Accuracy Evaluation of Summer Maize Coverage and Leaf Area Index Inversion Based on Images Extraction Technology

doi:10.3969/j.issn.1000-6362.2016.04.013

Abstract

Abstract:

How to extract accurately the crops from the complex field scene is the key to calculate crop coverage and invert LAI for crop segmentation methods. In this paper, the dynamic images of summer maize growth season in 2011-2012, in Zhengzhou,Taian and Gucheng, which were under the different light intensity and complex outdoor background with shadows, plants residues, were obtained through on-line, real-time automatic transmission device. Meanwhile, to overcome the error caused by image distortion, geometric correction of the raw images was needed, crop coverage extraction ability and leaf area index inversion performance of four popular crop extraction algorithms (ExG, ExGR, CIVE and AP-HI) were compared and evaluated. By comparison, the effective extraction method for canopy coverage and leaf area index of summer maize under complex environment was selected. On this basis, models of canopy coverage and leaf area index with canopy porosity method were established and verified with measured data. The results showed that the light intensity changes and complex field environment, which contained plant shadows and residues, had a significant impact on the accuracy of crop segmentation algorithm. And inversion model of AP-HI was superior to the other methods in both light adaptability and complex environment, the relative error compared with true image was less than 0.2 and higher than the current visual estimation accuracy. Leaf area index was estimated by four extraction algorithms in summer maize growth season in 2011 and 2012. Based on comparison of R and RMSR among models, high fitting models were selected. The optimal model for LAI was based on AP-HI extraction algorithm, which had the highest R (0.89-0.96) and the lowest RMSR (0.47-0.75). Considering the accuracy and stability of the model, inversion model of AP-HI based on the method of application had more advantages.

Key words: Crops, Image processing, Coverage, Leaf area index, Model

LI Cui-na, ZHANG Xue-fen, YU Zheng-hong, WANG Xiu-fang. Accuracy Evaluation of Summer Maize Coverage and Leaf Area Index Inversion Based on Images Extraction Technology[J]. Chinese Journal of Agrometeorology, 2016, 37(04): 479-491.

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