Soil aggregate solid-phase characteristic detection method and device based on deep learning

Through the solid phase characteristic detection method of soil agglomerates based on deep learning, unsupervised segmentation is used to use image acquisition and deep learning models, which solves the problems of low detection efficiency and difficulty in obtaining labels in the existing technology, and achieves efficient and accurate soil agglomeration Body solid phase characteristic detection.

CN119151884APending Publication Date: 2024-12-17SOUTH CHINA AGRICULTURAL UNIVERSITY
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Patent Information

Application Number
CN202411190234.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-08-28
Publication Date
2024-12-17

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Abstract

The invention aims to provide a soil aggregate solid-phase characteristic detection method and device based on deep learning. The method comprises the following steps: acquiring a soil aggregate sample image; segmenting the soil aggregate sample image by adopting a soil aggregate segmentation model to obtain a mask image of each aggregate shape; and according to the mask image, calculating by using opencv to obtain the solid-phase characteristics of the soil aggregate. The method comprises the following steps: performing image acquisition on a to-be-detected soil aggregate sample, and calculating to obtain the solid-phase characteristic of the to-be-detected soil aggregate sample through a deep learning-based soil aggregate solid-phase characteristic detection model; the detection process is simple, and the method has the characteristics of high detection efficiency and high detection precision.
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Citation Information

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