A method, device, equipment and medium for estimating field straw covering amount

By acquiring multispectral remote sensing images in the field study area, the correlation between spectral bands and straw coverage was optimized using a random forest model and a genetic-chaotic particle swarm optimization algorithm. Combined with the Monte Carlo method, the problem of the inability to accurately estimate the straw coverage of the entire farmland in existing technologies was solved, and accurate global straw coverage estimation was achieved.

CN118865172BActive Publication Date: 2026-03-24JILIN AGRICULTURAL UNIV
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Patent Information

Application Number
CN202410907543.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-07-08
Publication Date
2026-03-24
Estimated Expiration
2044-07-08

AI Technical Summary

Technical Problem

Existing technologies cannot accurately estimate the amount of straw cover on an entire farmland, while remote sensing technology can only obtain estimates of the amount of straw cover in a localized area.

Method used

By acquiring multispectral remote sensing images of each sampling point in the field study area, the exponential values ​​of different spectral bands are extracted. The correlation between spectral bands and straw coverage is learned by training a random forest model. The model is then optimized using a genetic-chaotic particle swarm optimization algorithm, and combined with the Monte Carlo method to estimate the global straw coverage.

Benefits of technology

It enables accurate estimation of straw coverage in the field, improves estimation accuracy and efficiency, is suitable for large-scale testing, and meets the needs of conservation tillage.

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Abstract

The application discloses a kind of estimation methods, devices, equipment and medium of field straw covering quantity, it is related to farmland conservation tillage technical field, comprising: according to the corresponding relationship of different spectral bands and vegetation index, the index value of different spectral bands corresponding to each sampling point is obtained, the correlation of the index value of different spectral bands corresponding to each sampling point and the straw covering quantity of corresponding sampling point is determined;The straw covering quantity of each sampling point is sorted from small to large, and the sampling point with strong correlation is selected and the index value of corresponding different spectral bands is used to train random forest model;The index value of different spectral bands corresponding to each sampling point in the field research area is input into the model for estimating straw covering quantity to obtain the straw covering quantity of each sampling point;And the average of the straw covering quantity of each sampling point is used as the field straw covering quantity.The application can accurately estimate the straw covering quantity of whole farmland.
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