Spectrum and Image Texture Features Analysis for Early Blight Disease Detection on Eggplant Leaves

This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves.

[1]  Gamal ElMasry,et al.  Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef , 2012 .

[2]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

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

[4]  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..

[5]  Thomas F. Burks,et al.  Citrus black spot detection using hyperspectral image analysis , 2013 .

[6]  Fei Liu,et al.  Application of successive projections algorithm for variable selection to determine organic acids of plum vinegar. , 2009 .

[7]  M. Ngadi,et al.  Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry , 2007 .

[8]  Da-Wen Sun,et al.  Recent applications of image texture for evaluation of food qualities—a review , 2006 .

[9]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

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

[11]  Renfu Lu,et al.  Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging , 2013 .

[12]  He Yong.  Study on the Early Detection of Early Blight on Tomato Leaves Using Hyperspectral Imaging Technique Based on Spectroscopy and Texture , 2013 .

[13]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[14]  Xin Qin,et al.  Study of the feasibility of distinguishing cigarettes of different brands using an Adaboost algorithm and near-infrared spectroscopy , 2007, Analytical and bioanalytical chemistry.

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

[16]  Fei Liu,et al.  Application of Visible and Near Infrared Hyperspectral Imaging to Differentiate Between Fresh and Frozen–Thawed Fish Fillets , 2013, Food and Bioprocess Technology.

[17]  Yuan Yan Tang,et al.  ISABoost: A weak classifier inner structure adjusting based AdaBoost algorithm - ISABoost based application in scene categorization , 2013, Neurocomputing.

[18]  Gamal ElMasry,et al.  Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. , 2012, Analytica chimica acta.

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

[20]  Fei Liu,et al.  Ripeness Classification of Astringent Persimmon Using Hyperspectral Imaging Technique , 2014, Food and Bioprocess Technology.

[21]  Paul Allen,et al.  Prediction of moisture, color and pH in cooked, pre-sliced turkey hams by NIR hyperspectral imaging system , 2013 .

[22]  Yong He,et al.  Color Measurement of Tea Leaves at Different Drying Periods Using Hyperspectral Imaging Technique , 2014, PloS one.

[23]  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.

[24]  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.

[25]  Yong He,et al.  Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds , 2012, Sensors.

[26]  Wen-Chang Cheng,et al.  A self-constructing cascade classifier with AdaBoost and SVM for pedestriandetection , 2013, Eng. Appl. Artif. Intell..

[27]  Clement Atzberger,et al.  New ways to extract archaeological information from hyperspectral pixels , 2014 .

[28]  Anne-Katrin Mahlein,et al.  Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases , 2012, Plant Methods.

[29]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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