A method for physical modal segmentation and physiological quality collaborative evaluation of seedling stems and leaves

By utilizing the image difference method based on the resonant frequency difference between leaves and stems, the problem of metamerism in stem and leaf segmentation is solved, achieving high-precision stem and leaf segmentation and physiological quality assessment. This method is suitable for low-computing-power equipment and supports automated agricultural operations.

CN121921578BActive Publication Date: 2026-05-26EAST CHINA JIAOTONG UNIVERSITY
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Patent Information

Application Number
CN202610360585.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-24
Publication Date
2026-05-26
Estimated Expiration
2046-03-24

AI Technical Summary

Technical Problem

Existing visual recognition technologies suffer from metamerism in stem and leaf segmentation, leading to missegmentation. Furthermore, deep learning models are difficult to deploy on low-power devices, making it hard to meet the needs of large-scale agricultural production.

Method used

By outputting a horizontal sinusoidal excitation signal, the image difference is performed and a dynamic mask is extracted using the resonant frequency difference between the leaves and stems to achieve stem-leaf segmentation, and physiological stiffness and quality are determined based on the measured amplitude.

Benefits of technology

It improves the accuracy and robustness of stem and leaf identification and segmentation, meets the requirements of low computing power, realizes early non-destructive identification of water stress, and provides a basis for decision-making in precise horticultural regulation.

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Abstract

This application discloses a method for collaborative evaluation of the physical modal segmentation of seedling stems and leaves and their physiological quality, belonging to the field of agricultural robot perception and precision detection. The method includes the following steps: outputting a horizontal sinusoidal excitation signal and acquiring at least two images of the seedling under two morphological conditions within the leaf resonant frequency range; performing image difference on any two images to obtain a grayscale difference map; extracting a dynamic mask of the leaves from the grayscale difference map; segmenting the leaves and stems in one of the two images based on the dynamic mask; determining the measured amplitude of the leaves based on the dynamic mask; determining the physiological stiffness of the leaves based on the measured amplitude; determining the stiffness difference between the physiological stiffness and a stiffness threshold, and determining the ratio of the stiffness difference to the stiffness threshold; and determining the seedling quality based on the ratio. This application improves the accuracy and robustness of stem and leaf recognition and segmentation, and meets the requirements for early non-destructive recognition under low computational power and water stress.
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