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Abstract: The chlorophyll is the main pigment for the photosynthesis of crops. The BPNN is a novel method of inversing chlorophyll content. In order to study chlorophyll content inversion model with higher precision, it was used to increase the correlation between input factors and chlorophyll content by combining empirical mode decomposition (EMD) with spectral derivative (SD). And genetic algorithm(GA) was used to optimize BPNN building GABP model to get the best initial weights and thresholds. The spectral reflectance of corn leaf was pretreated by the methods of EMD and derivative, getting the EMDSD spectrum. It was selected as the input factors of GABP model that the EMDSD values in five bands whose correlation coefficients with chlorophyll content were over 06. Then the GABP model with seven hidden layer nodes was established, selecting the network whose fitness of the best individual was the lowest to predict the chlorophyll content of corn leaf. R2 of GABP model was the highest, at 0818, RMSE was 2442 and e was 5436%. The results showed that the predicting precision of GABP model using EMDSD values as input factors was higher than MLR and BP model. It was verified feasible that using GABP model based on EMDSD spectrum to improve the inversion accuracy of corn leaf chlorophyll content.
Key words: chlorophyll content, spectral derivative(SD), genetic algorithm(GA), BP neural network(BPNN)
ZHANG Wanwan,YANG Keming, WANG Guoping, LIU Erxiong, LIU Cong. Study on GABP inversing modeling method of corn leaf chlorophyll content based on EMD and spectral derivative method[J]. .
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http://www.zjnyxb.cn/EN/Y2016/V28/I8/1297