Corn ear test using SIFT-based panoramic photography and machine vision technology
Xinyi Zhang | Jiexin Liu | Huaibo Song
Corn ear test is important to modern corn breeding. The test indexes mainly include lengths, radiuses, rows and numbers of corn ears and the kernels they bear, which can benefit the study on breeding new and fine corn varieties. These corn traits are often collected by traditional manual measurement, which is difficult to meet the needs of high throughput corn ear test. In this study, image sequences of corn ear samples were captured by building a panoramic photography collecting system. And then, to get the lengths and radiuses indexes, the corn area images were processed based on Lab color space and adaptive threshold segmentation. The sequence images were then matched and the panoramic image of a corn surface were extracted using Scale-invariant feature transform (SIFT). Finally, by using Exponential transformation (ETR) and Sobel-Hough algorithm, ears and rows indexes were acquired. Test results showed that the accuracy of the radiuses and lengths were 93.84% and 94.53%, respectively. Meanwhile, the accuracy of kernels and rows indexes were 98.12% and 96.14%, which were 4.03% and 7.25% higher than that of common mosaiced panoramic image. And the accuracy of kernel area and length-width ratio were 95.36% and 97.42%, respectively. All the results showed that the proposed method can be used for corn ear test effectively.
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