1. 中国农业大学现代精细农业系统集成研究教育部重点实验室，北京 100083
2. Center for Precision & Automated Agricultural System, Washington State University, Pullman WA 99350, USA
Chlorophyll Content Detection Based on Image Segmentation by Plant Spectroscopy
LONG Yao-wei1, LI Min-zan1, GAO De-hua1, ZHANG Zhi-yong1, SUN Hong1*, Qin Zhang2
1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
2. Center for Precision & Automated Agricultural System, Washington State University, Pullman WA 99350, USA
Abstract：In order to quickly analyze the growth of the crop in the field, the spectral imaging sensor was used to detect the chlorophyll content of the maize canopy. The imagesof 47 maize plants were photographed using an IMEC 5×5 imaging unit multispectral camera. The camera was designed based on the coating principle to obtain spectral images of 25 wavelengths in the range of 673～951 nm. At the same time, the chlorophyll content was measured by SPAD-520 device. There were 2～3 sampling points in each leaf, and they were measured 3 times at each point so that 242 sample data were collected. A linear inversion formula was established based on the relationship between the gray value of multi-spectral images and the gray plate standard reflectance. The gray plate standard was made up of 4 gray level standard plates. In order to separate the plant from flowerpots and soil background, a combination method was studied. Although the canopy was segmented using OTSU method, it was not useful. After analyzing the spectral reflectance characteristics of different objects, a plant extraction algorithm was proposed based on normalization difference vegetation index (NDVI) image and region marker calculation. Firstly, the initial segmentation was conducted based on NDVI calculation on each pixel. Secondly, the noise points were eliminated by the edge-preserved median filtering algorithm. Thirdly, the region algorithm was used to obtain a mask and finally segment the multi-spectral images of theplant canopy. The characteristic wavelengths were selected based on CA (Correlation Analysis, CA) and RF (random Frog, RF) algorithm, which was used to construct the Near-Infrared (NIR) and Red (R) data set. The vegetationindices were calculated by the traversing NIR and R sets including the Ratio Vegetation Index (RVI), the Normalized Difference Vegetation Index (NDVI), the Difference Vegetation Index (DVI), and the SPAD Transfer Index (TSPAD). According to the proportion of 7∶3, the total samples were divided into calibration and validation setby SPXY (Sample set partitioning based on joint X-Y distance, SPXY) algorithm. After screening the vegetation indices by CA and RF algorithm again, the model of chlorophyll content was established by CA+RF-PLSR (Partial least squares regression, PLSR). The results showed thatthe calibration accuracy of CA+RF-PLSR model was 0.573 9, the RMSEC was 3.84%, and the validation accuracy was 0.420 2, the RMSEV was 2.3%. The chlorophyll contentdistribution of crop was analyzed visually using the pseudo color image. The study could provide technical and application support for chlorophyll distribution of field maize plants and visual monitoring of corn growth dynamics.
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