• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于预播种模式下机器视觉的甘蔗种茎切断系统。

Sugarcane-Seed-Cutting System Based on Machine Vision in Pre-Seed Mode.

机构信息

School of Engineering, Anhui Agricultural University, Hefei 230036, China.

Anhui Province Engineering Laboratory of Intelligent Agricultural Machinery and Equipment, Hefei 230036, China.

出版信息

Sensors (Basel). 2022 Nov 2;22(21):8430. doi: 10.3390/s22218430.

DOI:10.3390/s22218430
PMID:36366128
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9655777/
Abstract

China is the world's third-largest producer of sugarcane, slightly behind Brazil and India. As an important cash crop in China, sugarcane has always been the main source of sugar, the basic strategic material. The planting method of sugarcane used in China is mainly the pre-cutting planting mode. However, there are many problems with this technology, which has a great impact on the planting quality of sugarcane. Aiming at a series of problems, such as low cutting efficiency and poor quality in the pre-cutting planting mode of sugarcane, a sugarcane-seed-cutting device was proposed, and a sugarcane-seed-cutting system based on automatic identification technology was designed. The system consists of a sugarcane-cutting platform, a seed-cutting device, a visual inspection system, and a control system. Among them, the visual inspection system adopts the YOLO V5 network model to identify and detect the eustipes of sugarcane, and the seed-cutting device is composed of a self-tensioning conveying mechanism, a reciprocating crank slider transmission mechanism, and a high-speed rotary cutting mechanism so that the cutting device can complete the cutting of sugarcane seeds of different diameters. The test shows that the recognition rate of sugarcane seed cutting is no less than 94.3%, the accuracy rate is between 94.3% and 100%, and the average accuracy is 98.2%. The bud injury rate is no higher than 3.8%, while the average cutting time of a single seed is about 0.7 s, which proves that the cutting system has a high cutting rate, recognition rate, and low injury rate. The findings of this paper have important application values for promoting the development of sugarcane pre-cutting planting mode and sugarcane planting technology.

摘要

中国是世界第三大甘蔗生产国，仅次于巴西和印度。作为中国的重要经济作物，甘蔗一直是制糖的主要原料，是基本的战略物资。中国采用的甘蔗种植方式主要是预切种植模式。然而，这种技术存在许多问题，对甘蔗的种植质量有很大的影响。针对甘蔗预切种植模式中存在的切割效率低、质量差等一系列问题，提出了一种甘蔗切种装置，并设计了一种基于自动识别技术的甘蔗切种系统。该系统由甘蔗切割平台、切种装置、视觉检测系统和控制系统组成。其中，视觉检测系统采用 YOLO V5 网络模型识别和检测甘蔗的假茎，并由自张紧输送机构、往复曲柄滑块传动机构和高速旋转切割机构组成，使切割装置能够完成不同直径的甘蔗种子的切割。试验表明，甘蔗种子切割的识别率不低于 94.3%，准确率在 94.3%到 100%之间，平均准确率为 98.2%。芽损伤率不高于 3.8%，而单粒种子的平均切割时间约为 0.7s，这证明了切割系统具有较高的切割率、识别率和较低的损伤率。本文的研究结果对促进甘蔗预切种植模式和甘蔗种植技术的发展具有重要的应用价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/9ceef7836b4f/sensors-22-08430-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/4efc18604670/sensors-22-08430-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/f21d8d6443d1/sensors-22-08430-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/769b7f742273/sensors-22-08430-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/95817f9ee89c/sensors-22-08430-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/549d67c7cd0f/sensors-22-08430-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/c653ed71750d/sensors-22-08430-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/bad5e9163a71/sensors-22-08430-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/05506d5118f0/sensors-22-08430-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/13b5341635a5/sensors-22-08430-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/cee85fd67959/sensors-22-08430-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/73fc0e2db084/sensors-22-08430-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/8aa77ee522dd/sensors-22-08430-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/2a5ae6170c32/sensors-22-08430-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/fd58c84a928c/sensors-22-08430-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/00ecfd24f335/sensors-22-08430-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/632875a8b41e/sensors-22-08430-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/215d89228f74/sensors-22-08430-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/9ceef7836b4f/sensors-22-08430-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/4efc18604670/sensors-22-08430-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/f21d8d6443d1/sensors-22-08430-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/769b7f742273/sensors-22-08430-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/95817f9ee89c/sensors-22-08430-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/549d67c7cd0f/sensors-22-08430-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/c653ed71750d/sensors-22-08430-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/bad5e9163a71/sensors-22-08430-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/05506d5118f0/sensors-22-08430-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/13b5341635a5/sensors-22-08430-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/cee85fd67959/sensors-22-08430-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/73fc0e2db084/sensors-22-08430-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/8aa77ee522dd/sensors-22-08430-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/2a5ae6170c32/sensors-22-08430-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/fd58c84a928c/sensors-22-08430-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/00ecfd24f335/sensors-22-08430-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/632875a8b41e/sensors-22-08430-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/215d89228f74/sensors-22-08430-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2b9/9655777/9ceef7836b4f/sensors-22-08430-g018.jpg

