Identification of Nitrogen, Phosphorus, and Potassium Deficiencies Based on Temporal Dynamics of Leaf Morphology and Color

Yuanyuan Sun, Cheng Tong, Shan He, Ke Wang and Lisu Chen
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Yuanyuan Sun: Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Cheng Tong: Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Shan He: Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Ke Wang: Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Lisu Chen: Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China

Sustainability, 2018, vol. 10, issue 3, 1-14

Abstract: Non-destructive nutrition diagnosis provides effective technological support for agricultural sustainability. According to the plant nutrition mechanism, leaf characteristics displays different changing trends under nitrogen (N), phosphorus (P), and potassium (K) nutrition stress. In this study, the dynamic capture of rice leaf by scanning was used to research the changing regulation of leaf characteristics under nutrition stress. The leaf characteristics were extracted by mean value and regionprops functions in MATLAB, and the leaf dynamics were quantified by calculating the relative growth rate. Stepwise discriminant analysis and leave one out cross validation were applied to identify NPK deficiencies. The results indicated that leaves with N deficiency presented the lowest extension rate and the fastest wilt rate, followed by P and K deficiencies. During the identification, both morphological and color indices of the first incomplete leaf were effective indices for identification, but for the third fully expanded leaf, they were mainly color indices. Moreover, the first incomplete leaf had comparative advantage in early diagnosis (training accuracy 73.7%, validation accuracy 71.4% at the 26th day after transplantation), and the third fully expanded leaf generated higher accuracy at later stage. Overall, dynamic analysis expanded the application of leaf characteristics in identification, which contributes to improving the diagnostic effect.

Keywords: leaf image; dynamic analysis; nondestructive nutrition diagnosis; agricultural sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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