Improved YOLO-based real-time brinjal detection algorithm for vision modules in harvesting robots

摘要

Abstract A novel, lightweight, and accurate brinjal detection algorithm, YOLOv11s-Brinjal, was developed for vision modules in selective harvesting robots operating under complex horticultural environments. The algorithm addressed critical detection challenges, including variable lighting, spotlight effects, object overlap, occlusion, and cluttered backgrounds in unstructured farm settings. Multiple configurations from YOLOv8 to YOLOv12 were initially evaluated using a custom dataset, manually annotated and augmented through the Roboflow framework. The best-performing base model, YOLOv11s, was further optimized via systematic channel dimension pruning applied to the convolutional layers of its backbone architecture, significantly reducing both parameter count and computational load. To mitigate performance degradation and ensure task-specific alignment, weight adjustment techniques were implemented during fine-tuning. The YOLOv11s-Brinjal model was evaluated using the same test datasets, demonstrating robust performance with precision, recall, F1 score, and mean average precision values of 94%, 96.6%, 95.3%, and 98.1%, respectively. To assess generalization and detect potential overfitting, a 5-fold cross-validation was conducted. Compared to the original model, the proposed pruning and weight adjustment techniques improved recall by 1.3% , while reducing parameters and computational load by over 57%. With a compact model size of 8.2 MB and an inference time of 10.1 ms, YOLOv11s-Brinjal is well-suited for integration on edge devices as the vision component in real-time selective brinjal harvesting applications.