Method and device for detecting ripeness of fruits and vegetables

Abstract

The invention discloses a method and device for detecting ripeness of fruits and vegetables. The method comprises the following steps: obtaining video information of fruits and vegetables; performing image enhancement processing on the obtained video information to improve the identification of the colors of the fruits and vegetables in the image; converting the image after the enhancement processing from a three-primary color light mode into an HSV color attribute mode to obtain the tone of the fruits and vegetables in the image; and detecting the ripeness of the fruits and vegetables in the image according to the tone of the fruits and vegetables in the image. By adoption of the method and device for detecting the ripeness of fruits and vegetables provided by the invention, since the video information of the fruits and vegetables can be processed to obtain the tone of the fruits and vegetables in the image so as to detect the ripeness of the fruits and vegetables, the accuracy of detecting the ripeness of the fruits and vegetables is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method and device for detecting maturity of fruits and vegetables. Background technique [0002] In the fruit and vegetable planting industry, accurately identifying ripe fruits is the primary task of fruit picking, and it is also the key to the successful realization of automated picking operations. [0003] At present, the ripeness of the fruit needs to be manually judged. Monitoring equipment can be installed in the planting area, and the video information of the fruit in the planting area can be obtained through the monitoring equipment, and the planters can judge whether the fruit is ripe through the video information. However, since farmland surveillance videos are easily affected by conditions such as light and weather, especially in heavy rain, heavy fog, thick smoke or at night, the contrast and color of the scene in the acquired video will degrade, and the re...

AI Technical Summary

Problems solved by technology

However, since the farmland surveillance video is easily affected by conditions such as light and weather, especially in heavy rain, heavy fog, thick smoke or at night, the contrast and color of the scene in the acquired video will be degraded, and the recognizability will be reduced, resulting in Difficulty judging fruit ripeness

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