A Fruit Picking Sequence Planning Method Based on Visual Selection Attention Mechanism

Abstract

The invention discloses a fruit picking sequence planning method based on a visual selection attention mechanism, including fruit image collection and registration, fruit visual saliency calculation, fruit target recognition and segmentation, fruit picking decision attribute analysis, and then the average of each fruit Salient degree, central depth value and occlusion coefficient are the three decision-making elements of picking priority sorting. The fruit picking priority sorting method is used to comprehensively evaluate the attribute values ​​of the decision-making elements of each fruit to determine the picking priority of each fruit in the current scene. order; then according to the picking priority order of each fruit, the three-dimensional coordinates of each fruit are sent to the fruit robot in turn, and the fruit robot is driven to continuously pick. The invention can pick higher-quality fruits under the condition of relatively low energy consumption, and has practical significance for promoting the increase of production and income of my country's fruit industry and the intelligentization of automatic picking equipment.

Description

technical field [0001] The invention belongs to the field of visual bionics of agricultural robots, and in particular relates to a fruit picking sequence planning method based on a visual selection and attention mechanism. Background technique [0002] With the start of smart agriculture, there are more and more applications of smart agricultural systems. Fruit picking robots are an emerging field in smart agriculture. How to improve the efficiency and quality of fruit picking has always been the focus of research in this field. At present, there are two main picking methods for vision-based fruit picking robots: (1) Use the camera to continuously take pictures from multiple random directions until the fruit target is found, and then control the robot to pick; The uncertainty of the environment wastes a lot of repetitive actions, resulting in low efficiency. (2) Use the camera to take images at a distance, detect all fruit targets in the current scene at one time, and then ...

AI Technical Summary

Problems solved by technology

At present, there are two main picking methods for vision-based fruit picking robots: (1) Use the camera to continuously take pictures from multiple random directions until the fruit target is found, and then control the robot to pick; The uncertainty of the environment wastes a lot of repetitive actions, resulting in low efficiency

(2) Use the camera to take images at a distance, detect all fruit targets in the current scene at one time, and then control the robot to pick them one by one; but this approach creates a problem, that is, how to determine which fruit the robot picks first and which fruit

To solve this problem, there are mainly two picking methods. One is to pick from left to right and from top to bottom, but the distance between the fruit and the robot is not considered, so the efficiency cannot be guaranteed; the second is to plan the shortest path of the entire picking process. Go picking to reduce the consumption of the robot and improve the picking efficiency of the robot

However, the above fruit picking methods all ignore the quality of the picked fruit, resulting in easy picking of fruits with uneven quality, which increases the heavy burden of subsequent fruit quality sorting work, and the labor cost remains high, which directly affects fruit farmers. overall revenue

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