关键词: 叶绿素含量, 光谱微分, 遗传算法, BP神经网络

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 GABP 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 EMDSD spectrum. It was selected as the input factors of GABP model that the EMDSD values in five bands whose correlation coefficients with chlorophyll content were over 06. Then the GABP 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 GABP model was the highest， at 0818， RMSE was 2442 and e was 5436%. The results showed that the predicting precision of GABP model using EMDSD values as input factors was higher than MLR and BP model. It was verified feasible that using GABP model based on EMDSD 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）