Greedy genetic algorithm-based pot seedling thin planting and transplantation path optimization method

Abstract

The invention discloses a greedy genetic algorithm-based pot seedling thin planting and transplantation path optimization method. Health information of pot seedlings in a transplantation hole tray of a greenhouse pot seedling thin planting and transplantation machine is obtained through machine vision, and healthy seedling hole positions in the transplantation hole tray and empty hole positions in a target hole tray are subjected to label coding respectively; a greedy genetic selection principle is that the empty holes in the target hole tray are partitioned by column for carrying out current path optimization of a local genetic algorithm; the coding of a column of empty holes in the target hole tray is integrated with the coding of unplanned seedling holes in the transplantation hole tray, random path coding is generated, an initial population of the local genetic algorithm is formed, the selection, crossing, variation and reinsertion operations are circularly carried out until preset convergence generations, and individuals with maximum population fitness serve as local optimal paths; and the successive columns of planned local optimal paths are combined to generate a thin planting and transplantation path of the whole target hole tray. According to the method, an optimal path for greenhouse pot seedling thin planting and transplantation can be generated, the transplantation efficiency can be improved, and the real-time planning requirements of a control system are met.

Description

technical field [0001] The invention relates to a transplanting method for pot seedlings in agricultural machinery, in particular to a method for optimizing the transplanting path of pot seedlings with sparse planting based on greedy genetic algorithm. Background technique [0002] In greenhouse seedling cultivation, pot seedlings in high-density plugs need to be transplanted thinly to low-density plugs, and the seedling rate in the plugs is between 80-95%. The traditional manual identification and transplanting operation is low in efficiency, labor-intensive, and the consistency of transplanted seedlings is not good; the greenhouse seedling thinning transplanting machine detects the health status and position information of the pot seedlings through machine vision, and uses the end effector Grabbing and transplanting can solve the above problems. [0003] There are many holes in the low-density tray, and the randomness of the position of the healthy seedlings in the high-d...

AI Technical Summary

Problems solved by technology

The traditional manual identification and transplanting operation is low in efficiency, labor-intensive, and the consistency of transplanted seedlings is not good; the greenhouse seedling thinning transplanting machine detects the health status and position information of the pot seedlings through machine vision, and uses the end effector Grabbing and transplanting can solve the above problems

[0003] There are many holes in the low-density tray, and the randomness of the position of the healthy seedlings in the high-density tray is large. The transplanter controls the end effector to transplant from the origin to each hole with more options, that is, thin transplanting The length of the path is variable; due to the large amount of hole data, the method of calculating and selecting the shortest optimal path by the controller cannot meet the requirements of real-time control

The path planning method for the sparse transplanting of seedlings in pots needs to be developed

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