Garlic harvester automatic row control method and system based on machine vision

By analyzing plant density through deep learning networks and dynamic models, combined with adaptive visual compensation and multi-time-scale predictive control, the operating accuracy and efficiency issues of garlic harvesters in areas with different plant densities were solved, intelligent control of garlic harvesters was achieved, and operational accuracy and adaptability were improved.

CN120652829AActive Publication Date: 2025-09-16BEIJING UNIV OF POSTS & TELECOMM
6 Cites 1 Cited by

Patent Information

Application Number
CN202511157388.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-19
Publication Date
2025-09-16
Estimated Expiration
2045-08-19

AI Technical Summary

Technical Problem

Existing garlic harvesters suffer from insufficient operating precision, low efficiency, decreased visual recognition accuracy, and poor control effects when facing areas with different plant densities. In particular, the damage rate is high in high-density areas and the efficiency is low in low-density areas. There is also a lack of a compensation mechanism for visual perception due to speed changes.

Method used

A deep learning network is used to analyze plant density characteristics in real time and generate a density distribution map. The travel speed is dynamically adjusted based on a density-speed coupling dynamic model. Through an adaptive visual compensation mechanism and a multi-time-scale predictive control framework, short-term precision control and long-term path planning are coordinated to achieve intelligent control of the garlic harvester.

Benefits of technology

It improves the accuracy and adaptability of garlic harvesting operations, reduces the missed picking rate and damage rate, improves operational efficiency and adaptability, and ensures smooth and efficient operations in complex environments.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a garlic harvester automatic row control method and system based on machine vision, and belongs to the field of agricultural machinery automatic control, and the method comprises the steps: employing a deep learning network to generate a plant density distribution diagram based on image data obtained through machine vision; based on the plant density distribution diagram, a density and speed coupling dynamic model is constructed, and the optimal advancing speed of the garlic harvester under the current plant density condition is solved through a nonlinear mapping function and an optimization algorithm; compensating visual perception based on the optimal advancing speed to obtain compensated visual perception information; based on the compensated visual perception information and the optimal advancing speed, constructing a multi-time scale prediction control framework to obtain a control strategy; and based on actual operation data, optimizing a density-speed coupling dynamic model and a control strategy, and realizing automatic row control of the garlic harvester. According to the invention, traditional multiple sets of speed control and path detection strategies are replaced, and the system adaptability is improved while the complexity of the control system is reduced.
Need to check novelty before this filing date? Find Prior Art