robotic harvesting on the basis of edge device.