Using hyperspectral imaging technology to identify diseased tomato leaves

In the process of tomato plants growth, due to the effect of plants genetic factors, poor environment factors, or disoperation of parasites, there will generate a series of unusual symptoms on tomato plants from physiology, organization structure and external form, as a result, they cannot grow normally, and further to influence the tomato yield and economic benefits. Hyperspectral image usually has high spectral resolution, not only contains spectral information, but also contains the image information, so this study adopted hyperspectral imaging technology to identify diseased tomato leaves, and developed a simple hyperspectral imaging system, including a halogen lamp light source unit, a hyperspectral image acquisition unit and a data processing unit. Spectrometer detection wavelength ranged from 400nm to 1000nm. After hyperspectral images of tomato leaves being captured, it was needed to calibrate hyperspectral images. This research used spectrum angle matching method and spectral red edge parameters discriminant method respectively to identify diseased tomato leaves. Using spectral red edge parameters discriminant method produced higher recognition accuracy, the accuracy was higher than 90%. Research results have shown that using hyperspectral imaging technology to identify diseased tomato leaves is feasible, and provides the discriminant basis for subsequent disease control of tomato plants.

[1]  J. Gómez-Sanchís,et al.  Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables , 2011 .

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

[3]  Tang Hai,et al.  Application of Discriminant Analysis in Distinguishing Plant Photosynthetic Types—A Case Study in Northeast China Transect (NECT) Area , 1999 .

[4]  M. Cho,et al.  A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method , 2006 .

[5]  Zhou Da-ke A Modified Lanear Discriminant Analysis and Its Application to Face Recognition , 2005 .

[6]  Yibin Ying,et al.  Near-infrared Spectroscopy in detecting Leaf Miner Damage on Tomato Leaf , 2007 .

[7]  Yu-Zhou Du,et al.  Genetic Variation of Host Populations of Liriomyza sativae Blanchard , 2008 .

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

[9]  D. Binkley,et al.  Rates of free-living nitrogen fixation in some Piedmont forest types , 1987 .

[10]  Michael D. Steven,et al.  High resolution derivative spectra in remote sensing , 1990 .

[11]  Won Suk Lee,et al.  Original paper: Diagnosis of bacterial spot of tomato using spectral signatures , 2010 .

[12]  A. Skidmore,et al.  MERIS and the red-edge position , 2001 .

[13]  Jingcheng Zhang,et al.  Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects , 2014 .

[14]  Yang Linzhang,et al.  Comparative study on estimation of chlorophyll content in spinach leaves using various red edge position extraction techniques , 2008 .

[15]  Zhao Wei-zhong,et al.  The Classification of Natural Types and the Charateristic Discriminatory Analysis of Pinus tabulaeformis in Shanxi , 1994 .