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The method of leaf area extraction based on 3D point clouds offers advantages such as non-contact, high efficiency, and high-precision, making it well-suited to meet the demands of modern agriculture for rapid acquisition and accurate assessment of leaf area. Focusing on summer maize during its full growth period in field conditions, with four different fertilization treatments, each containing two sample plots, a self-developed handheld structured light crop 3D scanner was used, point cloud data were collected throughout the entire growth period of summer maize, and a series of point cloud preprocessing processes, including point cloud registration, denoising, and downsampling, were proposed. Subsequently, the PCT deep learning point cloud segmentation network was applied to accurately segment the crop organ point clouds, extracting the maize leaf point cloud data and successfully calculating the leaf area. The segmentation results showed that the PCT network performed excellently in the point cloud segmentation accuracy for maize organs, with the precision, recall, F1-score, and IoU metrics for the leaf point cloud all exceeding 95%, and the segmentation metrics for other organs also being above 75%. Significant differences were observed in the leaf area extraction results across different growth stages. During the seedling, jointing, and full growth stages, the extraction results were excellent, with R2 values of 0.906 2, 0.983 8, and 0.994 9, and RMSE values of 221.34 cm2, 172.77 cm2, and 206.64 cm2, respectively. However, in the mature stage, the model’s performance significantly was decreased, with an R2 of 0.517 8 and an RMSE of 209.32 cm2. Under different fertilization levels, the leaf area extraction results were consistently good, with R2 values above 0.98. As the fertilization amount changed, the RMSE showed a trend of first decreasing and then increasing, with specific values of 176.38 cm2, 106.36 cm2, 110.18 cm2, and 270.34 cm2. In conclusion, the method proposed can accurately and effectively extract the leaf area of individual maize plants in field conditions, providing reliable data support for precision agriculture.