相似文献

1
Sugarcane-Seed-Cutting System Based on Machine Vision in Pre-Seed Mode.基于预播种模式下机器视觉的甘蔗种茎切断系统。
Sensors (Basel). 2022 Nov 2;22(21):8430. doi: 10.3390/s22218430.
2
A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor.一种基于物联网技术和 RGB 颜色传感器的新型自动甘蔗种子切割机。
PLoS One. 2024 Mar 28;19(3):e0301294. doi: 10.1371/journal.pone.0301294. eCollection 2024.
3
Design, construction and field testing of a manually feeding semiautomatic sugarcane dud chipper.手动进料半自动甘蔗蔗渣切碎机的设计、制作与现场测试。
Sci Rep. 2024 Mar 4;14(1):5373. doi: 10.1038/s41598-024-54980-3.
4
Advanced design and Engi-economical evaluation of an automatic sugarcane seed cutting machine based RGB color sensor.基于 RGB 颜色传感器的自动甘蔗种切机的先进设计与工程经济评估。
PLoS One. 2024 Oct 17;19(10):e0306584. doi: 10.1371/journal.pone.0306584. eCollection 2024.
5
Sugarcane stem node detection and localization for cutting using deep learning.基于深度学习的甘蔗茎节切割检测与定位
Front Plant Sci. 2022 Dec 12;13:1089961. doi: 10.3389/fpls.2022.1089961. eCollection 2022.
6
Varied frequencies of resistance alleles to Cry1Ab and Cry1Ac among Brazilian populations of the sugarcane borer, Diatraea saccharalis (F.).巴西蔗螟（Diatraea saccharalis （F.））种群对 Cry1Ab 和 Cry1Ac 的抗性等位基因存在多样化频率。
Pest Manag Sci. 2022 Dec;78(12):5150-5163. doi: 10.1002/ps.7133. Epub 2022 Sep 7.
7
Agronomic parameters of sugarcane under planting densities in different cultivation cycles.不同种植周期下甘蔗种植密度的农艺参数。
Braz J Biol. 2023 Dec 22;83:e278007. doi: 10.1590/1519-6984.278007. eCollection 2023.
8
Sugarcane stem node identification algorithm based on improved YOLOv5.基于改进 YOLOv5 的甘蔗茎节点识别算法。
PLoS One. 2023 Dec 11;18(12):e0295565. doi: 10.1371/journal.pone.0295565. eCollection 2023.
9
Identification and Localisation Algorithm for Sugarcane Stem Nodes by Combining YOLOv3 and Traditional Methods of Computer Vision.基于 YOLOv3 和传统计算机视觉方法的甘蔗茎节点识别与定位算法。
Sensors (Basel). 2022 Oct 28;22(21):8266. doi: 10.3390/s22218266.
10
Metabolite profiles of energy cane and sugarcane reveal different strategies during the axillary bud outgrowth.能源甘蔗和甘蔗的代谢产物谱揭示了腋芽生长过程中的不同策略。
Plant Physiol Biochem. 2021 Oct;167:504-516. doi: 10.1016/j.plaphy.2021.08.022. Epub 2021 Aug 14.

引用本文的文献

1
EdgeSugarcane: a lightweight high-precision method for real-time sugarcane node detection in edge computing environments.EdgeSugarcane：一种用于边缘计算环境中实时甘蔗节点检测的轻量级高精度方法。
Front Plant Sci. 2025 Jul 7;16:1626725. doi: 10.3389/fpls.2025.1626725. eCollection 2025.
2
A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor.一种基于物联网技术和 RGB 颜色传感器的新型自动甘蔗种子切割机。
PLoS One. 2024 Mar 28;19(3):e0301294. doi: 10.1371/journal.pone.0301294. eCollection 2024.
3
Design, construction and field testing of a manually feeding semiautomatic sugarcane dud chipper.

本文引用的文献

1
Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor.基于双重优化人工碳氢网络的轮内电机机械故障智能诊断。
Sensors (Basel). 2022 Aug 22;22(16):6316. doi: 10.3390/s22166316.
2
Image Recognition of Wind Turbine Blade Defects Using Attention-Based MobileNetv1-YOLOv4 and Transfer Learning.基于注意力机制的MobileNetv1-YOLOv4和迁移学习的风力涡轮机叶片缺陷图像识别
Sensors (Basel). 2022 Aug 11;22(16):6009. doi: 10.3390/s22166009.
3
Learning-Based Slip Detection for Robotic Fruit Grasping and Manipulation under Leaf Interference.
手动进料半自动甘蔗蔗渣切碎机的设计、制作与现场测试。
Sci Rep. 2024 Mar 4;14(1):5373. doi: 10.1038/s41598-024-54980-3.
4
Sugarcane stem node detection and localization for cutting using deep learning.基于深度学习的甘蔗茎节切割检测与定位
Front Plant Sci. 2022 Dec 12;13:1089961. doi: 10.3389/fpls.2022.1089961. eCollection 2022.
基于学习的叶干扰下机器人果实抓取和操作中的滑动检测。
Sensors (Basel). 2022 Jul 22;22(15):5483. doi: 10.3390/s22155483.
4
Improved Faster R-CNN Based Surface Defect Detection Algorithm for Plates.基于改进型更快区域卷积神经网络的板材表面缺陷检测算法
Comput Intell Neurosci. 2022 May 17;2022:3248722. doi: 10.1155/2022/3248722. eCollection 2022.
5
One Spatio-Temporal Sharpening Attention Mechanism for Light-Weight YOLO Models Based on Sharpening Spatial Attention.基于锐化空间注意力的轻量级 YOLO 模型的一种时空锐化注意力机制。
Sensors (Basel). 2021 Nov 28;21(23):7949. doi: 10.3390/s21237949.
6
Small Object Detection in Traffic Scenes Based on YOLO-MXANet.基于 YOLO-MXANet 的交通场景小目标检测。
Sensors (Basel). 2021 Nov 8;21(21):7422. doi: 10.3390/s21217422.
7
Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT.基于 YOLOv4 模型和 Deep SORT 的实时梨果检测与计数
Sensors (Basel). 2021 Jul 14;21(14):4803. doi: 10.3390/s21144803.
8
Real-Time Plant Leaf Counting Using Deep Object Detection Networks.基于深度目标检测网络的实时植物叶片计数。
Sensors (Basel). 2020 Dec 3;20(23):6896. doi: 10.3390/s20236896.