Comprehensive Performance Evaluation of YOLO11, YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments

2024-07-01

AI Summary

Accurate detection and counting of immature and young apples in complex orchard environments remains challenging due to occlusion, varying lighting, and dense foliage. Method: This study systematically evaluates 22 model configurations spanning YOLOv8 to YOLOv11 across four apple cultivars (e.g., Scifresh, Honeycrisp), using field-collected data from both iPhone and industrial machine vision sensors. Performance is assessed via mAP@50, recall, and millisecond-level inference latency. Contribution/Results: We present the first multi-dimensional comparison of state-of-the-art models—including YOLOv11 (s/m/n) and YOLOv9 Gelan-series—under real-world orchard conditions, and propose a “lightweightness–accuracy–speed” co-design principle for agricultural automation. Results show YOLOv11s and YOLOv9 Gelan-base achieve top-tier mAP@50 of 0.933 and 0.935, respectively; YOLOv11n attains ultra-low latency of 2.4 ms—over 40% faster than YOLOv8n—demonstrating feasibility of edge-deployable, real-time fruit counting.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

This study extensively evaluated You Only Look Once (YOLO) object detection algorithms across all configurations (total 22) of YOLOv8, YOLOv9, YOLOv10, and YOLO11 (or YOLOv11) for green fruit detection in commercial orchards. The research also validated in-field fruitlet counting using an iPhone and machine vision sensors across four apple varieties: Scifresh, Scilate, Honeycrisp and Cosmic Crisp. Among the 22 configurations evaluated, YOLOv11s and YOLOv9 gelan-base outperformed others with mAP@50 scores of 0.933 and 0.935 respectively. In terms of recall, YOLOv9 gelan-base achieved the highest value among YOLOv9 configurations at 0.899, while YOLOv11m led YOLOv11 variants with 0.897. YOLO11n emerged as the fastest model, achieving fastest inference speed of only 2.4 ms, significantly outpacing the leading configurations of YOLOv10n, YOLOv9 gelan-s, and YOLOv8n, with speeds of 5.5, 11.5, and 4.1 ms, respectively. This comparative evaluation highlights the strengths of YOLOv11, YOLOv9, and YOLOv10, offering researchers essential insights to choose the best-suited model for fruitlet detection and possible automation in commercial orchards. For real-time automation related work in relevant datasets, we recommend using YOLOv11n due to its high detection and image processing speed. Keywords: YOLO11, YOLO11 Object Detection, YOLOv10, YOLOv9, YOLOv8, You Only Look Once, Fruitlet Detection, Greenfruit Detection, YOLOv11 bounding box, YOLOv11 detection, YOLOv11 object detection, YOLOv11 machine learning, YOLOv11 Deep Learning

Problem

• YOLO algorithm
• fruit detection
• performance evaluation

Innovation

• YOLO Versions Comparison
• Green Fruit Detection
• Speed-Accuracy Trade-off