Laboratory and UAV-Based Identification and Classification of Tomato Yellow Leaf Curl, Bacterial Spot, and Target Spot Diseases in Tomato Utilizing Hyperspectral Imaging and Machine Learning

[1]  Xin Zhang,et al.  A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images , 2019, Remote. Sens..

[2]  Reza Ehsani,et al.  Detection and Differentiation between Laurel Wilt Disease, Phytophthora Disease, and Salinity Damage Using a Hyperspectral Sensing Technique , 2016 .

[3]  Josep Peñuelas,et al.  Visible and near-infrared reflectance techniques for diagnosing plant physiological status , 1998 .

[4]  Lucas Costa,et al.  A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms , 2020, Comput. Electron. Agric..

[5]  Bruce Thompson,et al.  Stepwise Regression and Stepwise Discriminant Analysis Need Not Apply here: A Guidelines Editorial , 1995 .

[6]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[7]  Andrea Luvisi,et al.  iPathology: Robotic Applications and Management of Plants and Plant Diseases , 2017 .

[8]  Lorraine Remer,et al.  Detection of forests using mid-IR reflectance: an application for aerosol studies , 1994, IEEE Trans. Geosci. Remote. Sens..

[9]  Reza Ehsani,et al.  Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado , 2018, Comput. Electron. Agric..

[10]  Alessandra Toncelli,et al.  THz Water Transmittance and Leaf Surface Area: An Effective Nondestructive Method for Determining Leaf Water Content , 2019, Sensors.

[11]  Chris Brien,et al.  The Development of Hyperspectral Distribution Maps to Predict the Content and Distribution of Nitrogen and Water in Wheat (Triticum aestivum) , 2019, Front. Plant Sci..

[12]  Cristina E. Davis,et al.  Advanced methods of plant disease detection. A review , 2014, Agronomy for Sustainable Development.

[13]  Yiannis Ampatzidis,et al.  Citrus rootstock evaluation utilizing UAV-based remote sensing and artificial intelligence , 2019, Comput. Electron. Agric..

[14]  S. Elvira,et al.  A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants , 1992 .

[15]  Yiannis Ampatzidis,et al.  UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning , 2019, Remote. Sens..

[16]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[17]  C. Jordan Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[18]  K. Omasa,et al.  Estimation of the leaf chlorophyll content using multi-angular spectral reflectance factor. , 2019, Plant, cell & environment.

[19]  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 .

[20]  P. Ginzburg,et al.  Biological Kerker effect boosts light collection efficiency in plants. , 2019, Nano letters.

[21]  L. Rustioni,et al.  Iron, magnesium, nitrogen and potassium deficiency symptom discrimination by reflectance spectroscopy in grapevine leaves , 2018, Scientia Horticulturae.

[22]  Andrea Luvisi,et al.  Plant Pathology and Information Technology: Opportunity for Management of Disease Outbreak and Applications in Regulation Frameworks , 2016 .

[23]  Rei Sonobe,et al.  Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments , 2018, Biosystems Engineering.

[24]  A. Gitelson,et al.  Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll , 1996 .

[25]  Reza Ehsani,et al.  Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor , 2018, Scientific Reports.

[26]  Andrew P French,et al.  Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress , 2017, Plant Methods.

[27]  Moon S. Kim,et al.  Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves , 1992 .

[28]  Daniel S. Falster,et al.  Detecting myrtle rust (Austropuccinia psidii) on lemon myrtle trees using spectral signatures and machine learning , 2018 .

[29]  M. Daub,et al.  The Photoactivated Cercospora Toxin Cercosporin: Contributions to Plant Disease and Fundamental Biology. , 2000, Annual review of phytopathology.

[30]  Gérard Dedieu,et al.  On the Potentiality of UAV Multispectral Imagery to Detect Flavescence dorée and Grapevine Trunk Diseases , 2018, Remote. Sens..

[31]  C. J. Huberty,et al.  Issues in the use and interpretation of discriminant analysis , 1984 .

[32]  Predicting macroalgal pigments (chlorophyll a, chlorophyll b, chlorophyll a + b, carotenoids) in various environmental conditions using high-resolution hyperspectral spectroradiometers , 2018 .

[33]  Lei Zhang,et al.  Detection of peanut leaf spots disease using canopy hyperspectral reflectance , 2019, Comput. Electron. Agric..

[34]  Yiannis Ampatzidis,et al.  UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence , 2019, Remote. Sens..

[35]  G. A. Blackburn,et al.  Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves , 1998 .

[36]  Rebecca L. Whetton,et al.  Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 1: Laboratory study , 2018 .

[37]  William R. Raun,et al.  Spectral Reflectance to Estimate Genetic Variation for In-Season Biomass, Leaf Chlorophyll, and Canopy Temperature in Wheat , 2006 .

[38]  L. Plümer,et al.  Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with Support Vector Machines , 2011 .

[39]  Andrea Luvisi,et al.  Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence , 2019, Comput. Electron. Agric..

[40]  D. Moshou,et al.  The potential of optical canopy measurement for targeted control of field crop diseases. , 2003, Annual review of phytopathology.

[41]  Yiannis Ampatzidis,et al.  Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques , 2019, Precision Agriculture.

[42]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[43]  Amr H. Abd-Elrahman,et al.  A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses , 2019, Comput. Electron. Agric..

[44]  Yiannis Ampatzidis,et al.  Finite Difference Analysis and Bivariate Correlation of Hyperspectral Data for Detecting Laurel Wilt Disease and Nutritional Deficiency in Avocado , 2019, Remote. Sens..

[45]  Oksana Sytar,et al.  Evaluation of Hyperspectral Reflectance Parameters to Assess the Leaf Water Content in Soybean , 2019, Water.

[46]  W. Verhoef,et al.  Using reflectance to explain vegetation biochemical and structural effects on sun-induced chlorophyll fluorescence , 2019, Remote Sensing of Environment.

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

[48]  John B. Solie,et al.  In‐Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy Reflectance , 2001 .

[49]  Paul D. Gader,et al.  Unsupervised hyperspectral band selection for apple Marssonina blotch detection , 2018, Comput. Electron. Agric..

[50]  Yiannis Ampatzidis,et al.  Automated vision-based system for monitoring Asian citrus psyllid in orchards utilizing artificial intelligence , 2019, Comput. Electron. Agric..