Corn aflatoxin detection method based on YOLO

Abstract

The invention discloses a corn aflatoxin detection method based on YOLO, and the method comprises the following steps: S1, building a machine vision detection platform, controlling an ultraviolet lamp light source and an industrial camera to be asynchronously triggered by a computer, and collecting a color RGB image of corn in real time; S2, carrying out image processing and segmentation on the collected corn image; S3, establishing a YOLO deep learning neural network detection model; and S4, identifying whether the segmented image is infected with aflatoxin or not in real time to obtain an identification result of whether the segmented image is infected with aflatoxin or not, and outputting the identification result. The RGB color image of the corn is extracted, detection is carried out by adopting a YOLO deep learning mode, and the detection precision and efficiency are greatly improved.

Description

technical field [0001] The invention belongs to the technical field of machine vision detection, and relates to a method for detecting aflatoxin in corn based on YOLO. Background technique [0002] Corn is one of the most important food crops in the world, widely distributed in China, the United States, Brazil and other countries. Corn is not only the main source of food for human consumption in my country, but also the main source of raw materials for livestock feed in animal husbandry. However, corn is easily infected by aflatoxin and mildewed during storage. Aflatoxin is a class of carcinogens designated by the World Health Organization Cancer Research Agency, and is a highly toxic and highly toxic substance. Therefore, accurate detection of whether corn is infected with aflatoxin has become an urgent problem to be solved. [0003] At present, the means of detecting whether corn is infected with aflatoxin is mainly the traditional manual visual inspection. By manually ...

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Problems solved by technology

[0003] At present, the means of detecting whether corn is infected with aflatoxin is mainly the traditional manual visual inspection. By manually extracting corn, it is detected whether the corn is infected with aflatoxin with the naked eye. Due to the strong subjectivity of this detection method and the workers working long hours After that, it will cause visual fatigue, easy to produce wrong sorting, heavy workload and low detection efficiency. Therefore, it is urgent to propose a more accurate and efficient detection method.

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