Detection of cold stressed maize seedlings for high throughput phenotyping using hyperspectral imagery

Hyperspectral imaging can provide hundreds of images at different wave bands covering the visible and near infrared regions, which is superior to traditional spectral and RGB techniques. Minnesota produced a lot of maize every year, while the temperature in Minnesota can change abruptly during spring. This study was carried out to use hyperspectral imaging technique to identify maize seedlings with cold stress prior to having visible phenotypes. A total of 60 samples were scanned by the hyperspectral camera at the wave range of 395-885 nm. The spectral reflectance information was extracted from the corrected hyperspectral images. By spectral reflectance information, support vector machine (SVM) classification models were established to identify the cold stressed samples. Then, the wavelengths which could play significant roles for the detection were selected using two-wavelength combination method. The classifiers were built again using the selected wavelengths. From the results, it can be found the selected wavelengths can even perform better than full wave range. The overall results indicated that hyperspectral imaging has the potential to classify cold stress symptoms in maize seedlings and thus help in selecting the corn genome lines with cold stress resistance.