Rapid diagnosis of nitrogen nutrition status in rice based on static scanning and extraction of leaf and sheath characteristics

Abstract: According to the mechanism of rice growth, if nitrogen deficiency occurs, not only rice leaf but also sheath shows special symptoms: sheaths become short, stems appear light green, older sheath become lemon-yellowish. Nitrogen nutrition status of rice could be identified based on the differences of color and shape of leaf and sheath under different levels of nitrogen nutrition. Machine vision technology can be used to non-destructively and rapidly identify rice nutrition status, but image acquisition via digital camera is susceptible to external conditions, and the images are of poor quality. In this research, static scanning technology was used to collect images of rice leaf and sheath. From those images, 14 color and shape characteristic parameters of leaf and sheath were extracted by R, G, B mean value function and region props function in MATLAB. Based on the relationship between nitrogen content and the characteristics extracted from the images, the leaf R, leaf length, leaf area, leaf tip R, sheath G, and sheath length were chosen to identify nitrogen status of rice by using Support Vector Machine (SVM). The results showed that the overall identification accuracies of different nitrogen nutrition were 94%, 98%, 96% and 100% for the four growth stages, respectively. Different years of data were used for validation, identification accuracies were 88%, 98%, 90% and 100%, respectively. The results showed that additional sheath characteristics can effectively increase the identification accuracy of nitrogen nutrition status and the methodology developed in the study is capable of identifying nitrogen deficiency accurately in the rice.