AS  Vol.8 No.8 , August 2017
Hyperspectral Inversion of Potassium Content in Apple Leaves Based on Vegetation Index
The aim of this study is to establish the estimation model of potassium content in apple leaves by using vegetation index. A total of 96 fresh apple leaves were collected from 24 orchards in Qixia County, Shandong Province. The spectral reflectance of the leaves was measured by ASD FieldSpec4. The difference vegetation index (DVI), ratio vegetation index (RVI) and normalized vegetation index (NDVI) were used to make the contour map through Matlab platform, and the combination of high correlation wavelength was selected to establish the random forest (RF) regression model of potassium content. The hyperspectral reflectance increased with the increase of leaf potassium content. The correlation between DVI and the content of potassium is higher than NDVI and RVI. The optimal vegetation index was DVI (364,740), the correlation coefficient was 0.5355. The random forest regression model established with DVI selected vegetation index was the best. R2 was 0.8995, RMSE and RE% were 0.0791 and 0.0617 respectively. Using DVI to establish the random forest regression model to reverse the potassium content of apple leaves has achieved good results. It is important to determine the growth status of apple in hyperspectral and to determine the potash fertilizer of apple trees.
Cite this paper: Guo, X. , Zhu, X. , Li, C. , Wei, Y. , Yu, X. , Zhao, G. and Sun, H. (2017) Hyperspectral Inversion of Potassium Content in Apple Leaves Based on Vegetation Index. Agricultural Sciences, 8, 825-836. doi: 10.4236/as.2017.88061.

[1]   Chen, C., Tong, Y.A., Lu, Y.L. and Gao, Y.M. (2016) Effects of Different Potassium Fertilizers on Production, Quality and Storability of Fuji Apple. Journal of Plant Nutrition and Fertilizer Science, 22, 216-224.

[2]   Zhong, P. (1994) Effects of Potassium towards Fruit Yield and Fruit Quality. China Fruits, 1, 40-41.

[3]   Zhu, X.C., Jiang, Y.M., Zhao, G.X., Wang, L. and Li, X.C. (2013) Hyperspectral Estimation of Kalium Content in Apple Florescence Canopy Based on Fuzzy Recognition. Spectroscopy and Spectral Analysis, 33, 1023-1027.

[4]   Al-Abbas, A.H., Barr, R., Hall, J.D., Crane, F.L. and Baumgardner, M.F. (1974) Spectra of Normal and Nutrient Deficient Maize Leaves. Agronomy Journal, 66, 16-20.

[5]   Jackson, R.D., Jones, C.A., Uehara, G. and Santo, L.T. (1980) Remote Detection of Nutrient and Water Deficiencies in Sugarcane under Variable Cloudiness. Remote Sensing of Environment, 11, 327-331.

[6]   Guo, H., Jin, L.S. and Lin, G.L. (2008) Rapid Determination of Nitrogen and Potassium Contents in Tobacco Leaves by Near Infrared Reflectance Spectroscopy. Heilongjiang Agricultural Sciences, 4, 103-104.

[7]   Liu, Y.L., He, Q.L., Lu, Q., Yi, S.Y., Xie, Y.J., Zheng, Y.Q., Liu, X.F. and Deng, L. (2014) Characteristics of Potassium Content in Citrus Flowers with Hyperspectral Imagery. Journal of Fruit Science, 31, 1065-1071.

[8]   Huang, S.P., Yue, X.J., Hong, T.S., Wu, W.B. and Li, Y.Y. (2013) Potassium Content Prediction Model of Citrus Leaves Indifferent Phenological Period. Journal of Jiangsu University, 34, 529-535.

[9]   Wang, K., Shen, Z.Q. and Wang, R.C. (1993) Study on the Feasibility of Estimating Potassium Content by Rice Spectral Analysis. Journal of Zhejiang Agricultural University, 19, 104-107.

[10]   Wang, K., Shen, Z.Q., Abou-Ismail, O., Yaghi, A. and Wang, R.C. (1997) Preliminary Study on Canopy and Leaf Reflectance Characteristics of Rice with Various Potassium Levels. Bulletin of Science and Technology, 13, 211-214.

