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Estimation of plant height and above ground biomass of potato based on UAV digital image

  • role： First author第一作者
  • Affiliation：

    College of Surveying Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

    Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China

    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China

  • Email：liuyanghe810@163.com
  • Introduction：刘杨，1994年生，男，硕士研究生，研究方向为农业定量遥感。E-mail：liuyanghe810@163.com
LIU Yang123,  
  • Affiliation：

    College of Surveying Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

HUANG Jue1,  
  • Affiliation：

    Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China

    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China

SUN Qian23,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China

    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China

  • Email：fenghaikuan123@163.com
  • Introduction：冯海宽，1982年生，男，副研究员，研究方向为农业定量遥感。E-mail：fenghaikuan123@163.com
FENG Haikuan23*,  
  • Affiliation：

    Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China

    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China

YANG Guijun23,  
  • Affiliation：

    College of Civil Engineering, Henan University of Engineering, Zhengzhou 451191, China

YANG Fuqin4

Resümee

Plant height and Above-Ground Biomass (AGB) are important agronomic parameters for crop growth monitoring. Therefore, efficiently and timely acquire this information of potato plant is important for guiding farmland production management. Traditionally, manual actual surveys are time-consuming, laborious and destructive, and fail to meet the modern needs of smart agriculture. With the advancement of science and technology, remote sensing technology has attracted people’s attention for its advantages of non-destructive, high-throughput, and rapid acquisition of phenotype information of ground objects. Compared with satellite, aerial and ground remote sensing, UAV remote sensing technology is widely popularized in precision agriculture monitoring due to its strong mobility, simple operation, low operating cost, and the ability to obtain high-resolution digital orthophotos under the cloud. In this study, the UAV equipped with high-definition digital camera was used to obtain the image data of potato with budding periods, tuber formation period, tuber growth period, starch accumulation period and maturity period, and the height (H) and AGB of potato plant on the ground were measured, and the longitude, dimension and height of Ground Control Points (GCPs) were obtained by Global Positioning System (GPS) from March to July 2019. Firstly, the Digital Surface Model (DSM) was generated by structure from motion algorithm based on the image data of the experimental area and the location information of GCPs, and the Hdsm (potato plant height) of each growth period was extracted based on DSM. Then, combining 26 image indices with better performance in AGB monitoring based on the digital number value of the image, crop height of field survey by ruler (H) and crop height extracted based on DSM difference calculation (Hdsm) into a new data set. The first 7 indices and Hdsm based analyzing the correlation between these parameters (26 vegetation indices, H and Hdsm) and AGB were screened to construct the AGB estimation model of five growth periods. Finally, in order to further increase the variance of the different model, Multiple Linear Regression (MLR), Support Vector Machine (SVM) and Artificial Neural Network (ANN) are selected to build the AGB estimation model based on the sensitivity parameters. Through the quantitative analysis of the model, the optimal estimation model is selected for each growth period to monitor crop conditions. The results showed that: the extracted plant height (Hdsm) is fitted with the measured plant height (R2=0.86, RMSE=6.36cm, NRMSE=13.42%); the AGB estimation model was constructed by three different modeling methods in each growth period, in which the model by integrating with Hdsm into vegetation indices was better; it is found that the effect of MLR model (R2 =0.61, 0.74, 0.77, 0.72 and 0.60) with incorporating the Hdsm into image indices in each growth period to estimate AGB is better than that of SVM (R2=0.60, 0.69, 0.73, 0.69 and 0.58) and ANN (R2=0.56, 0.67, 0.71, 0.65 and 0.55). The results of this research help solve the problem of monitoring AGB in the traditional way and provide reference for real-time monitoring of potato growth and yield prediction accurately.

Schlüsselwort

unmanned aerial vehicle;digital image;digital surface model;potato;plant height;above-ground biomass

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