Crop spraying positioning method based on binocular vision gridding partition matching algorithm

Abstract

The invention discloses a crop spraying positioning method based on a binocular vision gridding partition matching algorithm and belongs to the technical field of medicine application of crops. The method comprises the following steps of: utilizing double cameras to calibrate target crops and obtaining an image of the target crops; respectively obtaining a binary image of a left camera target crop and a binary image of a right camera target crop; respectively carrying out gridding division on the binary image of the target crop obtained by the left camera and the binary image of the target crop obtained by the right camera; carrying out matched searching on each point of a left side gridding region in a right side gridding region to obtain mutually-matched points to form a matched point pair; calculating a left and right image vision difference by each matched point pair and solving a three-dimensional coordinate of the point of the target crop corresponding to the matched point pair; deleting an error point and carrying out fitting treatment on the point of the target crop to obtain a fitting curve or curve surface; and planning a sprayer path according to the fitting curve or curve surface. The crop spraying positioning method disclosed by the invention realizes extraction and positioning of a three-dimensional information outline of the target crop.

Description

technical field [0001] The invention belongs to the technical field of crop spraying, and in particular relates to a crop spray positioning method based on a binocular vision grid division matching algorithm. Background technique [0002] In the process of agricultural production, in order to prevent pests and diseases, it is often necessary to spray pesticides many times. The process of applying pesticides will cause certain harm to the environment and the health of operators, especially in greenhouse conditions, where the space is relatively closed and the frequency of pesticide application is high, so the hazards are more obvious. Through the development of an automated precision pesticide application system, the liquid medicine can be sprayed directly on the surface of the crops, avoiding waste of the liquid medicine, improving the use efficiency of the medicine liquid, reducing environmental pollution, ensuring the health of workers, and reducing labor intensity. The d...

AI Technical Summary

Problems solved by technology

Most of the pesticides have not been fully and effectively used. The root cause is that the methods and methods of pesticide application are not scientific and reasonable. In addition, extensive spraying is often used in the use of pesticides, and the technology and conditions for precise pesticide application are lacking.

[0005] 2. The spraying of pesticides is not uniform enough, and the residual liquid on the surface of crops exceeds the standard, especially for crops produced in greenhouses

Anti-drift and other technologies can also improve the spray effect to a certain extent, but fundamentally speaking, the accuracy of spray positioning will directly affect the spray quality

[0006] 3. The use adaptability of spray agricultural machinery is limited

Therefore, it is difficult to work effectively when the environment and crops change

[0007] 4. The robot positioning detection effect for automatic and precise spraying is not ideal, and the real-time performance is poor

Therefore, when there is a certain difference in the shape and size of the crops, different spray effects will be caused.

[0009] 6. The cost performance of the automatic pesticide spraying system is also a problem that restricts its wide application

The advantage of the binocular vision positioning system is that it has a wide range of applications, and it can directly realize the recognition and positioning of the target through a certain algorithm; its disadvantage is that the recognition and positioning algorithms are often complicated, and the real-time performance and robustness are poor. It is more difficult to detect objects with irregular shapes, complex environments, and poor lighting conditions

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