Identifying of rice phosphorus stress based on machine vision technology

At present, the identifying of rice nutrition stress by the chemical analysis is time-consuming and laborious. Machine vision technology can be used to non-destructively and rapidly identify rice nutrition 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. The R, G, leaf area and Area/Perimeter ratio were chosen as the key characteristics for identifying phosphorus stress by One-Way ANOVA.The results showed that the overall recognition accuracy of phosphorus stress were 87.5 %, 100 %, 92 %, and 100% respectively. Based on the result, the methodology developed in the study is capable of identifying phosphorus stress accurately in the rice. (L. S. Chen, S. J. Zhang, K. Wang, Z. Q. Shen, J. S. Deng. Identifying of rice phosphorus stress based on machine vision technology. Life Sci J 2013; 10(2): 2655-2663). (ISSN: 1097-8135). http://www.lifesciencesite.com 368

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