Abstract
Abstract At present, the identifying of rice nitrogen stress by the chemical analysis is time-consuming and laborious. Machine vision technology can be used to non-destructively and rapidly identify rice nitrogen status, but image acquisition via digital camera is vulnerable to external conditions, and the images are of poor quality. In this research static scanning technology was used to collect images of the rice ’s top-three leaves that were fully expand in 4 growth periods. From those images, 14 spectral and shape characteristic parameters were extracted by R, G, B mean value function and Regionprops function in MATLAB. After analyzing, the R, G, Leaf Length, Leaf Area, and Leaf Perimeter were chosen as 5 universal characteristic parameters for identifying nitrogen stress in 4 growth periods.The results showed that the overall recognition accuracy of nitrogen stress were 92%, 92%, 100% and 96% respectively. Based on the result, the methodology developed in the study is capable of identifying nitrogen stress accurately in the
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