Automatic fruit identification method of apple picking robot on basis of support vector machine

Abstract

The invention discloses an automatic fruit identification method of an apple picking robot on the basis of a support vector machine. An apple orchard colored image under natural illumination is collected, and a vector median filter is adopted to pretreat the apple colored image; after pretreatment, the image is cut with the method of combining region growing with color characteristics; the color characteristics and the geometry characteristics of the cut apple colored image are respectively extracted; apple fruits are indentified with the mode identification method of the support vector machine; and finally, the fruits are accurately positioned. The identification method of the support vector machine of the invention which integrates color characteristics and shape characteristics has higher apple fruit identification precision rate than the precision rate when only color characteristics or shape characteristics are adopted and has better identification effect; the algorithm is easy to realize, the operation time is short, and the identification performance is superior to a commonly used neural network method, so that the identification method shows advantages in small sample learning.

Description

technical field [0001] The invention relates to an agricultural fruit and vegetable picking robot, in particular to the technical field of image recognition of an apple picking robot, which automatically recognizes apple fruits based on a support vector machine method. Background technique [0002] In the vision system of an apple picking robot, the identification and positioning of the fruit is the key link. Whether the fruit can be identified quickly and accurately directly affects the real-time performance and reliability of the robot. Most of the operations in the orchard rely on sunlight as a light source. Changes in light conditions will lead to image quality degradation, resulting in incomplete or inaccurate target extraction, which will affect subsequent image processing links; and, at the same moment , Due to the different positions of the fruit, the light obtained by the fruit is also different. These factors need to be considered by the machine vision system. [...

AI Technical Summary

Problems solved by technology

However, these methods have problems such as low recognition accuracy and long running time, which largely restrict the real-time and multi-task performance of apple picking robots operating in natural environments.

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