Winter wheat drought-resistant water-saving variety identification and selection method, system and device

Through multi-source, multi-time sequence data fusion and machine learning algorithms, the traditional winter wheat drought-resistant and water-saving variety identification methods are solved, with low efficiency, large error and single evaluation methods, and high-throughput and multi-dimensional variety identification are achieved, improving measurement efficiency and accuracy.

CN120561527APending Publication Date: 2025-08-29NANJING AGRICULTURAL UNIVERSITY
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Patent Information

Application Number
CN202511071102.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-31
Publication Date
2025-08-29

AI Technical Summary

Technical Problem

The traditional winter wheat drought-resistant and water-saving variety identification method is inefficient, and it depends on large errors in manual measurement, which cannot achieve dynamic monitoring, and the evaluation dimension is single. The existing methods cannot guarantee water-saving performance, which restricts the identification and promotion of excellent varieties.

Method used

Multi-source and multi-time sequence data fusion is adopted, image and non-image data are obtained using remote sensing technology, biomass, LAI and yield prediction models are trained through machine learning algorithms, combined with lodging detection, a multi-dimensional evaluation system is built to achieve high-throughput variety identification.

Benefits of technology

It improves measurement efficiency and accuracy, realizes dynamic monitoring during the reproductive period, builds a multi-dimensional evaluation system, and significantly improves the scientificity and comprehensiveness of variety selection.

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Abstract

The invention provides a method, a system and a device for identifying and selecting drought-resistant and water-saving winter wheat varieties. The method comprises the following steps: acquiring multi-source and multi-time-sequence wheat remote sensing data; non-image data is preprocessed; dividing the preprocessed non-image data into a non-image training set and a non-image test set which are used for biomass prediction, LAI and yield prediction, and dividing the image data into an image training set and an image test set which are used for lodging detection; training a biomass prediction model, a yield prediction model and an LAI prediction model by using the non-image training set and the non-image test set; training a lodging detection model by using the image training set and the image test set; the trained biomass prediction model, yield prediction model and lodging detection model are utilized to predict the biomass, LAI, yield and lodging conditions of the selected varieties; and determining dominant varieties according to biomass, plant height, LAI, yield and lodging conditions. According to the invention, high-throughput and high-precision variety identification and selection can be realized.
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