Abstract
The inspection of rice grain that may be infected by seedborne disease is important for ensuring uniform plant stands in production fields as well as preventing proliferation of some seedborne diseases. The goal of this study was to use a hyperspectral imaging (HSI) technique to find optimal wavelengths and develop a model for detecting discolored, diseased rice seed infected by bacterial panicle blight (Burkholderia glumae), a seedborne pathogen. For this purpose, the HSI data spanning the visible/near-infrared wavelength region between 400 and 1000 nm were collected for 500 sound and discolored rice seeds. For selecting optimal wavelengths to use for detecting diseased seed, a sequential forward selection (SFS) method combined with various spectral pretreatments was employed. To evaluate performance based on optimal wavelengths, support vector machine (SVM) and linear and quadratic discriminant analysis (LDA and QDA) models were developed for detection of discolored seeds. As a result, the violet and red regions of the visible spectrum were selected as key wavelengths reflecting the characteristics of the discolored rice seeds. When using only two or only three selected wavelengths, all of the classification methods achieved high classification accuracies over 90% for both the calibration and validation sample sets. The results of the study showed that only two to three wavelengths are needed to differentiate between discolored, diseased and sound rice, instead of using the entire HSI wavelength regions. This demonstrates the feasibility of developing a low cost multispectral imaging technology based on these selected wavelengths for non-destructive and high-throughput screening of diseased rice seed.
Highlights
Rice seeds are known to harbor endophytes along with numerous seedborne bacterial and fungal pathogens that can decrease plant stands in production fields and limit yield [1,2]
The present study demonstrated that, with two or three optimized wavelengths, it is possible to develop a highly accurate inspection system for detecting diseased rice grain, in this case likely caused by bacterial panicle blight (BPB), using the four discrimination methods
The spectral information from the region of interest (ROI) of the hyperspectral image were acquired and the classification models were developed by using support vector machine (SVM) and discriminant analysis
Summary
Rice seeds are known to harbor endophytes along with numerous seedborne bacterial and fungal pathogens that can decrease plant stands in production fields and limit yield [1,2]. Subjective visible assessment of panicles in the field or using post-harvested seeds for development of the discoloration and distinctive BPB symptoms is currently the only means to quantify incidence of the disease. Hyperspectral imaging (HSI) has been used for assessment fungal infection levels in rice panicles, which was previously performed by human visual surveys [9,10]. These subjective observations are tedious, time-consuming, and less accurate than HSI. Development of a rapid and nondestructive technique to accurately assess disease incidence in seed would enhance disease control research efforts and offer a means of high-throughput sorting of seed to assure healthy seed rice for planting, to prevent spread of the disease, and to assure plant stand establishment in fields
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