## 1 Introduction  
Bamboo is widely distributed in the world, and bamboo forests are considered the \"second largest forest in the world\". From a global perspective, China has the most bamboo resources, among which moso bamboo is the most economically important species, with a long history of cultivation and the widest distribution area in China. Bamboo not only plays an important role in absorbing carbon dioxide, preventing soil erosion, and maintaining biodiversity, it also provides a variety of materials for human beings [1], and bamboo shoots are an environmentally friendly and healthy food. The number of new shoots and the height growth of shoots are important information for the cultivation of moso bamboo shoots, timber, and dual-use forests. Thus, there is an urgent need to promote scientific decision making and production management through efficient and accurate monitoring to improve China's bamboo industry, which is currently characterized by high labor requirements, high cost, and low efficiency. In recent years, much research has been conducted to overcome the challenges involved in counting the number of plants and measuring their heights in actual production. Current detection technologies include light detection and ranging (LIDAR), ultrasound, spectrum (multiple, high and infrared), and imaging techniques. Detection approaches can be classified by their detection platform, which mainly includes vehicle-mounted, small unmanned aerial vehicle (UAV), portable, and fixed systems. For example, [PERSON] carried a 2D LIDAR on a tractor to scan cotton from the top and measure the height of the plant [2]. [PERSON] proposed a crop height measurement system based on cameras and lasers to solve the problem of difficult identification of a crop's top and bottom points [3]. [PERSON] constructed an information acquisition system for peanut canopy height characteristics in the field, using LiDAR to obtain 3D point cloud data, and using polynomial curve fitting algorithm to obtain canopy height characteristics [4]. However, the changes in the terrain and the stability of the information acquisition system affect the results. [PERSON] proposed a tree diameter at breast height (DBH) estimation algorithm based on the randomized random sample consensus (RANSAC) model, which used a ground-based laser scanner combined with a cloth simulation filtering (CSF) algorithm to extract the point cloud at the DBH of the tree, and realized automatic tree DBH extraction [5]. [PERSON] used a six-rotor aircraft with RGB cameras to take high-resolution images at very low altitudes to estimate wheat plant density at emergence [6]. [PERSON] proposed a corn plant height measurement method based on an RGB-depth camera, which combined the watershed algorithm and skeleton extraction to measure the height of each corn plant [7]. [PERSON] carried image sensors on a UAV to collect remote sensing images of corn and obtain the height information of corn plants [8]. [PERSON] used a high-definition digital camera synchronized with a multi-spectral camera onboard an UAV to obtain citrus field images and then combined this process with a watershed algorithm to extract information such as the number and height of citrus plants [9]. [PERSON] built a machine-vision system for automatic tree classification based on UAV cameras and deep learning [10]. [PERSON] developed an image processing system capable of automatically measuring rice height and tiller number using a digital camera, but this method was only applied to single rice plants under special circumstances [11]. [PERSON] studied a low-cost, small-area-specific method for counting wheat ears with the help of camera-acquired images combined with color features and texture features; however, lighting in the actual environment greatly affected the color and texture features [12]. [PERSON] accurately measured tree height, stem diameter, etc., using low-cost handheld cameras and multi-view stereo photography, but this approach was only valid for tree seedlings [13].