Quantifying Lodging Percentage and Lodging Severity Using a UAV-Based Canopy Height Model Combined with an Objective Threshold Approach

Unmanned aerial vehicles (UAVs) open new opportunities in precision agriculture and phenotyping because of their flexibility and low cost. In this study, the potential of UAV imagery was evaluated to quantify lodging percentage and lodging severity of barley using structure from motion (SfM) techniques. Traditionally, lodging quantification is based on time-consuming manual field observations. Our UAV-based approach makes use of a quantitative threshold to determine lodging percentage in a first step. The derived lodging estimates showed a very high correlation to reference data (R2 = 0.96, root mean square error (RMSE) = 7.66%) when applied to breeding trials, which could also be confirmed under realistic farming conditions. As a second step, an approach was developed that allows the assessment of lodging severity, information that is important to estimate yield impairment, which also takes the intensity of lodging events into account. Both parameters were tested on three ground sample distances. The lowest spatial resolution acquired from the highest flight altitude (100 m) still led to high accuracy, which increases the practicability of the method for large areas. Our new lodging assessment procedure can be used for insurance applications, precision farming, and selecting for genetic lines with greater lodging resistance in breeding research.

[1]  Martin J. Wooster,et al.  High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing , 2016, Remote. Sens..

[2]  John Spink,et al.  Predicting yield losses caused by lodging in wheat , 2012 .

[3]  Mark G. Eramian,et al.  Classification of Crop Lodging with Gray Level Co-occurrence Matrix , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[4]  Min Jiang,et al.  Estimates of rice lodging using indices derived from UAV visible and thermal infrared images , 2018 .

[5]  D. Mulla.  Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[6]  Marco Dubbini,et al.  Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments , 2013, Remote. Sens..

[7]  Juliane Bendig,et al.  Low-weight and UAV-based Hyperspectral Full-frame Cameras for Monitoring Crops: Spectral Comparison with Portable Spectroradiometer Measurements , 2015 .

[8]  Juliane Bendig,et al.  UAV-based Imaging for Multi-Temporal, very high Resolution Crop Surface Models to monitor Crop Growth Variability , 2013 .

[9]  Kevin P. Smith,et al.  An automatable, field camera track system for phenotyping crop lodging and crop movement , 2018 .

[10]  Ming-Der Yang,et al.  Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery , 2017, Remote. Sens..

[11]  R. A. Fischer,et al.  Lodging effects on high-yielding crops of irrigated semidwarf wheat , 1987 .

[12]  Koichi Amaha,et al.  Canopy height measurement by photogrammetric analysis of aerial images: Application to buckwheat (Fagopyrum esculentum Moench) lodging evaluation , 2012 .

[13]  Andreas Burkart,et al.  Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance , 2015 .

[14]  Erle C. Ellis,et al.  High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision , 2013 .

[15]  Marco Dubbini,et al.  Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images , 2015, Remote. Sens..

[16]  Simon Bennertz,et al.  Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Chunhua Zhang,et al.  The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.

[18]  Gary E. Varvel,et al.  Use of Remote-Sensing Imagery to Estimate Corn Grain Yield , 2001 .

[19]  Seth C. Murray,et al.  Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images , 2017, Remote. Sens..

[20]  F. López-Granados,et al.  Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat , 2013, Precision Agriculture.

[21]  T. Kraska,et al.  Phenological analysis of unmanned aerial vehicle based time series of barley imagery with high temporal resolution , 2018, Precision Agriculture.

[22]  A. R. Ennos,et al.  Understanding and Reducing Lodging in Cereals , 2004 .

[23]  T. Jarmer,et al.  Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data , 2015 .

[24]  H. Eisenbeiss,et al.  DIRECT GEOREFERENCING OF UAVS , 2012 .

[25]  J. Schjoerring,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[26]  Görres Grenzdörffer,et al.  THE PHOTOGRAMMETRIC POTENTIAL OF LOW-COST UAVs IN FORESTRY AND AGRICULTURE , 2008 .

[27]  Tomaz Podobnikar.  Methods for visual quality assessment of a digital terrain model , 2009 .

[28]  Muhammad Iqbal,et al.  The relationship between lodging and plant height in a diverse wheat population , 2006 .

[29]  L. Klingbeil,et al.  DEVELOPMENT AND EVALUATION OF A UAV BASED MAPPING SYSTEM FOR REMOTE SENSING AND SURVEYING APPLICATIONS , 2015 .

[30]  N. Pfeifer,et al.  QUALITY MEASURES FOR DIGITAL TERRAIN MODELS , 2004 .

[31]  Viacheslav I. Adamchuk,et al.  Soil Heterogeneity and Crop Growth , 2010 .

[32]  Jose A. Jiménez-Berni,et al.  Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping , 2014 .

[33]  Sebastian G. Elbaum,et al.  On crop height estimation with UAVs , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[34]  Zhen-lin Wang,et al.  Lodging resistance of winter wheat (Triticum aestivum L.): Lignin accumulation and its related enzymes activities due to the application of paclobutrazol or gibberellin acid , 2014 .

[35]  J. W. Pendleton,et al.  Effect of Artificial Lodging on Winter Wheat Grain Yield and Quality1 , 1964 .

[36]  Hans-Peter Piepho,et al.  Combining phenotypic data from ordinal rating scales in multiple plant experiments. , 2011, Trends in plant science.

[37]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

[38]  Clement Atzberger,et al.  Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs , 2013, Remote. Sens..

[39]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[40]  Zhilin Li,et al.  ON THE MEASURE OF DIGITAL TERRAIN MODEL ACCURACY , 2006 .

[41]  Luis Alonso,et al.  Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV Based Goniometer , 2015, Remote. Sens..

[42]  J. Bendig.  Unmanned aerial vehicles (UAVs) for multi-temporal crop surface modelling. A new method for plant height and biomass estimation based on RGB-imaging , 2015 .

[43]  Christian Hirt,et al.  Digital Terrain Models , 2015 .

[44]  R. D. Ramsey,et al.  Canopy Reflectance Estimation of Wheat Nitrogen Content for Grain Protein Management , 2004 .

[45]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[46]  Seth C. Murray,et al.  UAS imaging for automated crop lodging detection: a case study over an experimental maize field , 2017, Commercial + Scientific Sensing and Imaging.

[47]  Jarrod C Hodgson,et al.  Precision wildlife monitoring using unmanned aerial vehicles , 2016, Scientific Reports.

[48]  Arko Lucieer,et al.  An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds , 2012, Remote. Sens..

[49]  Moshe J. Pinthus,et al.  Lodging in Wheat, Barley, and Oats: The Phenomenon, its Causes, and Preventive Measures , 1974 .

[50]  Michael J. Starek,et al.  Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment , 2017 .

[51]  Simon Bennertz,et al.  Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging , 2014, Remote. Sens..

[52]  P. Berry,et al.  Predicting lodging in winter wheat , 1998 .

[53]  Rubens Augusto Camargo Lamparelli,et al.  Height estimation of sugarcane using an unmanned aerial system (UAS) based on structure from motion (SfM) point clouds , 2017 .

[54]  Joan Koss-Chioino.  Religion and spirituality in Latino life in the United States. , 2013 .

[55]  Ray D. Jackson,et al.  Estimation of Daily Evapotranspiration from one Time-of-Day Measurements , 1983 .