1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China 3. College of Agronomy, Hebei Agricultural University, Baoding 071001, China 4. Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China
Leaf angle is an important parameter to describe the canopy structure of vegetation. The leaf angle distribution (LAD) determines the interception of vegetation canopy and is an important parameter in quantitative inversion of remote sensing. The current method of measuring the leaf angle is time-consuming, labor-intensive and subjective, with no accuracy guarantee. In this paper, image-based probability density function extraction for LAD of corn plant is proposed, which can extract LAD of corn plant quickly and accurately with low cost. Firstly, the skeleton is extracted from the image. Secondly, the information such as burrs and stems in the skeleton image is removed to obtain the leaf skeleton. Finally, the leaf angle is extracted by searching the skeleton with a search window of size 2×20. The results of precision evaluation show that the correlation coefficient between the measured value of the corn dip angle and the extracted value is 0.821 4, and the correlation coefficient between measured and extracted values of the corn leaf angle at jointing stage is 0.908 7, which suggests that the method is feasible and accurate with low cost.
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2020, Vol. 32 
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