Crop yield prediction method and system based on unmanned aerial vehicle low-altitude remote sensing information

Abstract

The invention relates to a crop yield prediction method and system based on low-altitude remote sensing information of an unmanned aerial vehicle. The method comprises the following steps: acquiring aplurality of images acquired by aerial photography of an unmanned aerial vehicle; the unmanned aerial vehicle aerial photography adopts a multispectral camera to shoot a crop canopy to obtain reflection spectrum images of various different wave bands, and the multiple images are spliced to obtain a spliced image; performing spectral correction on the spliced image to obtain the reflectivity of each pixel point in the spliced image; segmenting the spliced image by adopting a threshold segmentation method to obtain a crop yield prediction target area; performing correlation analysis on the reflectivity of each waveband and the crop growth condition and yield by adopting a Pearson correlation analysis method to obtain a characteristic waveband; constructing a yield measurement factor according to the characteristic wave band; and determining a crop yield prediction value of the crop yield prediction target area according to the yield prediction factor and the crop planting area of the crop yield prediction target area. The crop yield prediction accuracy can be improved, and the labor intensity is reduced.

Description

Technical field [0001] The invention relates to the field of yield prediction, in particular to a crop yield prediction method and system based on low-altitude remote sensing information of drones. Background technique [0002] Monitoring the planting area and output of food crops has always been highly valued. Both the government and scientific researchers are committed to studying how to understand and accurately grasp the information of food crops planting output in a timely manner. At the same time, timely access to crop planting information can provide a scientific basis for the government to formulate agricultural production policies, which is of great significance for ensuring food security. In agricultural production, timely and accurate forecasting of crop yields can also be of great significance for better implementation of crop management, especially in the determination of crop insurance, harvest planning, storage requirements, cash flow budget, nutrition, pesticides,...

AI Technical Summary

Problems solved by technology

[0003] Traditional crop yield prediction methods mainly rely on farmers' experience knowledge or large-scale destructive sampling, so there are problems of high labor intensity and low prediction accuracy

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