Hyperspectral Estimation of Kalium Content in Apple Florescence Canopy Based on Fuzzy Recognition
ZHU Xi-cun1, 2, 3, JIANG Yuan-mao2*, ZHAO Geng-xing1, WANG Ling1, 3, LI Xi-can4
1. College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China 2. College of Horticulture Science and Engineering, Shandong Agricultural University, Tai’an 271018, China 3. Key Laboratory of Agricultural Ecology Environment of Shandong Agricultural University, Tai’an 271018, China 4. College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China
Abstract：The objective of the present paper is fast and nondestructive estimate of kalium content using ASD FieldSpec3 spectrometer determined hyperspectral data in apple florescence canopy. According to detection of hyperspectral data of the apple florescence canopy and kalium content data at laboratory in Qixia city of experimental orchards in 2008 and 2009, the correlation analysis of hyperspectral reflectance and its eleven transforms with kalium content was proceeded. The biggest correlation coefficient as independent variable and the estimation model of kalium content were established based on fuzzy recognition algorithms. The model was tested by sample inspection in 2008 and verified by data in 2009. The results showed that the correlation is less for the original spectral reflectance (R) and its reciprocal(1/R), logarithm (lgR), square root (R1/2) and the kalium content, but it is enhanced obviously for their first derivative and second derivative. The correlation coefficient(r) of kalium content estimating model =11.344 5h+1.309 7 is 0.985 1, the total root mean square difference (RMSE) is 0.355 7 and F statistics is 3 085.6. The average relative error of measured values and estimated values for 24 inspection sample is 9.8%, estimation accuracy is 90.2% and verification accuracy is 83.3% utilizing test data in 2009. It was showed that this model is more stable by estimating apple florescence canopy of kalium content and the model precision is able to meet the needs of production.
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