旋转式耕作作业中农田表面地形和耕作深度的综合测量方法

IF 8.9 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Gaolong Chen, Yuqi Chen, Zhicheng Huang, Jingting Wang, Yufei Deng, Pei Wang, Runmao Zhao, Lian Hu
{"title":"旋转式耕作作业中农田表面地形和耕作深度的综合测量方法","authors":"Gaolong Chen,&nbsp;Yuqi Chen,&nbsp;Zhicheng Huang,&nbsp;Jingting Wang,&nbsp;Yufei Deng,&nbsp;Pei Wang,&nbsp;Runmao Zhao,&nbsp;Lian Hu","doi":"10.1016/j.compag.2025.111000","DOIUrl":null,"url":null,"abstract":"<div><div>Field surface topography and tillage depth are crucial information for guiding crop production. However, the separate measurement of field surface topography and tillage depth increases production costs. To address issues, this study proposes an integrated measurement method for field surface topography and tillage depth in rotary tillage operations. Based on the operational characteristics of the rotary tiller, a simultaneous measurement method for field surface and the tillage bottom-layer topography (FS-TBLSM) was proposed. Building on this, a method was developed to arrange grid points, referred to as the directional adaptive gridding method in plane topography (DAG-PT), and a sample-approximated Gaussian process regression (SA-GPR) algorithm was used to estimate the field surface topography height and tillage depth at a given grid point. The accuracy of these methods was evaluated using verification and field tests. The verification results showed that the FS-TBLSM method achieved a static root mean square error (RMSE) of less than 15.00 mm along all three axes, with dynamic RMSEs below 20.00 mm, confirming the effectiveness of the FS-TBLSM method and its good dynamic tracking capability. Further field test results indicated that the field surface topography measured using the FS-TBLSM method aligned with the true topography. The measured field surface topography height exhibited an average absolute error (AAE) of 18.13 mm and an RMSE of 20.58 mm, validating the accuracy and reliability of this method for field surface topography measurement. Using true surface topographic height and tillage depth at 20 points as references, an AAE and RMSE of 17.13 and 17.95 mm, respectively, were obtained for surface topographic height estimation; estimated tillage depth exhibited an AAE and RMSE of 14.52 and 16.49 mm, respectively. These results demonstrate that the SA-GPR algorithm can accurately estimate the field surface topography height and tillage depth after rotary tillage operations. The integrated measurement method performs the measurement in a single operation, reducing the number of field operations by 50 %, saving an estimated 15.57 kg/ha in fuel consumption. Additionally, this study provides key inputs for leveling operations, including setting the base height, calculating earthwork volume, and planning paths. It also supports active control of seeding depth and provides references for yield assessment.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111000"},"PeriodicalIF":8.9000,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrated measurement method for field surface topography and tillage depth in rotary tillage operations\",\"authors\":\"Gaolong Chen,&nbsp;Yuqi Chen,&nbsp;Zhicheng Huang,&nbsp;Jingting Wang,&nbsp;Yufei Deng,&nbsp;Pei Wang,&nbsp;Runmao Zhao,&nbsp;Lian Hu\",\"doi\":\"10.1016/j.compag.2025.111000\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Field surface topography and tillage depth are crucial information for guiding crop production. However, the separate measurement of field surface topography and tillage depth increases production costs. To address issues, this study proposes an integrated measurement method for field surface topography and tillage depth in rotary tillage operations. Based on the operational characteristics of the rotary tiller, a simultaneous measurement method for field surface and the tillage bottom-layer topography (FS-TBLSM) was proposed. Building on this, a method was developed to arrange grid points, referred to as the directional adaptive gridding method in plane topography (DAG-PT), and a sample-approximated Gaussian process regression (SA-GPR) algorithm was used to estimate the field surface topography height and tillage depth at a given grid point. The accuracy of these methods was evaluated using verification and field tests. The verification results showed that the FS-TBLSM method achieved a static root mean square error (RMSE) of less than 15.00 mm along all three axes, with dynamic RMSEs below 20.00 mm, confirming the effectiveness of the FS-TBLSM method and its good dynamic tracking capability. Further field test results indicated that the field surface topography measured using the FS-TBLSM method aligned with the true topography. The measured field surface topography height exhibited an average absolute error (AAE) of 18.13 