Environmental Greening And Tree Health Maintenance Based on Color Histogram

3rd International Symposium on Resource Exploration and Environmental Science

生态环境科学

Xie, Junlin^1 ; Wang, Ruiping^1 ; Ma, Shanpeng^1 ; Han, Xu^1 ; Yu, Qirui^1 ; Yang, Huijun^1^3 ; Wang, Xin^2

College of Information Engineering, Northwest A and F University, Yangling, Shaanxi

712100, China^1

Department of Foreing Languages, Northwest A and F University, Yangling, Shaanxi

712100, China^2

Shaanxi Agricultural Internet of Things Technology Research Institute, China^3

关键词: Chemical treatments;    Classification standard;    Color difference;    Ecological environments;    Health diagnosis;    Plant diseases and insect pests;    Quantitative data;    Weighted summations;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/300/3/032112/pdf
DOI  :  10.1088/1755-1315/300/3/032112

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Due to man-made damage, plant diseases and insect pests, environmental greening, ecological environment and other reasons, ancient and famous trees have been severely damaged. The protection of ancient trees requires diagnosis of their health and biological and chemical treatment measures. Aiming at the inconvenience, high cost and low degree of automation of manual methods for health diagnosis of ancient trees, the solution and overall architecture of a tree health diagnosis system based on OpenCV and genetic algorithm are analyzed and designed. Firstly, the leaf image mode is trained, and the color histogram is used to distinguish the leaf density and color difference between different individuals, and the fitting index is used to obtain more accurate diagnostic classification standards. Then, the matching value is calculated by genetic algorithm training through the H average value in HSVHSV and the H average value of healthy ancient trees in the histogram to obtain the matching grade. Then, the tree image is read, artificial segmentation is carried out to obtain the quasi-healthy crown score, weighted summation is carried out to obtain the leaf score, and the health grade is separated according to the ratio. Finally, the two are weighted to obtain the final health grade. The experimental results show that the system can accurately and effectively distinguish different leaf health grades according to the difference of leaf color and density, and provide quantitative data for health diagnosis research of ancient and famous trees.