Utilization of Machine Vision to Monitor the Dynamic Responses of Rice Leaf Morphology and Colour to Nitrogen, Phosphorus, and Potassium Deficiencies

Machine vision technology enables the continuous and nondestructive monitoring of leaf responses to different nutrient supplies and thereby contributes to the improvement of diagnostic effects. In this study, we analysed the temporal dynamics of rice leaf morphology and colour under different nitrogen (N), phosphorus (P), and potassium (K) treatments by continuous imaging and further evaluated the effectiveness of dynamic characteristics for identification. The top four leaves (the 1st incomplete leaf and the top three fully expanded leaves) were scanned every three days, and all images were processed in MATLAB to extract the morphological and colour characteristics for dynamic analysis. Subsequently, the mean impact value was applied to evaluate the effectiveness of dynamic indices for identification. According to the results, higher nutrient supply resulted in a faster leaf extension rate and a lower developing rate of chlorosis, and the influence of N deficiency on leaf growth was the greatest, followed by P deficiency and then K deficiency. Furthermore, the optimal indices for identification were mainly calculated from morphological characteristics of the 1st incomplete leaf and colour characteristics of the 3rd fully expanded leaf. Overall, dynamic analysis contributes not only to the exploration of the plant growth mechanism but also to the improvement of diagnostics.

[1]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[2]  Yunseop Kim,et al.  Hyperspectral image analysis for water stress detection of apple trees , 2011 .

[3]  Zou Xiaobo,et al.  Nondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imaging , 2012 .

[4]  Ke Wang,et al.  [Leaf characteristics extraction of rice under potassium stress based on static scan and spectral segmentation technique]. , 2010, Guang pu xue yu guang pu fen xi = Guang pu.

[5]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[6]  Haiyan Cen,et al.  Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification , 2016 .

[7]  W. Hager,et al.  and s , 2019, Shallow Water Hydraulics.

[8]  Jing Xu,et al.  Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model , 2013, PLoS neglected tropical diseases.

[9]  Xiaojun Liu,et al.  Modeling Dynamics of Leaf Color Based on RGB Value in Rice , 2014 .

[10]  Clive H. Bock,et al.  Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging , 2010 .

[11]  A. Karnieli,et al.  Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment , 2015 .

[12]  Y. Wang,et al.  Estimating nitrogen status of rice using the image segmentation of G-R thresholding method , 2013 .

[13]  Li Lan-ta,et al.  Diagnosis of N nutrition of rice using digital image processing technique , 2015 .

[14]  Xiaojun Liu,et al.  Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice , 2017, Front. Plant Sci..

[15]  T. Ingestad,et al.  Nitrogen stress in birch seedlings. II. N, K, P, Ca, and Mg nutrition , 1979 .

[16]  E. J. Hewitt,et al.  Principles of plant nutrition , 1979, Nature.

[17]  W. Marsden.  I and J , 2012 .

[18]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[19]  Jinsong Deng,et al.  Identification of Nitrogen, Phosphorus, and Potassium Deficiencies in Rice Based on Static Scanning Technology and Hierarchical Identification Method , 2014, PloS one.

[20]  A. M. Edwards,et al.  Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time , 2015, Journal of experimental botany.

[21]  R. Hunt.  Plant Growth Curves: The Functional Approach to Plant Growth Analysis , 1983 .

[22]  이화영.  X , 1960, Chinese Plants Names Index 2000-2009.

[23]  S. Oyedeji,et al.  Effect of NPK and Poultry Manure on Growth, Yield, and Proximate Composition of Three Amaranths , 2014 .

[24]  Fei Liu,et al.  Hyperspectral Imaging for Mapping of Total Nitrogen Spatial Distribution in Pepper Plant , 2014, PloS one.

[25]  Yufeng Ge,et al.  Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging , 2016, Comput. Electron. Agric..

[26]  R. Furbank,et al.  Digital imaging approaches for phenotyping whole plant nitrogen and phosphorus response in Brachypodium distachyon. , 2014, Journal of integrative plant biology.

[27]  Weixing Cao,et al.  Indicators for diagnosing nitrogen status of rice based on chlorophyll meter readings , 2016 .

[28]  L. Chen,et al.  Diagnosing of rice nitrogen stress based on static scanning technology and image information extraction , 2014 .

[29]  Kenji Omasa,et al.  Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light , 2014, Plant Methods.

[30]  Deng Lie,et al.  Prediction of nitrogen and phosphorus contents in citrus leaves based on hyperspectral imaging , 2015 .

[31]  Mohammadmehdi Saberioon,et al.  Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale , 2014, Int. J. Appl. Earth Obs. Geoinformation.