A large-scale soybean and corn mapping method based on agronomic knowledge-driven index

Through an index method based on agronomic knowledge-driven, GWCCI and CARI are used to construct corn identification index GC, and the decision tree model GGC is constructed in combination with GWCCI and GC. The problem of relying on long-term time series images and sample data in the existing technology is solved, and the efficient identification and classification of soybean corn is achieved, which improves the spatial and temporal generalization ability and application potential of the algorithm.

CN119478552BActive Publication Date: 2025-04-01INST OF AGRI RESOURCES & REGIONAL PLANNING CHINESE ACADEMY OF AGRI SCI
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Patent Information

Application Number
CN202510031361.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-01-09
Publication Date
2025-04-01
Estimated Expiration
2045-01-09

AI Technical Summary

Technical Problem

The existing large-scale crop classification algorithm relies on long-term time series images and training samples, resulting in large data volume, complex calculations, weak physical interpretability of identification indicators, and poor spatial and temporal generalization ability.

Method used

Using an index method based on agronomic knowledge-driven, a corn identification index GC is constructed by calculating the greenness moisture comprehensive index (GWCCI) and the carotenoid index (CARI), and a decision tree model GGC is constructed in combination with GWCCI and GC to achieve efficient identification and classification of soybean corn.

Benefits of technology

The identification and classification of soybean corn without relying on time series images and sample data is realized, which reduces the data volume and calculation complexity, improves the biophysical basis and spatial and temporal generalization ability of identification indicators, and has great application potential.

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Abstract

A large-scale soybean and corn mapping method based on an agronomic knowledge-driven index. The present invention relates to a large-scale soybean and corn mapping method based on an agronomic knowledge-driven index. The present invention solves the problem of the lack of efficient and concise crop identification indicators in the prior art. Method: 1. Calculate the GWCCI and CARI of the multispectral image; 2. Construct a new corn identification index GC; 3. The pixel values ​​of the multispectral remote sensing image pixels are defined as,, and are three thresholds for identification and extraction. The method of the present invention fills the gap in the corn identification index, and further improves the ability of the green moisture comprehensive index to filter out other crops. The GGC model described in the present invention has strong spatiotemporal generalization capabilities and does not need to rely on training samples and long time series data. The soybean and corn classification method driven by agronomic knowledge provides a new way to achieve large-scale crop mapping.
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