mm and an RMSE of 20.58 mm, validating the accuracy and reliability of this method for field surface topography measurement. Using true surface topographic height and tillage depth at 20 points as references, an AAE and RMSE of 17.13 and 17.95 mm, respectively, were obtained for surface topographic height estimation; estimated tillage depth exhibited an AAE and RMSE of 14.52 and 16.49 mm, respectively. These results demonstrate that the SA-GPR algorithm can accurately estimate the field surface topography height and tillage depth after rotary tillage operations. The integrated measurement method performs the measurement in a single operation, reducing the number of field operations by 50 %, saving an estimated 15.57 kg/ha in fuel consumption. Additionally, this study provides key inputs for leveling operations, including setting the base height, calculating earthwork volume, and planning paths. It also supports active control of seeding depth and provides references for yield assessment.</div></div>\",\"PeriodicalId\":50627,\"journal\":{\"name\":\"Computers and Electronics in Agriculture\",\"volume\":\"239 \",\"pages\":\"Article 111000\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2025-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers and Electronics in Agriculture\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0168169925011068\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169925011068","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

农田表面地形和耕作深度是指导作物生产的重要信息。然而,单独测量农田表面地形和耕作深度增加了生产成本。为了解决这些问题,本研究提出了一种旋耕机作业中农田表面地形和耕作深度的综合测量方法。根据旋耕机的工作特点,提出了一种旋耕机田面与耕层地形同时测量的方法(FS-TBLSM)。在此基础上,提出了平面地形定向自适应网格点法(DAG-PT),并利用样本逼近高斯过程回归(SA-GPR)算法估计给定网格点处的田面地形高度和耕作深度。通过验证和现场试验对这些方法的准确性进行了评价。验证结果表明,FS-TBLSM方法三轴静态均方根误差(RMSE)均小于15.00 mm,动态均方根误差(RMSE)均小于20.00 mm,验证了FS-TBLSM方法的有效性和良好的动态跟踪能力。进一步的现场测试结果表明,利用FS-TBLSM方法测量的现场表面地形与真实地形一致。实测的野外地形高度平均绝对误差(AAE)为18.13 mm,均方根误差(RMSE)为20.58 mm,验证了该方法野外地形测量的准确性和可靠性。以20个点的真实地表地形高度和耕作深度为参考,估算地表地形高度的AAE和RMSE分别为17.13和17.95 mm;估计耕作深度的AAE和RMSE分别为14.52和16.49 mm。结果表明,SA-GPR算法能较准确地估计旋耕作业后的田面地形高度和耕作深度。综合测量方法在一次作业中完成测量,减少了50%的现场作业次数,节省了15.57 kg/ha的燃料消耗。此外,本研究还提供了基础高度设置、土方体积计算和路径规划等平整作业的关键输入。它还支持播种深度的主动控制,并为产量评估提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrated measurement method for field surface topography and tillage depth in rotary tillage operations
Field surface topography and tillage depth are crucial information for guiding crop production. However, the separate measurement of field surface topography and tillage depth increases production costs. To address issues, this study proposes an integrated measurement method for field surface topography and tillage depth in rotary tillage operations. Based on the operational characteristics of the rotary tiller, a simultaneous measurement method for field surface and the tillage bottom-layer topography (FS-TBLSM) was proposed. Building on this, a method was developed to arrange grid points, referred to as the directional adaptive gridding method in plane topography (DAG-PT), and a sample-approximated Gaussian process regression (SA-GPR) algorithm was used to estimate the field surface topography height and tillage depth at a given grid point. The accuracy of these methods was evaluated using verification and field tests. The verification results showed that the FS-TBLSM method achieved a static root mean square error (RMSE) of less than 15.00 mm along all three axes, with dynamic RMSEs below 20.00 mm, confirming the effectiveness of the FS-TBLSM method and its good dynamic tracking capability. Further field test results indicated that the field surface topography measured using the FS-TBLSM method aligned with the true topography. The measured field surface topography height exhibited an average absolute error (AAE) of 18.13 mm and an RMSE of 20.58 mm, validating the accuracy and reliability of this method for field surface topography measurement. Using true surface topographic height and tillage depth at 20 points as references, an AAE and RMSE of 17.13 and 17.95 mm, respectively, were obtained for surface topographic height estimation; estimated tillage depth exhibited an AAE and RMSE of 14.52 and 16.49 mm, respectively. These results demonstrate that the SA-GPR algorithm can accurately estimate the field surface topography height and tillage depth after rotary tillage operations. The integrated measurement method performs the measurement in a single operation, reducing the number of field operations by 50 %, saving an estimated 15.57 kg/ha in fuel consumption. Additionally, this study provides key inputs for leveling operations, including setting the base height, calculating earthwork volume, and planning paths. It also supports active control of seeding depth and provides references for yield assessment.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信