# A Multi-Plant Height Detection Method Based on Ruler-Free Monocular Computer Vision
## 1 Introduction
used an RGB-D depth camera to obtain color and depth images of corn, directly obtained the depth information of plants and locations, and obtained the plant height by calculating the height difference between the highest point of the leaves and the ground point [15]. Based on the principle of binocular parallel vision, [PERSON] et al. used the internal reference of a binocular camera as the reality reference to calibrate and correct the 3D information of the plant [16]. [PERSON] et al. used the ResNeXt network to realize the depth estimation of monocular images and used the estimated position depth feature as the reference to further calculate the plant height information [17]. The above methods mainly realize the height estimation of single plants under specific environments or conditions, meaning they are not completely applicable to all plant height measurement scenarios and still have certain limitations [18; 19]. The plant height measurement methods based on scale are highly dependent on the setting of the scale, and the measurement accuracy is affected by the degree and quality of the scale, so their applicability is poor. The depth camera is composed of an infrared emitter and an infrared sensor, and the depth measurement is achieved by measuring the laser speckle objective [20]. This method is easily affected by environmental factors such as the material, surface characteristics, and illumination. Compared with the height measurement method based on a ruler reference, the RGB-D depth camera does not need to set additional calibration objects. However, this method has high requirements for the quality of the depth image, and its application is limited due to its sensitivity to the measurement environment. Compared with the RGB-D depth camera, the plant height measurement method based on binocular vision has a better environmental adaptability and is less sensitive to environmental lighting conditions, so it can be applied to most indoor and outdoor scenes. However, its perceptual range of measurement is affected by the fixed internal and external parameters of the camera, and there are still certain limitations in the application. The monocular depth estimation method based on a neural network only uses a single camera to complete the measurement, which requires the least equipment and can be applied to more scenes than the above methods. However, this method needs to refer to the depth information obtained by the neural network, and the results depend on the accuracy of the output results of the neural network. For neural networks and deep learning methods, input disturbances or changes may significantly affect the output results, and changes in the plant height measurement environment may affect their accuracy. The accuracy of plant height measurements in different environments has high requirements for the generalization ability of the model [21; 22; 23]. This paper proposes a multi-plant height measurement method based on scale-free monocular computer vision, which can overcome the limitations of the above methods in terms of equipment, environment, and sensing range to some extent, and can achieve the simultaneous measurements of multiple plants. In order to meet the current agricultural requirements for plant height measurement and adapt to different measurement environments, this paper provides an idea.