Detection of pests and diseases in vegetable crops using hyperspectral sensing: a comparison of reflectance data for different sets of symptoms

The aim of the study was to examine the potential of hyperspectral sensing to detect the incidence of pests and diseases in vegetable crops. The specific objectives were: a) to test if symptoms of pests and diseases of vegetable crops can be detected by hyperspectral sensing, b) to determine the best spectral bands relevant to pest and disease detection, and c) to compare the spectral responses obtained from the symptoms of two different pest and disease. Using a handheld spectrometer, sample measurements of diseased/infested and healthy leaves were collected separately from tomato and eggplant crops. The tomato crops were affected by a fungal “early blight” disease (Alternaria solani), with symptoms characterised by a yellowing or chlorosis of leaves. Conversely, the eggplants exhibited skeletal interveinal damage (“holes”) on leaves, caused by the 28-spotted ladybird (Epilachna vigintioctopunctata). To overcome the problems of using traditional regression techniques, the Partial Least Squares (PLS) regression technique was used. The cross-validated results showed that the incidence of disease on tomato and pest on eggplant could be predicted with 82% accuracy. Different sets of symptoms (i.e. chlorosis vs. loss of leaf area) provided different sets of optimum number of principal components and significant predictor variables. The most significant spectral bands for the tomato disease prediction corresponded to the reflectance red-edge (690nm-720nm), as well as the visible region (400nm700nm), and part of near-infrared wavelengths (735nm-1142nm). For the eggplant, the near-infrared region (particularly the bands 732nm-829nm) was identified by the regression model to be as equally significant as the red-edge (694nm716nm) in the prediction. However, the inclusion of the shortwave infrared bands (1590nm-1766nm) as significant variables in the eggplant regression model has indicated the contributing role of other factors. Results such as these confirmed the utility of hyperspectral data to diagnose pests and diseases of vegetables that can improve detection speed and provide opportunity for non-destructive sampling.

[1]  B. S. Ausmus,et al.  Reflectance studies of healthy, maize dwarf mosaic virus-infected, and Helminthosporium maydis-infected corn leaves , 1971 .

[2]  A. Gaunt,et al.  An assessment of the potential economic impact of Thrips palmi on horticulture in England and the significance of a successful eradication campaign. , 2004 .

[3]  Elizabeth Pattey,et al.  Narrowband vegetation indexes and detection of disease damage in soybeans , 2004, IEEE Geoscience and Remote Sensing Letters.

[4]  H. Nilsson.  Remote sensing and image analysis in plant pathology. , 1995, Annual review of phytopathology.

[5]  S. M. de Jong,et al.  Imaging spectrometry : basic principles and prospective applications , 2001 .

[6]  Sukumar Chakraborty,et al.  Recent Developments in Methods for Assessing Disease Losses in Forage/Pasture Crops , 1996 .

[7]  Armando Apan,et al.  Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery , 2004 .

[8]  S. Ustin,et al.  Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing , 2003 .

[9]  H. Gausman,et al.  LEAF REFLECTANCE OF NEAR-INFRARED , 1974 .

[10]  Robert W. Toler,et al.  Use of Aerial Color Infrared Photography to Evaluate Crop Disease , 1981 .

[11]  Doyle.  Plant Pathology and Plant Pathogens , 1999 .

[12]  Lalit Kumar,et al.  Imaging Spectrometry and Vegetation Science , 2001 .

[13]  P. Walker.  Crop losses: The need to quantify the effects of pests, diseases and weeds on agricultural production , 1983 .

[14]  G. Tatchell.  An estimate of the potential economic losses to some crops due to aphids in Britain , 1989 .

[15]  M. Bauer,et al.  Effects of Cultural Practices on Agronomic and Reflectance Characteristics of Soybean Canopies1 , 1982 .

[16]  R. Colwell.  Determining the prevalence of certain cereal crop diseases by means of aerial photography , 1956 .

[17]  M. S. Moran,et al.  Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .

[18]  W. R. Windham,et al.  Exploring the relationship between reflectance red edge and chlorophyll concentration in slash pine leaves. , 1995, Tree physiology.