Multitemporal Chlorophyll Mapping in Pome Fruit Orchards from Remotely Piloted Aircraft Systems

Early and precise spatio-temporal monitoring of tree vitality is key for steering management decisions in pome fruit orchards. Spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while manned aircraft sensor-platform systems are very expensive. In order to address the shortcomings of these platforms, this study investigates the potential of Remotely Piloted Aircraft Systems (RPAS) to facilitate rapid, low cost, and flexible chlorophyll monitoring. Due to the complexity of orchard scenery a robust chlorophyll retrieval model on RPAS level has not yet been developed. In this study, specific focus therefore lies on evaluating the sensitivity of retrieval models to confounding factors. For this study, multispectral and hyperspectral imagery was collected over pome fruit orchards. Sensitivities of both univariate and multivariate retrieval models were demonstrated under different species, phenology, shade, and illumination scenes. Results illustrate that multivariate models have a significantly higher accuracy than univariate models as the former provide accuracies for the canopy chlorophyll content retrieval of R2 = 0.80 and Relative Root Mean Square Error (RRMSE) = 12% for the hyperspectral sensor. Random forest regression on multispectral imagery (R2 > 0.9 for May, June, July, and August, and R2 = 0.5 for October) and hyperspectral imagery (0.6 < R2 < 0.9) led to satisfactory high and consistent accuracies for all months.

[1]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[2]  S. Singh Apple , 2020, Luxury and Fashion Marketing.

[3]  Cardona Alzate,et al.  Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas , 2020 .

[4]  Senén Barro,et al.  An extensive experimental survey of regression methods , 2019, Neural Networks.

[5]  Quan Wang,et al.  Informative bands used by efficient hyperspectral indices to predict leaf biochemical contents are determined by their relative absorptions , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[6]  Ben Somers,et al.  Urban tree health assessment using airborne hyperspectral and LiDAR imagery , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[7]  Maggi Kelly,et al.  Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks , 2018, Drones.

[8]  Stuart R. Phinn,et al.  Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications , 2018, Remote. Sens..

[9]  G. Gong,et al.  The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features , 2018, NeuroImage.

[10]  Achim Walter,et al.  Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[11]  Gang Liu,et al.  Prediction of Chlorophyll Content in Different Light Areas of Apple Tree Canopies based on the Color Characteristics of 3D Reconstruction , 2018, Remote. Sens..

[12]  Shujing Cao,et al.  Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging , 2018, Scientific Reports.

[13]  Helge Aasen,et al.  Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers – From theory to application , 2018 .

[14]  Gustau Camps-Valls,et al.  Advances in Kernel Machines for Image Classification and Biophysical Parameter Retrieval , 2018 .

[15]  Jon Atli Benediktsson,et al.  Mathematical Models and Methods for Remote Sensing Image Analysis: An Introduction , 2018 .

[16]  Raul Morais,et al.  Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry , 2017, Remote. Sens..

[17]  I. Farrera,et al.  A global evaluation of apple flowering phenology models for climate adaptation , 2017 .

[18]  Luís Pádua,et al.  UAS, sensors, and data processing in agroforestry: a review towards practical applications , 2017 .

[19]  Stéphane Jacquemoud,et al.  PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle , 2017 .

[20]  José F. Moreno,et al.  Spectral band selection for vegetation properties retrieval using Gaussian processes regression , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[21]  Gustau Camps-Valls,et al.  Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval , 2016, IEEE Geoscience and Remote Sensing Letters.

[22]  Laurent Tits,et al.  Viewing Geometry Sensitivity of Commonly Used Vegetation Indices towards the Estimation of Biophysical Variables in Orchards , 2016, J. Imaging.

[23]  M. Mõttus,et al.  Retrieval of leaf chlorophyll content in field crops using narrow-band indices: effects of leaf area index and leaf mean tilt angle , 2015 .

[24]  Jan G. P. W. Clevers,et al.  Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review , 2015 .

[25]  Lala Septem Riza,et al.  frbs: Fuzzy Rule-Based Systems for Classification and Regression in R , 2015 .

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

[27]  David Nuyttens,et al.  Spray deposition profiles in pome fruit trees: Effects of sprayer design, training system and tree canopy characteristics , 2015 .

[28]  Juha Suomalainen,et al.  A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles , 2014, Remote. Sens..

[29]  Achim Zeileis,et al.  evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R , 2014 .

[30]  S. Ercişli,et al.  The Effect of Water Stress on Some Morphological, Physiological, and Biochemical Characteristics and Bud Success on Apple and Quince Rootstocks , 2014, TheScientificWorldJournal.

[31]  Laurent Tits,et al.  Stem Water Potential Monitoring in Pear Orchards through WorldView-2 Multispectral Imagery , 2013, Remote. Sens..

