Seedling counts and estimated distance between plants play a crucial role in assessing seed and planting quality, as well as estimating subsequent yields. Traditional manual measurements are time-consuming and labor-intensive, leading to a shift towards using unmanned aerial vehicle (UAV) aerial photography technology and deep learning (DL) processing methods for statistical analysis. This study proposes an efficient and rapid model for counting maize plants and estimating plant distances. The model utilizes an UAV at a fixed flight altitude to collect data and employs the YoloV5 algorithm for maize plant and plant center detection. Subsequently, the DeepSort algorithm is employed for target tracking to achieve accurate maize seedling counting. Finally, the plant distances between the maize are estimated by incorporating the principle of similar triangles. Experimental results demonstrate a counting accuracy of 97.5% for the maize counting model, as well as high speed and accuracy in maize plant distance estimation. The model significantly reduces the time required for field plant information counting and is suitable for research on seeding plant information counting, facilitating its application in maize and other crop production processes.