An image segmentation method for crop weeds in field

Abstract

The invention belongs to the technical field of image processing and discloses an image segmentation method of field crop weeds. The image data of the field crop weeds is collected by an image acquisition module using a camera. The main control module enhances image definition by image processing software through image enhancement module. The weed image is recognized by using the depth stack network model through the image recognition module. A feature extraction module extracts and segments the weed image data according to the identification content by using the image processing software; Thewarning module uses the warning device to warn according to the extracted weed data; Storing the collected weed image data through a data storage module by using a memory; And the weed image data isdisplayed by the display module. Deep-stacking network adopt by that image recognition module of the invention not only has strong feature expression and classification ability, but also reduces traintime and recognition time, and enhances robustness of weed recognition.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image segmentation method for field crops and weeds. Background technique [0002] Crops refer to various plants cultivated in agriculture. A type of living thing. Including food crops and economic crops (oil crops, vegetable crops, flowers, grass, trees) two categories. Edible crops are one of the sources of basic human food. "Food is paramount to man", which expresses the relationship between man and food, and a reasonable diet can bring health to human beings. Food self-sufficiency is the foundation of a country's sustainable development. The growth of crops is inseparable from scientific and technological production technologies, as well as machinery and equipment manufactured by new industries that can assist agricultural production. However, the contrast of the collected images of existing field crops and weeds is still low, showing that the ima...

AI Technical Summary

Problems solved by technology

[0004] (1) The contrast of the collected images of existing field crops and weeds is still low, and the displayed images are distorted; at the same time, the recognition degree of field crops and weeds is not high

[0005] (2) The focusing speed of the camera in the prior art is relatively slow and the accuracy is poor, which is not conducive to obtaining

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