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A refinement tactic is then applied to strengthen segmentation precision in intricate areas. At last, stem segmentation regularity is Increased by means of central axis fitting and length-centered filtering. In this particular analyze, we employ the Pheno4D dataset, which comprises a few-dimensional stage cloud facts of maize vegetation at unique progress phases, collected from greenhouse environments. Experimental final results clearly show the proposed algorithm achieves a mean precision of ninety eight.15% and an IoU of 84.eighty one% on the Pheno4D dataset, demonstrating potent robustness across various expansion phases. Segmentation time for every instance is diminished to four.eight s, featuring about a fourfold improvement in comparison with PointNet while maintaining superior precision and efficiency. Additionally, validation experiments on tomato point cloud facts ensure the proposed method’s strong generalization capability. On this paper, we existing an algorithm that addresses the shortcomings of common solutions in complex agricultural environments. Precisely, our technique increases effectiveness and precision while decreasing dependency on significant-top quality annotated data. This solution don't just provides significant precision and a lot quicker computational performance but will also lays a robust specialized foundation for top-throughput crop management and precision breeding. Whole article (This short article belongs into the Area Precision and Digital Agriculture)
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