Study On The Monitoring Of Shoot Height Growth Of Phyllostachys Edulis Based On Image

As an important forest resource,bamboo is widely distributed in the world and has great ecological,economic and social value.Among bamboo resources,Moso bamboo(Phyllostachys edulis)is the most widely distributed and economically valuable bamboo species.In the growth process of Moso bamboo,the high growth information of bamboo shoot is regarded as an important index to reflect the growth and yield of bamboo,which is an important content of the growth and development research of bamboo.For a long time,the information of Moso bamboo growth has been obtained mainly by artificial observation,but this method is time-consuming and subjective,which is difficult to realize real-time monitoring in a large range.In view of this,in this paper,aiming at the shortcomings of the existing monitoring methods,with the help of image technology,the field low-illumination image enhancement,the image segmentation of Moso bamboo shoots and the image-based high growth monitoring method of Moso bamboo shoots were studied,which laid a foundation for online real-time monitoring of high growth of Moso bamboo shoots.Main research contents and results include:1)Research on image enhancement method of low illumination.Firstly,the optimal histogram parameter of histogram mapping function in global contrast enhancement is obtained by using artificial bee colony algorithm,that is,the optimal contrast enhancement function is obtained.Then combined with hue-preserving theory to achieve contrast enhancement of low illumination images in the field.The research shows that the proposed method is subjectively better visually than the four classic image enhancement algorithms such as CLAHE,HF,MSR,and MSRCR,and also performs better on objective evaluation indicators such as PSNR,SSIM,and QILV.2)Research on image segmentation method of Moso bamboo shoots.Firstly,the local spatial information is introduced on the basis of fuzzy clustering,and then the artificial bee colony optimized by sine and cosine algorithm is used to optimize the initial clustering center of the clustering algorithm.Finally,the image is segmented twice to achieve the segmentation and extraction of Moso bamboo shoots.The research shows that compared with PSO,SCA and MFO,this algorithm has better optimization performance,and has stronger anti-noise performance and higher segmentation accuracy compared with FCM and ABC-FCM in the real image segmentation of Moso bamboo shoots in forest.3)Research on the monitoring method of Moso bamboo shoots height growth based on image.Firstly,the growth image of Moso bamboo shoots was acquired in real time by setting up a solar 4G monitoring spherical camera in the forest,and then the target image of Moso bamboo shoots was obtained through image enhancement,image segmentation and other operations.Then,based on the principle of geometric angle,the linear mapping relationship between the number of pixels in the image and the rotation angle of the camera is calculated,and the real-time height growth data of Moso bamboo shoots is obtained.The results show that the proposed method can effectively achieve the non-destructive and accurate real-time monitoring of the high growth of Moso bamboo shoots.