Field crop wide-view image generation method and system based on deep learning

Abstract

This invention discloses a deep learning-based method for generating wide-field crop images, belonging to the field of smart agriculture technology. The method utilizes a mobile platform equipped with multiple high-resolution RGB cameras to simultaneously acquire top-view images of crops after calibration and distortion correction. A dataset is constructed and enhanced based on the acquired images, and a cascaded network of SuperPoint and SuperGlue is built. The former detects key points and generates descriptors, while the latter achieves feature matching through a graph neural network. Intrinsic points are selected using the RANSAC algorithm, the homography matrix is ​​calculated, the images are aligned through perspective transformation, and multi-band fusion is used to eliminate seams, gradually stitching together to generate a wide-field image. This invention solves the problems of limited field of view, severe distortion, and insufficient generalization ability of traditional techniques, providing high-resolution and accurate crop images and reliable data support for crop phenotypic analysis.

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