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  • Author or Editor: Won Suk Lee x
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Citrus black spot (CBS) is a fungal disease caused by Phyllosticta citricarpa (synonym Guignardia citricarpa). CBS causes fruit lesions and significant yield loss in all citrus (Citrus) species. The most distinguishing CBS symptom is called hard spot, which is a circular lesion with gray tissue at the center surrounded by a black margin. The spectral characteristic of CBS lesions was investigated and compared with the spectral signature of healthy fruit tissue to determine the best distinguishing wave band. Healthy and CBS-affected samples presented similar reflectance below 500 nm and above 900 nm. However, healthy samples reflected more light between 500 and 900 nm, especially within the visible band. Also, spectral reflectance of the same symptomatic lesion was acquired six times over a 2-month period to determine the variation of symptom’s spectral signatures over time after being harvested. A two-sample t test was employed to compare each pair of consecutive repetitions. The results showed that the spectral signature of the CBS lesion did not change significantly over 2 months. The wavelengths between 587 and 589 nm were identified as the distinguishing band to develop a monochrome vision–based sensor for CBS diagnosis. A support vector machine (SVM) classifier was trained using the spectral reflectance data at the selected bands to identify CBS-affected samples in each repetition. The overall CBS detection accuracies varied between 93.3% and 94.6%.

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Proper nutrient management is essential to increase yield, quality and profit. This study was conducted to estimate the N concentrations of chinese cabbage (Brassica campestris L. ssp. pekinensis `Norangbom') plug seedlings using visible and near infrared spectroscopy for nondestructive N detection. Chinese cabbage seeds were sown and raised in three 200-cell plug trays filled with growing mixture in a plant growth chamber with three different levels (40%, 80%, and 100%) of required N. Reflectance for leaves of chinese cabbage seedlings was measured with a spectrophotometer 15 days after the experiment started. Reflectance was measured in the 400 to 2500 nm wavelength range at 1.1-nm increments. The leaves were dried afterwards to measure their water content and were analyzed for their actual N contents. The experiment was repeated twice (group I and II). Correlation coefficient spectrum, standard deviation spectrum, stepwise multiple linear regression (SMLR), and partial least squares (PLS) regression were used to determine wavelengths for N prediction models. Performances of SMLR and PLS were similar. For the validation data set (group II), SMLR produced an r 2 of 0.846 and PLS yielded r 2 of 0.840. The most significant wavelength 710 nm, which was identified by all methods, was correlated to chlorophyll. Water content positively correlated with N concentration (r = 0.76). Wavelengths of 1467, 1910, and 1938 nm selected by SMLR from both groups also showed that water had a strong effect on N prediction. Wavelengths near 2136 nm indicated that protein had potential use in N prediction. Wavelengths near 550 and 840 nm could also contribute to N prediction.

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