Rapid diagnosis of nitrogen nutrition status in rice based on static scanning and extraction of leaf and sheath characteristics
Keywords:
N deficiency, static scanning, leaf sheath, support vector machine (SVM), identificationAbstract
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. Keywords: N deficiency, static scanning, leaf sheath, support vector machine (SVM), identification DOI: 10.3965/j.ijabe.20171003.1860 Citation: Chen L S, Sun Y Y, Wang K. Rapid diagnosis of nitrogen nutrition status in rice based on static scanning and extraction of leaf and sheath characteristics. Int J Agric & Biol Eng, 2017; 10(3): 158–164.References
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[3] Lin F F, Ding X D, Fu Z P, Deng J S, Shen Z Q. Application of mutual information to variable selection in diagnosis of phosphorus nutrition in rice. Spectrosc Spect Anal, 2009; 9: 2467–2470.
[4] Shibayama M, Takahashi W, Morinaga S, Akiyama T. Canopy water deficit detection in paddy rice using a high resolution field spectroradiometer. Remote Sens Environ, 1993; 45(2): 117–126.
[5] Jia L L, Fan M S, Zhang F S, Chen X P, Lyu S H, Sun Y M. Nitrogen status diagnosis of rice by using a digital camera. Spectrosc Spect Anal, 2009; 29(8): 2176–2179.
[6] McCauley A, Jones C, Jacobsen J. Plant nutrient functions and deficiency and toxicity symptoms. Nutrient management module, 2009; (9): 1–16.
[7] Shi Y Y, Deng J S, Chen L S, Zhang D Y, Ding X D, Wang K. Leaf characteristics extraction of rice under potassium stress based on static scan and spectral segmentation technique. Spectrosc Spect Anal, 2009; 29(7): 1745–1748.
[8] Chen L S, Zhang S J, Wang K, Shen Z Q, Deng J S. Identifying of rice phosphorus stress based on machine vision technology. Life Sci J, 2013; 10(2): 2655–2663.
[9] Chen L S, Wang K. Diagnosing of rice nitrogen stress based on static scanning technology and image information extraction. J Soil Sci Plant Nut, 2014; 14(2): 382–393.
[10] Yang X, Zhang J J, Guo D D, Xiong X, Chang L Y, Niu Q L, et al. Measuring and evaluating anthocyanin in lettuce leaf based on color information. IFAC-PapersOnLine, 2016; 49(16): 96–99.
[11] Rangel B M S, Fernandez M A A, Murillo J C. KNN-based image segmentation for grapevine potassium deficiency diagnosis. 2016 International Conference on Electronics, Communications and Computers, 2016. DOI: 10.1109/ Conielecomp.2016.7438551.
[12] Li W M. Crop symptoms under nutrition stress. Qinghai Agro-Technology Extension, 2012; 2: 44–45.
[13] Armstrong D L. Nutrient deficiency symptoms in rice. Better Crops International 16, 2002; Special Supplement: 23–25.
[14] Weston J, Mukherjee S, Chapelle O, Pontil M, Poggio T, Vapnik V. Feature selection for SVMs. Nips, 2000; 12: 68–674.
[15] Fung G M, Mangasarian O L. A feature selection Newton method for support vector machine classification. Comput Optim Appl, 2004; 28(2): 185–202.
[16] Li G Z, Wang M, Zeng H J. An introduction to support vector machines and other kernel based learning methods. Bejing: Publishing House of Electronics Industry Press, 2004.
[17] Fung G M, Mangasarian O L. A feature selection newton method for support vector machine classification. Comput Optim Appl, 2004; 2(28): 185–202.
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Published
2017-05-31
How to Cite
Lisu, C., Yuanyuan, S., & Ke, W. (2017). Rapid diagnosis of nitrogen nutrition status in rice based on static scanning and extraction of leaf and sheath characteristics. International Journal of Agricultural and Biological Engineering, 10(3), 158–164. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/1860
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Information Technology, Sensors and Control Systems
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