Crop growth whole-course digital evaluation method based on unmanned aerial vehicle vision

Abstract

The invention discloses a crop growth whole-course digital evaluation method based on unmanned aerial vehicle vision, and the method comprises the steps: firstly carrying out the rasterization of an unmanned aerial vehicle operation region, and arranging a Bluetooth positioning benchmark; aiming at a rasterized working area, shooting a five-view image of each grid by the unmanned aerial vehicle, and carrying out crop plant segmentation, five-view three-dimensional reconstruction, plant leaf surface and fruit segmentation and five-view two-dimensional reconstruction; correcting the reconstructed target image; calculating parameters of crop plants, leaf surfaces and fruits by utilizing the corrected reconstructed image; digitally evaluating the growth condition and pest and disease damage condition of the crops in each growth stage of the crops by utilizing the parameters obtained by calculation; meanwhile, through calculation of field environment parameters, the dry condition and the waterlogging condition of the crop growth environment are comprehensively evaluated. Whole-course digital evaluation is provided for the crop growth process, and the method has a guiding effect on agricultural production.

Description

technical field [0001] The invention relates to a method for digitally evaluating the whole process of crop growth based on drone vision, and belongs to the technical field of crop intelligent monitoring. Background technique [0002] In recent years, the vigorous development of agricultural Internet of Things, wireless network transmission and other technologies has greatly promoted the massive explosion of agricultural product monitoring data, and agriculture has stepped into the era of big data. Modern agriculture obtains, collects, and analyzes data through technical means to effectively solve problems such as agricultural production and market circulation. Internet technology drives the transformation of agricultural production to intelligence, which is of great significance to the transformation and upgrading of my country's modern agriculture. [0003] At the same time, drones, this "cool" cutting-edge technology, are joining hands with agriculture, and the number of...

AI Technical Summary

Problems solved by technology

[0008] 1. The method of only using spectral information for processing cannot achieve high-resolution restoration of plants, leaves, and fruits, and cannot reconstruct and restore richer texture information, making the parameter extraction accuracy of the entire crop poor.

[0009] 2. Use the single-view image taken by the drone to perform direct spectral processing, calculate and obtain the parameter data of the crop, and cannot reconstruct the 3D image of the plant, and the 2D reconstruction of the leaf and fruit images, the accuracy is low, and it cannot Accurately extract parameters of crops

[0010] 3. In the picture positioning, using GPS positioning method usually leads to a large positioning error, and the error is in the range of 10m-20m

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