基于无人机影像的棉花株高预测

doi:10.6048/j.issn.1001-4330.2020.08.014

新疆农业科学 ›› 2020, Vol. 57 ›› Issue (8): 1493-1502.DOI: 10.6048/j.issn.1001-4330.2020.08.014

• 植物保护·微生物·土壤肥料·农产品分析检测·草业 • 上一篇下一篇

基于无人机影像的棉花株高预测

颜安^1,2,3, 郭涛^1,2, 陈全家⁴, 耿洪伟⁴, 郭斌⁴, 孙丰磊⁴

1.新疆农业大学草业与环境科学学院,乌鲁木齐 830052;
2.新疆土壤与植物生态过程重点实验室,乌鲁木齐 830052;
3.新疆农业大学信息化工程技术研究中心,乌鲁木齐 830052;
4.新疆农业大学农学院,乌鲁木齐 830052

收稿日期:2020-04-12 出版日期:2020-08-20 发布日期:2020-09-01
通信作者: 陈全家(1972-),男,新疆乌鲁木齐人,教授,博士,博士生导师,研究方向为棉花遗传育种,(E-mail)chqjia@126.com
作者简介:颜安(1983-),男,四川安岳人,副教授,博士,硕士生导师,研究方向为数字农业技术、农业资源与环境,(E-mail)zryanan@163.com
基金资助:
新疆维吾尔自治区青年科技创新人才培养工程(QN2016BS0705);新疆维吾尔自治区重点研发计划(2016B03041-1);新疆维吾尔自治区科技支疆项目计划(2019E0245)

Prediction of Cotton Plant Height Based on UAV Image

YAN An^1,2,3, GUO Tao^1,2, CHEN Quanjia⁴, GENG Hongwei⁴, GUO Bin⁴, SUN Fenglei⁴

1. College of Pratacultural and Environmental Sciences, Xinjiang Agricultural University, Urumqi 830052, China;
2. Xinjiang Key Laboratory of Soil and Plant Ecological Processes, Urumqi 830052, China;
3. Information Engineering Technology Research Center, Xinjiang Agricultural University, Urumqi 830052, China;
4. College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China

Received:2020-04-12 Online:2020-08-20 Published:2020-09-01
Correspondence author: Chen Quanjia(1972-),Male,from Urumqi,Xinjiang,Professor,Doctor,Doctoral supervisor,Research cirection is cotton genetics and breeding,(E-mail)chqjia@126.com
Supported by:
Supported by the Youth Science and Technology Innovation Talent Training Project of Xinjiang Uygur Autonomous Region (QN2016BS0705);The Key R & D Project of Xinjiang Uygur Autonomous Region(2016B03041-1);the Science and Technology Supporting Xinjiang Project of Xinjiang Uygur Autonomous Region(2019E0245)

摘要/Abstract

摘要： 【目的】 利用无人机遥感技术,快速、无损和高通量地获取田间株高表型信息,预测棉花品种(系)的长势监测及产量。【方法】 以无人机(UAV)搭载高清数码相机构成低空遥感平台,获取110份处于花铃期棉花品种(系)影像,测定地面实际株高;利用拼接软件与高清数码影像,生成研究区数字表面模型(DSM)和高清正射影像(DOM);基于高清的DOM和DSM,利用克里金插值法生成研究区离散地面高程值(DEM),经作差提取棉花株高(CHM),利用不同棉花品种(系)实测株高(H)与提取的棉花株高(CHM)作回归分析。【结果】 通过DOM可快速无损地监测花铃期各棉花品种(系)长势、叶色性状差异及分布状况,经DSM和克里金插值法提取的DEM和棉花株高分布图得出,研究区整体地势较平坦,高低落差仅0.5 m。所建株高模型R²达到0.846 9,验证模型R²也达到0.758 1。【结论】 利用无人机影像生成的DOM、DSM和克里金插值法生成的DEM,提取的棉花花铃期株高(CHM)精度较高,无人机搭载数码相机进行棉花株高测定具有较好的适用性。为大范围的棉花田间株高观测提供一种新的研究方法是可行的。

关键词: 无人机; 数码影像; 棉花; 株高; 新疆

Abstract: 【Objective】 This study aims to make use of the Unmanned Aerial Vehicle (UAV) remote sensing image technology platform to obtain the phenotypic information of field crop height through rapid, non-destructive and high-throughput methods, which is of great significance for the growth monitoring and yield prediction of cotton varieties (lines).【Method】 In this paper, UAV is equipped with high-definition digital camera to form a low-altitude remote sensing platform and low-altitude UAV remote sensing technology was used to obtain regional high-precision remote sensing images for 110 cotton varieties (lines) in the flowering and boll period planted in the cotton breeding test field base of Xinjiang Agricultural University, Shawan County, Xinjiang, and measured the actual plant height of the ground manually. Firstly, we used the Pix4D mapper and the high-definition digital image to generate the Digital Surface Model (DSM) and the Digital Orthophoto Map (DOM) of the study area. Then, based on DOM and high-definition DSM, the Kriging interpolation method was used to generate the discrete Digital Elevation Model (DEM) of the study area. The Crop Height Model (CHM) of the study area was obtained by the difference between the high-definition DSM and the discrete DEM of the study area. Finally, regression analysis was carried out using the measured plant height (H) of different varieties (lines) and the CHM of the extracted cotton.【Results】 The DOM could quickly and non-destructively monitor the growth, leaf color trait differences and distribution of cotton varieties (lines) at the flower-boll stage. According to the DEM and cotton plant height distribution maps extracted by DSM and Kriging interpolation, the overall terrain of the study area was relatively flat, and the height difference was only 0.5 m. The constructed plant height model R² reached 0.8469, and the verification model R² also reached 0.7581, indicating that the drone equipped with a digital camera had a good applicability for cotton plant height measurement.【Conclusion】 The DEM generated by DOM, DSM and Kriging interpolation method generated by UAV images is used to extract cotton plant height (CHM) at flowering stage, which reaches relatively high accuracy. It provides a new research method for large-scale observation of cotton field plant height, which proves that the experimental research method is feasible.

Key words: UAV; digital image; breeding; cotton; Xinjiang

中图分类号:

S252⁺.9

颜安, 郭涛, 陈全家, 耿洪伟, 郭斌, 孙丰磊. 基于无人机影像的棉花株高预测[J]. 新疆农业科学, 2020, 57(8): 1493-1502.

YAN An, GUO Tao, CHEN Quanjia, GENG Hongwei, GUO Bin, SUN Fenglei. Prediction of Cotton Plant Height Based on UAV Image[J]. Xinjiang Agricultural Sciences, 2020, 57(8): 1493-1502.

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基于无人机影像的棉花株高预测

Prediction of Cotton Plant Height Based on UAV Image

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