Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities

Spectral reflectance was used for classifying different types of samples.Features ranking method performed excellently in effective wavelengths selection.Early disease detection obtained the classification rate of 66.67%.Selected wavebands can be used for designing a polychromatic detection camera. This study used hyperspectral imaging technique to classify healthy and gray mold diseased tomato leaves. Hyperspectral images of diseased samples at 24h, 48h, 72h, 96h and 120h after inoculation and healthy samples were taken in the wave range of 3801023nm. A total of ten pixels from each sample were identified as the region of interest (ROI), and the mean reflectance values of ROI were calculated. The dependent variables of healthy samples were set as 0, and diseased samples were set as 1, 2, 3, 4 and 5 according to infection severities, respectively. K-nearest neighbor (KNN) and C5.0 models were built to classify the samples using the full wave band set. To reduce data volume, features ranking (FR) was used to select sensitive bands. Then, the KNN classification model was built based on just the selected bands. This later procedure of reducing spectral dimensionality and classifying infection stages was defined as FR-KNN. Performances of KNN classifier on all wave bands and FR-KNN were compared. The overall classification results in the testing sets were 61.11% for KNN, 54.17% for C5.0 and 45.83% for FR-KNN model. When differentiating infected samples from control, the testing results were 94.44%, 94.44% and 97.22% for each model, respectively. In addition, early disease detection (1dpi) obtained the results of 66.67% for KNN, 66.67% for C5.0 and 41.67% for FR-KNN. Therefore, it demonstrated that hyperspectral imaging has the potential to be used for early detection of gray mold disease on tomato leaves.

[1]  Jianwei Qin,et al.  Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method , 2008 .

[2]  Z. Niu,et al.  Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging , 2007, Precision Agriculture.

[3]  Roman M. Balabin,et al.  Gasoline classification using near infrared (NIR) spectroscopy data: comparison of multivariate techniques. , 2010, Analytica chimica acta.

[4]  Yong He,et al.  Spectrum and Image Texture Features Analysis for Early Blight Disease Detection on Eggplant Leaves , 2016, Sensors.

[5]  M. Badawy,et al.  Potential of the biopolymer chitosan with different molecular weights to control postharvest gray mold of tomato fruit , 2009 .

[6]  Yong He,et al.  Application of Time Series Hyperspectral Imaging (TS-HSI) for Determining Water Content within Tea Leaves during Drying , 2013 .

[7]  Robert I. Damper,et al.  A fast separability-based feature-selection method for high-dimensional remotely sensed image classification , 2008, Pattern Recognit..

[8]  D. Ingram,et al.  Managing Botrytis Gray Mold in Greenhouse Tomatoes Using Traditional and Bio-Fungicides , 2006 .

[9]  Onisimo Mutanga,et al.  Multispectral remote sensing for mapping grassland degradation using the key indicators of grass species and edaphic factors , 2016 .

[10]  W. Mahaffee,et al.  Variability Associated with Suppression of Gray Mold (Botrytis cinerea) on Geranium by Foliar Applications of Nonaerated and Aerated Compost Teas. , 2006, Plant disease.

[11]  Ting Yu,et al.  Biocontrol of postharvest gray mold of cherry tomatoes with the marine yeast Rhodosporidium paludigenum , 2010 .

[12]  Jens Myrup Pedersen,et al.  A method for classification of network traffic based on C5.0 Machine Learning Algorithm , 2012, 2012 International Conference on Computing, Networking and Communications (ICNC).

[13]  Won Suk Lee,et al.  Spectral Characteristics of Citrus Black Spot Disease , 2016 .

[14]  Michael E. Schaepman,et al.  Using spectral information from the NIR water absorption features for the retrieval of canopy water content , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[15]  Yong He,et al.  Different Algorithms for Detection of Malondialdehyde Content in Eggplant Leaves Stressed by Grey Mold Based on Hyperspectral Imaging Technique , 2015, Intell. Autom. Soft Comput..

[16]  Jordi-Roger Riba Ruiz,et al.  Comparative Study of Multivariate Methods to Identify Paper Finishes Using Infrared Spectroscopy , 2012, IEEE Transactions on Instrumentation and Measurement.

[17]  Chunjiang Zhao,et al.  Fast detection and visualization of early decay in citrus using Vis-NIR hyperspectral imaging , 2016, Comput. Electron. Agric..

[18]  Reza Ehsani,et al.  Review: A review of advanced techniques for detecting plant diseases , 2010 .

[19]  Sildomar T. Monteiro,et al.  Consistency of Measurements of Wavelength Position From Hyperspectral Imagery: Use of the Ferric Iron Crystal Field Absorption at $\sim$900 nm as an Indicator of Mineralogy , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[20]  L. Buydens,et al.  Predictive-property-ranked variable reduction in partial least squares modelling with final complexity adapted models: comparison of properties for ranking. , 2013, Analytica chimica acta.

[21]  Chu Zhang,et al.  Early Detection of Botrytis cinerea on Eggplant Leaves Based on Visible and Near-Infrared Spectroscopy , 2008 .

[22]  Nuria Aleixos,et al.  Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks , 2013, Food and Bioprocess Technology.

[23]  Paul D. Gader,et al.  Hyperspectral band selection for detecting different blueberry fruit maturity stages , 2014 .

[24]  Joanna Kaczmarek,et al.  Hyperspectral and Thermal Imaging of Oilseed Rape (Brassica napus) Response to Fungal Species of the Genus Alternaria , 2015, PloS one.

[25]  Won Suk Lee,et al.  Nondestructive Detection of Nitrogen in Chinese Cabbage Leaves Using VIS–NIR Spectroscopy , 2006 .

[26]  Farid Melgani,et al.  Nearest Neighbor Classification of Remote Sensing Images With the Maximal Margin Principle , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Yong He,et al.  Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging , 2015, Scientific Reports.

[28]  Rutvija Pandya,et al.  C5.0 Algorithm to Improved Decision Tree with Feature Selection and Reduced Error Pruning , 2015 .

[29]  Jianwei Qin,et al.  Estimation of citrus canker lesion size using hyperspectral reflectance imaging. , 2013 .

[30]  U. Małolepsza.  Induction of disease resistance by acibenzolar-S-methyl and o-hydroxyethylorutin against Botrytis cinerea in tomato plants , 2006 .

[31]  Gamal ElMasry,et al.  Application of NIR hyperspectral imaging for discrimination of lamb muscles , 2011 .

[32]  H. Shafri,et al.  Detection of stressed oil palms from an airborne sensor using optimized spectral indices , 2012 .

[33]  Adolfo Martínez Usó,et al.  Clustering-Based Hyperspectral Band Selection Using Information Measures , 2007, IEEE Transactions on Geoscience and Remote Sensing.