Chinese Journal of Agrometeorology ›› 2022, Vol. 43 ›› Issue (11): 935-944.doi: 10.3969/j.issn.1000-6362.2022.11.007

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Development of Winter Wheat Leaf Morphology Measurement Software Based On Machine Vision

GONG Zhi-hong , DONG Chao-yang, YU Hong , LIU Bu-chun, LI Chun , LIU Tao, LI Jun-ling   

  1. 1. Henan Key Laboratory of Agrometeorological Ensuring and Applied Technique, CMA，Zhengzhou 450003, China；2.Tianjin Climate Center，Tianjin 300074；3. Tianjin Xiqing District Meteorological Bureau, Tianjin 300380；4. Institute of Environment and Sustainable Development in Agriculture, CAAS, Beijing 100081；5. Henan Institute of Meteorological Sciences, Zhengzhou 450003
  • Received:2021-12-27 Online:2022-11-20 Published:2022-11-18

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Abstract: With the development of image processing and recognition technology, the technology of crop phenotype recognition is becoming more mature. In order to achieve accurate and rapid determination of leaf area and area coefficient of winter wheat with different varieties and different growth stages, VB.net and OpenCV software were used in the study. In the image processing and packaging library based on NET platform, a winter wheat leaf morphology measurement algorithm with machine vision was developed, and a software was designed and developed. The software can realize distortion calibration of digital images and simultaneously measure the length, width and area of multiple leaves. And then in order to verify the effect of software measurement, 100 green expanded leaves of winter wheat were selected, and the accuracy and stability of image recognition method were analyzed by comparing with the leaf length and width measured manually. The leaf area measured by WinDIAS leaf area analysis system. The results showed that the correlation coefficients of the winter wheat leaf length, width and area measured by image recognition method, manual and WinDIAS were more than 0.975 respectively, while the normalized root mean square errors were less than 0.10%. For the distortion calibration function of digital images, the maximum relative error between the measurement results and the original image is only 2% after the calibration of the image with the horizontal (vertical) scaling of 50% and the vertical (horizontal) beveling of 30°. It is suggested that the recognition method of winter wheat leaf morphology based on machine vision can accurately calibrate various distorted images, while it can be used as a new method to accurately measure the area, length and width of multiple leaves at the same time. It can be popularized and applied in agricultural scientific measurement, agricultural information service and agricultural meteorological observation service.

Key words: Winter wheat, Leaf morphology, Distortion calibration, RGB image