基于K230芯片和YOLOv11n的蓝莓果实成熟度识别系统设计OA
Design of blueberry fruit ripeness recognition system based on K230 and YOLOv11n
蓝莓成熟度检测目的是在自然环境下根据输入的蓝莓图像实现对蓝莓成熟度的自动判定,从而为蓝莓智能采摘提供技术支持.在实地采集蓝莓图像构建专用数据集基础上,基于YOLOv11n训练多成熟度检测模型,并将优化后的模型部署至K230芯片嵌入式设备.试验结果表明,该模型准确率较高,精确率、召回率、mAP0.5 和mAP0.50～0.95 指标分别达到90.1%、81.0%、88.1%和 64.9%,而参数量仅需 2 582 737个,可达到单帧图像推理速度 9.5 ms,部署在嵌入式设备的推理速度仅需 240 ms.该系统兼具高精度与实时性,其轻量化特性和嵌入式兼容性为蓝莓智能采摘机器人的实际应用提供了一种新的方法.
Blueberry ripeness detection aims to automatically determine blueberries ripeness based on input images captured in a natural environment,thereby providing technical support for intelligent blueberry harvesting.On basis of collecting blueberry images in field to build a dedicated dataset,a multi-ripeness detection model was trained based on YOLOv11n,and optimized model was deployed to K230 embedded platform.Experimental results showed that this model achieved high accuracy,with precision rate,recall rate,mAP0.5,and mAP0.50～0.95 indicators reaching 90.1%,81.0%,88.1%,and 64.9%respectively.Its parameter quantity was only 2 582 737,and it achieved a single-frame image inference speed of 9.5 ms,an inference speed on embedded device of only 240 ms.This system was both high accuracy and real-time performance.Its lightweight characteristics and embedded compatibility provided a new method for practical application of intelligent blueberry harvesting robots.
刘芹;罗启键;曾涛;欧阳婧玟;周智彬
仲恺农业工程学院自动化学院,广东 广州 510550||广东省智能海洋牧场与装备重点实验室,广东 广州 510550仲恺农业工程学院自动化学院,广东 广州 510550仲恺农业工程学院自动化学院,广东 广州 510550||广东省智能海洋牧场与装备重点实验室,广东 广州 510550仲恺农业工程学院机电工程学院,广东 广州 510550仲恺农业工程学院机电工程学院,广东 广州 510550
农业科技
蓝莓目标检测成熟度YOLOv11nK230芯片采摘机器人
blueberry fruitobject detectionripenessYOLOv11nK230 chipharvesting robot
《农业工程》 2026 (1)
14-20,7
广东省科学技术厅项目(KTP20240325)广州市科技局农村特派员项目(2025D04J0012)
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