| 基于深度学习目标测定的大蒜收获切根装置设计与试验 |
| |
| 引用本文： | 杨柯,胡志超,于昭洋,彭宝良,张延化,顾峰玮.基于深度学习目标测定的大蒜收获切根装置设计与试验[J].农业机械学报,2022,53(1):123-132. |
| |
| 作者姓名： | 杨柯 胡志超 于昭洋 彭宝良 张延化 顾峰玮 |
| |
| 作者单位： | 农业农村部南京农业机械化研究所,南京210014 |
| |
| 基金项目： | 国家自然科学基金项目(51805282)、江苏省现代农机装备与技术示范推广项目(NJ2020-24)和国家重点研发计划项目(2017YFD0701305-02) |
| |
| 摘 要： | 为研究适用于大蒜联合收获的智能化切根装置,提出了基于机器视觉的非接触式定位切根方法,设计了一种基于深度卷积神经网络的大蒜切根试验台.试验台采用深度学习的方法,对采集到的图像进行目标检测,利用APP完成人机交互和结果显示,由深度卷积神经网络给定切根的切入位置,电机控制系统自动调整定位双圆盘切根刀完成切根处理.目标比较试验...
|
| 关 键 词： | 卷积神经网络 YOLO 大蒜收获机 切根装置 |
| 收稿时间： | 2021/8/30 0:00:00 |
Design and Experiment of Garlic Harvesting and Root Cutting Device Based on Deep Learning Target Determination |
| |
| YANG Ke,HU Zhichao,YU Zhaoyang,PENG Baoliang,ZHANG Yanhu,GU Fengwei.Design and Experiment of Garlic Harvesting and Root Cutting Device Based on Deep Learning Target Determination[J].Transactions of the Chinese Society of Agricultural Machinery,2022,53(1):123-132. |
| |
| Authors: | YANG Ke HU Zhichao YU Zhaoyang PENG Baoliang ZHANG Yanhu GU Fengwei |
| |
| Institution: | Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs |
| |
| Abstract: | In order to study a suitable intelligent root cutting device for garlic combined harvesting, a non-contact bulb root cutting method with machine vision was proposed, and a garlic root cutting test bench based on a deep convolutional neural network was designed afterwards. Specially, the test bench adopted a deep learning theory to perform target detection on the collected images, through using the APP software in Matlab to complete the human-computer interaction. Then, the results presented that the deep convolutional neural network could determine the cutting position of the garlic root, and the motor control system could adjust the position of the double disc cutting automatically, ensuring the root cutting process completed by the root knife. Target comparison tests showed that bulb (availability rate was 94.79%, confidence score was 0.97697) was suitable for detecting, among the three kinds of bulb, root plate and garlic root. Comparison tests of detection models performed with ten models based on Faster R-CNN, SSD, YOLO v2, YOLO v3 and YOLO v4. The improved YOLO v2 model combined the detection speed and accuracy (the detection time in the test program was 0.0523s, and the confidence score was 0.96849), where ResNet50 was selected as the feature extraction network;by using the improved YOLO v2 model, the root cutting test took bulbs as the targets (the confidence score was 0.97099, the availability rate was 96.67%, the qualified rate of cutting roots was 95.33%, and the detection time in the APP was 0.0887s), can meet the requirements of garlic combined harvesting and cutting roots. |
| |
| Keywords: | convolutional neural network YOLO garlic harvester root cutting device |
| 本文献已被 维普 万方数据 等数据库收录！ |
| 点击此处可从《农业机械学报》浏览原始摘要信息 |
| 点击此处可从《农业机械学报》下载免费的PDF全文 |
|