[32]  José F. Moreno,et al.  Gaussian Process Retrieval of Chlorophyll Content From Imaging Spectroscopy Data , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[33]  Luis Alonso,et al.  Retrieval of Vegetation Biophysical Parameters Using Gaussian Process Techniques , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Luis Alonso,et al.  Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3 , 2012 .

[35]  M. Blanke,et al.  EFFECTS OF GLOBAL CLIMATE CHANGE ON APPLE 'GOLDEN DELICIOUS' PHENOLOGY - BASED ON 50 YEARS OF METEOROLOGICAL AND PHENOLOGICAL DATA IN KLEIN-ALTENDORF , 2011 .

[36]  Tong Zhang,et al.  Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations , 2011, IEEE Transactions on Information Theory.

[37]  Ben Somers,et al.  The contribution of the fruit component to the hyperspectral citrus canopy signal. , 2010 .

[38]  Nan Yang,et al.  The optimization of the crop chlorophyll content indices based on a new LAI determination index , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[39]  W. Verstraeten,et al.  Nonlinear Hyperspectral Mixture Analysis for tree cover estimates in orchards , 2009 .

[40]  John R. Miller,et al.  Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection , 2009 .

[41]  S. Delalieux,et al.  Hyperspectral indices to diagnose leaf biotic stress of apple plants, considering leaf phenology , 2009 .

[42]  M. Archetti Classification of hypotheses on the evolution of autumn colours , 2009 .

[43]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[44]  W. Verstraeten,et al.  The impact of common assumptions on canopy radiative transfer simulations: A case study in Citrus orchards , 2009 .

[45]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[46]  Jackson Adriano Albuquerque,et al.  Yield and fruit quality of apple from conventional and organic production systems , 2008 .

[47]  W. Verstraeten,et al.  A near-infrared narrow-waveband ratio to determine Leaf Area Index in orchards , 2008 .

[48]  Alan R. Gillespie,et al.  Interpretation and topographic compensation of conifer canopy self-shadowing , 2008 .

[49]  Eileen M. Perry,et al.  Spectral and spatial differences in response of vegetation indices to nitrogen treatments on apple , 2007 .

[50]  F. Stampar,et al.  Influence of nitrogen on leaf chlorophyll content and photosynthesis of ‘Golden Delicious’ apple , 2007 .

[51]  N. Hughes,et al.  Coordination of anthocyanin decline and photosynthetic maturation in juvenile leaves of three deciduous tree species. , 2007, The New phytologist.

[52]  G. A. Blackburn,et al.  Hyperspectral remote sensing of plant pigments. , 2006, Journal of experimental botany.

[53]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[54]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[55]  J. Hill,et al.  Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics , 2005 .

[56]  T. Baugher,et al.  Concise Encyclopedia of Temperate Tree Fruit , 2005 .

[57]  Kurt Hornik,et al.  kernlab - An S4 Package for Kernel Methods in R , 2004 .

[58]  R. Tibshirani,et al.  REJOINDER TO "LEAST ANGLE REGRESSION" BY EFRON ET AL. , 2004, math/0406474.

[59]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[60]  S. Sathiya Keerthi,et al.  Evaluation of simple performance measures for tuning SVM hyperparameters , 2003, Neurocomputing.

[61]  Christina Gloeckner,et al.  Modern Applied Statistics With S , 2003 .

[62]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[63]  J. Friedman Stochastic gradient boosting , 2002 .

[64]  H. Lichtenthaler,et al.  Chlorophylls and Carotenoids: Measurement and Characterization by UV‐VIS Spectroscopy , 2001 .

[65]  A. Gitelson,et al.  Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves¶ , 2001, Photochemistry and photobiology.

[66]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[67]  Arthur E. Hoerl,et al.  Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.

[68]  P. Thenkabail,et al.  Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .

[69]  L. C. Grappadelli,et al.  Measurement and Modeling of Carbon Balance of the Apple Tree , 1997 .

[70]  S. Chiu Method and software for extracting fuzzy classification rules by subtractive clustering , 1996, Proceedings of North American Fuzzy Information Processing.

[71]  A. Gitelson,et al.  Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm , 1996 .

[72]  B. Schaffer,et al.  Handbook of Environmental Physiology of Fruit Crops , 1994 .

[73]  J. Peñuelas,et al.  The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. , 1994 .

[74]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[75]  J. L. Hodges,et al.  Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties , 1989 .

[76]  Timo Pukkala,et al.  Simulation of within-tree and between-tree shading of direct radiation in a forest canopy: effect of crown shape and sun elevation , 1989 .

[77]  J. Friedman,et al.  Projection Pursuit Regression , 1981 .

[78]  H. Jonkers Autumnal leaf abscission in apple and pear. , 1980 .

[79]  R. R. Hocking The analysis and selection of variables in linear regression , 1976 .

[80]  J. A. Schell,et al.  Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. [Great Plains Corridor] , 1973 .

[81]  P. Spencer,et al.  Apple leaf senescence: leaf disc compared to attached leaf. , 1973, Plant physiology.