Real-Time Counting and Height Measurement of Nursery Seedlings Based on Ghostnet–YoloV4 Network and Binocular Vision Technology

Traditional nursery seedling detection often uses manual sampling counting and height measurement with rulers. This is not only inefficient and inaccurate, but it requires many human resources for nurseries that need to monitor the growth of saplings, making it difficult to meet the fast and efficient management requirements of modern forestry. To solve this problem, this paper proposes a real-time seedling detection framework based on an improved YoloV4 network and binocular camera, which can provide real-time measurements of the height and number of saplings in a nursery quickly and efficiently. The methodology is as follows: (i) creating a training dataset using a binocular camera field photography and data augmentation; (ii) replacing the backbone network of YoloV4 with Ghostnet and replacing the normal convolutional blocks of PANet in YoloV4 with depth-separable convolutional blocks, which will allow the Ghostnet–YoloV4 improved network to maintain efficient feature extraction while massively reducing the number of operations for real-time counting; (iii) integrating binocular vision technology into neural network detection to perform the real-time height measurement of saplings; and (iv) making corresponding parameter and equipment adjustments based on the specific morphology of the various saplings, and adding comparative experiments to enhance generalisability. The results of the field testing of nursery saplings show that the method is effective in overcoming noise in a large field environment, meeting the load-carrying capacity of embedded mobile devices with low-configuration management systems in real time and achieving over 92% accuracy in both counts and measurements. The results of these studies can provide technical support for the precise cultivation of nursery saplings.