Quantifying Citrus Tree Health Using True Color UAV Images

Huanglongbing (HLB) and Phytophthora foot and root rot are diseases that affect citrus production and profitability. The symptoms and physiological changes associated with these diseases are diagnosed through expensive and time-consuming field measurements. Unmanned aerial vehicles (UAVs) using red/green/blue (RGB, true color) imaging, may be an economic alternative to diagnose diseases. A methodology using a UAV with a RGB camera was developed to assess citrus health. The UAV was flown in April 2018 on a grapefruit field infected with HLB and foot rot. Ten trees were selected for each of the following disease classifications: (HLB-, foot rot–), (HLB+, foot rot–), (HLB-, foot rot+) (HLB+, foot rot+). Triangular greenness index (TGI) images were correlated with field measurements such as tree nutritional status, leaf area, SPAD (leaf greenness), foot rot disease severity and HLB. It was found that 61% of the TGI differences could be explained by Na, Fe, foot rot, Ca, and K. This study shows that diseased citrus trees can be monitored using UAVs equipped with RGB cameras, and that TGI can be used to explain subtle differences in tree health caused by multiple diseases.

[1]  M. Kunta,et al.  Diverse DNA extraction methods and PCR primers for detection of Huanglongbing-associated bacteria from roots of ‘Valencia’ sweet orange on sour orange rootstock , 2014 .

[2]  N. Oppelt,et al.  Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data , 2004 .

[3]  Joe Mari Maja,et al.  Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards , 2011 .

[4]  Christopher Searle,et al.  Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis , 2018, Remote. Sens..

[5]  T. M. Little,et al.  AGRICULTURAL EXPERIMENTATION: DESIGN AND ANALYSIS , 1982 .

[6]  Hong Li.  Citrus Tree Abiotic and Biotic Stress and Implication of Simulation and Modeling Tools in Tree Management , 2009 .

[7]  Won Suk Lee,et al.  An optimum method for real-time in-field detection of Huanglongbing disease using a vision sensor , 2015, Comput. Electron. Agric..

[8]  Won Suk Lee,et al.  Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees , 2013 .

[9]  Reza Ehsani,et al.  Visible-near infrared spectroscopy based citrus greening detection: Evaluation of spectral feature extraction techniques , 2011 .

[10]  Tim R. Gottwald,et al.  Citrus Huanglongbing: the pathogen and its impact. , 2007 .

[11]  Jordi Bort,et al.  Use of RGB Vegetation Indexes in Assessing Early Effects of Verticillium Wilt of Olive in Asymptomatic Plants in High and Low Fertility Scenarios , 2019, Remote. Sens..

[12]  S. Nebiker,et al.  LIGHT-WEIGHT MULTISPECTRAL UAV SENSORS AND THEIR CAPABILITIES FOR PREDICTING GRAIN YIELD AND DETECTING PLANT DISEASES , 2016 .

[13]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[14]  A. Seid,et al.  Assessing and Measuring of Citrus gummosis (Phytophthora spp.) in Major Citrus Growing Areas of Ethiopia , 2015 .

[15]  S. O. Cacciola,et al.  Management Of Citrus Diseases Caused By Phytophthora Spp , 2008 .

[16]  Junho Yeom,et al.  Validation of agronomic UAV and field measurements for tomato varieties , 2019, Comput. Electron. Agric..

[17]  M. Louhaichi,et al.  Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat , 2001 .

[18]  L. Plümer,et al.  Development of spectral indices for detecting and identifying plant diseases , 2013 .

[19]  Wenbin Li,et al.  Quantitative real-time PCR for detection and identification of Candidatus Liberibacter species associated with citrus huanglongbing. , 2006, Journal of microbiological methods.

[20]  V. Kakani,et al.  Discriminant analysis of nitrogen treatments in switchgrass and high biomass sorghum using leaf and canopy-scale reflectance spectroscopy , 2016 .

[21]  Craig S. T. Daughtry,et al.  A visible band index for remote sensing leaf chlorophyll content at the canopy scale , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Reza Ehsani,et al.  Evaluation of an active optical sensor for detection of Huanglongbing (HLB) disease , 2011 .

[23]  E. Stover,et al.  Influence of Photoperiod Duration and Phloem Disruption through Scoring on Growth, Disease Symptoms, and Bacterial Titer in Citrus Graft Inoculated with Candidatus Liberibacter asiaticus , 2016 .

[24]  H. E. Hayward,et al.  Physiology of Salt Tolerance , 1958 .

[25]  C. Daughtry,et al.  Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index , 2011 .

[26]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[27]  Tomas Ayala-Silva,et al.  Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency. , 2005, Advances in space research : the official journal of the Committee on Space Research.

[28]  Y. Nouvellon,et al.  Influence of potassium and sodium nutrition on leaf area components in Eucalyptus grandis trees , 2013, Plant and Soil.

[29]  John L. Jifon,et al.  Growth Environment and Leaf Anatomy Affect Nondestructive Estimates of Chlorophyll and Nitrogen in Citrus sp. Leaves , 2005 .

[30]  Adhemar Pereira de Barros,et al.  HUANGLONGBING: A DESTRUCTIVE, NEWLY-EMERGING, CENTURY-OLD DISEASE OF CITRUS 1 , 2006 .

[31]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[32]  Chris M. Roelfsema,et al.  Trends in Remote Sensing Accuracy Assessment Approaches in the Context of Natural Resources , 2019, Remote. Sens..

[33]  Farhad Samadzadegan,et al.  UAV-based multispectral imagery for fast Citrus Greening detection , 2019, Journal of Plant Diseases and Protection.

[34]  Tung-Ching Su,et al.  Application of Multispectral Sensors Carried on Unmanned Aerial Vehicle (UAV) to Trophic State Mapping of Small Reservoirs: A Case Study of Tain-Pu Reservoir in Kinmen, Taiwan , 2015, Remote. Sens..