[11]   Tian, J.G., Wang, S.D., Zhang, L.F., Ma, C. and Zhang, X. (2016) Spectral Index Sensitivity Study of Winter Wheat Chlorophyll Inversion Using Hyperspectral Remote Sensing Vegetation Index. Science Technology and Engineering, 16, 1-8.

[12]   Pan, B., Zhao, G.X., Zhu, X.C., Liu, H.T., Liang, S. and Tian, D.D. (2013) Estimation of Chlorophyll Content in Apple Tree Canopy Based on Hyperspectral Parameters. Spectroscopy and Spectral Analysis, 33, 2203-2206.

[13]   Tian, Y.C., Yang, J., Yao, X., Zhu, Y. and Cao, W.X. (2009) Quantitative Relationships between Hyperspectral Vegetation Indices and Leaf Area Index of Rice. Chin. Chinese Journal of Applied Ecology, 20, 1685-1690.

[14]   Xing, D.X. and Chang, Q.R. (2009) Research on Predicting the TN, TP, TK Contents of Fresh Fruit Tree Leaves by Spectral Analysis with Red Fuji Apple Tree as an Example. Journal of Northwest A&F University, 37, 141-147.

[15]   Chai, Z.P., Chen, B.L., Jiang, P.A., Sheng, J.D., Li, S.S., Liu, M. and Meng, Y.B. (2014) Hyperspectral Estimation Models for Total Potassium Content of Kuerle Fragrant Pear Leaves. Chinese Journal of Eco-Agriculture, 22, 80-86.

[16]   Yi, S.L., Deng, L., He, S.L., Zhang, Y.Q. and Mao, S.S. (2010) A Spectrum Based Models for Monitoring Leaf Potassium Content of Citrus sinensis (L) cv. Jincheng Orange. Scientia Agricultura Sinica, 43, 780-786.

[17]   He, Q.L., Zheng, S.L., Zhou, S.M., Zhang, Q., Yuan, J.C. and Hu, J.J. (2016) Estimation Models of Chlorophyll Contents in Potato Leaves Based on Hyperspectral Vegetation Indices. Journal of South China Agricultural University, 37, 45-49.

[18]   Jiang, A.N., Huang, W.J., Zhao, C.J., Liu, K.L., Liu, L.Y. and Wang, J.H. (2007) Effects of Variable Nitrogen Application Based on Characteristics of Canopy Light Reflectance in Wheat. Scientia Agricultura Sinica, 40, 1907-1913.

[19]   Yao, X., Zhu, Y., Feng, W., Tian, Y.C. and Cao, W.X. (2009) Exploring Novel Hyperspectral Band and Key Index for Leaf Nitrogen Accumulation in Wheat. Spectroscopy and Spectral Analysis, 29, 2191-2195.

[20]   Wang, W., Wang, W.J., Li, J.S., Wu H., Xu, C., Liu, X.F. and Liu, T. (2010) Remote Sensing Analysis of Impacts of Extreme Drought Weather on Ecosystems in Southwest Region of China Based on Normalized Difference Vegetation Index. Research of Environmental Sciences, 23, 1447-1455.

[21]   Fang, K.N., Wu, J.B., Zhu, J.P. and Xie, B.C. (2011) A Review of Technologies on Random Forests. Statistics& Information Forum, 26, 32-37.

[22]   Li, X.H. (2013) Using “Random Forest” for Classification and Regression. Chinese Journal of Applied Entomology, 50, 1190-1197.

[23]   Gao, D., Zhang, Y.X. and Zhao, Y.H. (2009) Random Forest Algorithm for Classification of Multiwavelength Data. Research in Astronomy and Astrophysics, 9, 220-226.

[24]   Li, X.Q., Liu, X.N., Liu, M.L. and Wu, L. (2014) Random Forest Algorithm and Regional Applications of Spectral Inversion Model for Estimating Canopy Nitrogen Concentration in Rice. Journal of Remote Sensing, 4, 923-945.

[25]   Cheng, L.Z., Zhu, X.C., Gao, L.L., Wang, L. and Zhao, G.X. (2016) Hyperspectral Estimation of Phosphorus Content for Apple Leaves Based on the Random Forest Model. Journal of Fruit Science, 33, 1219-1229.