浙江农业学报 ›› 2019, Vol. 31 ›› Issue (6): 977-985.DOI: 10.3969/j.issn.1004-1524.2019.06.16

• 生物系统工程 • 上一篇    下一篇

基于无人机的水肥一体化玉米出苗率估算方法与试验

刘志, 贺正, 苗芳芳, 贾彪*   

  1. 宁夏大学 农学院,宁夏 银川 750021
  • 收稿日期:2018-11-13 出版日期:2019-06-25 发布日期:2019-06-26
  • 通讯作者: 贾彪,E-mail:jiabiao2008@163.com
  • 作者简介:刘志(1994—),男,黑龙江哈尔滨人,硕士研究生,研究方向为作物高产高效优质栽培。E-mail: liuzhi1632018@163.com
  • 基金资助:
    国家自然科学基金(31560339); 宁夏高等学校科研项目(NGY2017025); 宁夏高等学校一流学科建设项目(NXYLXK2017A01)

Method and experiment for estimating emergence rate of water and fertilizer integrated maize based on drone technology

LIU Zhi, HE Zheng, MIAO Fangfang, JIA Biao*   

  1. School of Agriculture, Ningxia University, Yinchuan 750021, China
  • Received:2018-11-13 Online:2019-06-25 Published:2019-06-26

摘要: 出苗率是西北地区春播玉米夺得高产的前提保障。针对宁夏大面积玉米种植过程中人工统计出苗状况工程量大、耗时费力、误查漏数等现象。本文设计不同氮素处理试验,运用无人机搭载数码相机获取玉米苗期高清图像,运用MATLAB软件中ORB算法与距离加权融合算法合成无人机图像,通过二值化、腐蚀膨胀等深度优化处理技术得出玉米苗期图像轮廓,然后运用MATLAB软件八位连通域和ARCMAP 10.3计算方法自动规划路线并计算出玉米的出苗数量。结合田间人工调查数据,采用线性回归分析方法,建立了人工计数和无人机获取玉米出苗株数之间的线性关系模型。结果表明,线性回归关系模型的决定系数、均方根误差和标准均方根误差分别为0.895、4.359和2.436%。因此,基于低空无人机平台快速获取大田玉米出苗株数,是一种省时省力、高效精准的出苗率获取方法。可为后续玉米高产的准确评估提供技术支持,对于优化宁夏玉米滴灌水肥一体化精准种植技术具有积极意义。

关键词: 玉米, 出苗率, 无人机, 图像合成, 图像识别, 图像处理, 回归分析

Abstract: The seedling emergence rate is the prerequisite and guarantee for high yield of spring-sown maize in Northwest China. In view of the phenomenon of heavy workload, time-consuming, labor-consuming and error leakage by manual statistics in the process of large-scale cultivation of maize in Ningxia, different nitrogen treatment experiments were designed, which used a drone equipped with a digital camera to obtain HD images of maize seedlings. The ORB algorithm in MATLAB software and distance-weighted fusion algorithm were used to synthesize unmanned aerial vehicle images. The image outlines of maize seedlings were obtained through depth optimization techniques such as binarization and corrosion expansion, and then automatically planned routes and calculated using MATLAB eight-bit connected domain and ARCMAP 10.3 calculation method. The number of maize emergence was routed and calculated. Based on the field artificial survey data, a linear regression analysis method was introduced to establish a linear relationship model between manual counting and the number of maize seedlings obtained by drones. The results showed that the determination coefficient of the linear regression relationship model, root mean square error and normalized root mean square error were 0.895, 4.359 and 2.436%, respectively. Therefore, based on the low-altitude drone platform, the rapid acquisition of the number of emergence of maize in the field was a time-saving, efficient and accurate method for obtaining seedling rate. It could provide technical support for the accurate assessment of the subsequent high yield of maize, and was of great significance for optimizing the precise planting technology of water and fertilizer integration of maize in Ningxia.

Key words: maize, emergence rate, drone, image synthesis, image recognition, image processing, regression analysis

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