Efficient Semantic Segmentation for Large-Scale Agricultural Nursery Managements via Point Cloud-Based Neural Network DOI Creative Commons
Hui Liu, Jie Xu, Wen‐Hua Chen

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(21), P. 4011 - 4011

Published: Oct. 29, 2024

Remote sensing technology has found extensive application in agriculture, providing critical data for analysis. The advancement of semantic segmentation models significantly enhances the utilization point cloud data, offering innovative technical support modern horticulture nursery environments, particularly area plant cultivation. Semantic results aid obtaining tree components, like canopies and trunks, detailed on growth environments. However, precise from large-scale areas can be challenging due to vast number points involved. Therefore, this paper introduces an improved model aimed at achieving superior performance points. incorporates direction angles between improve local feature extraction ensure rotational invariance. It also uses geometric relative distance information better adjustment different neighboring features. An external attention module extracts global spatial features, upsampling strategy integrates features encoder decoder. A specialized dataset was created real environments experiments. Results show that surpasses several point-based models, a Mean Intersection over Union (mIoU) 87.18%. This precision environment analysis supports autonomous managements.

Language: Английский

Efficient Semantic Segmentation for Large-Scale Agricultural Nursery Managements via Point Cloud-Based Neural Network DOI Creative Commons
Hui Liu, Jie Xu, Wen‐Hua Chen

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(21), P. 4011 - 4011

Published: Oct. 29, 2024

Remote sensing technology has found extensive application in agriculture, providing critical data for analysis. The advancement of semantic segmentation models significantly enhances the utilization point cloud data, offering innovative technical support modern horticulture nursery environments, particularly area plant cultivation. Semantic results aid obtaining tree components, like canopies and trunks, detailed on growth environments. However, precise from large-scale areas can be challenging due to vast number points involved. Therefore, this paper introduces an improved model aimed at achieving superior performance points. incorporates direction angles between improve local feature extraction ensure rotational invariance. It also uses geometric relative distance information better adjustment different neighboring features. An external attention module extracts global spatial features, upsampling strategy integrates features encoder decoder. A specialized dataset was created real environments experiments. Results show that surpasses several point-based models, a Mean Intersection over Union (mIoU) 87.18%. This precision environment analysis supports autonomous managements.

Language: Английский

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