Detection of Chlorophyll Content in Maize Canopy from UAV Imagery. Issue 30 (2019)

• Record Type: Journal Article
• Authors: Lang, Qiao; Zhiyong, Zhang; Longsheng, Chen; Hong, Sun; Minzan, Li; Li, Li; Junyong, Ma
• Is Part Of: IFAC-PapersOnLine. Volume 52:Issue 30(2019)
• Page Start: 330
• Page End: 335
• Publication Date: 2019
• Subjects: chlorophyll content -- UAV sensing technology -- image processing -- BP neural network -- visual distribution; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2019.12.561
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Chlorophyll is an important indicator for the evaluation of plant photosynthesis ability and growth status. In order to obtain the spatial distribution of chlorophyll content in field crops quickly and non-destructively, the chlorophyll content detection of maize canopy was carried out based on UAV image processing. In this paper, the RGB (red, green, blue) images of the maize canopy were measured in the Hengshui, Hebei province. The processing method was proposed to estimate the chlorophyll content in the field. Firstly, the image was segmented based on the HSV (hue, saturation, value) color model to remove soil background. The parameters were extracted related to the color feature and the texture feature in the image. On the one hand, there were 10 color parameters were involved including the red, green, blue, green and red differences, normalized red and green differences, and so on. On the other hand, the texture parameters were calculated with mean, standard deviation, smoothness, third moment, etc. The detection model of maize chlorophyll content was established and discussed based on BP neural network. The experiment results showed that: (1) The detecting accuracy of chlorophyll content was increased by the image parameter combination of color and texture features. Compared with the color feature, the determination coefficient of the model was increased from 0.6987 to 0.7246 by involving the texture feature. (2) The segmentation of canopy could help to improve the estimation accuracy due to the influence elimination of soil background, and the determination coefficient of model increased from 0.7246 to 0.7564, meanwhile, the root mean square error (RMSE) decreased from 4.4659 mg·L -1 to 4.4425 mg·L -1 . The chlorophyll content of maize canopy was calculated at pixel level to indicate the field statues. The distribution map of chlorophyll content in field maize canopy was drawn based on pseudo-color technique. It provided a tool to visually distinguish the field road and canopy area, showing the difference in chlorophyll distribution of the plot. The UAV imagery could help to measure the content and distribution of maize chlorophyll non-destructively, and provide a support for crop evaluation and precision management in the field.