An open environment-oriented single-stage crop leaf anomaly identification method
By combining a single-stage deep learning framework with image segmentation and reconstruction techniques, the problems of slow speed and low accuracy in leaf lesion recognition in existing technologies have been solved, achieving efficient and accurate lesion recognition, which is suitable for large-scale agricultural monitoring and leaf image processing of diverse crop species.
CN117876338BActive Publication Date: 2026-06-09HEBEI AGRICULTURAL UNIV.
Patent Information
- Authority / Receiving Office
- CN Β· China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HEBEI AGRICULTURAL UNIV.
- Filing Date
- 2024-01-15
- Publication Date
- 2026-06-09
Smart Images
Figure CN117876338B_ABST
Abstract
The application discloses an open environment-oriented single-stage crop leaf anomaly identification method, and belongs to the technical field of computer vision and plant pathology, and the method comprises the following steps: S1, data acquisition and preprocessing; S2, based on the data set of step S1, data enhancement is carried out; S3, a single-stage training model is constructed; S4, model training and reasoning are carried out.The application adopts the above-mentioned open environment-oriented single-stage crop leaf anomaly identification method, which not only reduces the difficulty of obtaining training data, simplifies the implementation process, improves the accuracy and efficiency of disease spot identification, but also integrates leaf segmentation and disease spot identification in the same deep learning framework, provides a practical and efficient tool for large-scale agricultural monitoring and plant pathology research, has good generalization ability and adaptability in processing leaf images of different crop types and various environmental conditions, and has wide application prospect and great practical value.
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