Method and device for detecting stored-grain insects based on near infrared super-spectral imaging technology

Abstract

The invention relates to a method and a device for detecting stored-grain insects. A near infrared super-spectral imaging device for automatically acquiring grain screen underflows consists of five parts including a light box, a lighting unit, a displacement unit, a spectral imaging unit and a computer, wherein the spectral imaging unit comprises an indium-gallium-arsenic near infrared camera, an imaging spectrometer and a near infrared lens. The method for detecting stored-grain insects comprises the following steps of extracting the wavelength of an optimal spectrum synchronously containing data cube of both of live insect and dead insect, and determining the number of the live insects in stored-grain insects and the coordinate information of each live insect in the image; extracting sub-area of the image in which the live insects in the stored-grain insects are located, determining species information of the live insects in the stored-grain insects by identification software according to the near infrared spectrum characteristics of the live insects in the stored-grain insects, and realizing the automatic detection of the stored-grain insects.

Description

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AI Technical Summary

Benefits of technology

This patented method involves developing an algorithm called Live Grape Bee (LGB) based on artificial intelligence techniques like Neural Network Classifier Support Vector Machine(NBM). It achieves this by learning from data collected through experiments conducted over time and comparing it against previously trained models stored within memory. By doing these things we aimed at improving upon existing methods used during researches related to biodiversity conservation. Overall, LTB helps scientists better understanding how living beings work together effectively without being threatened.

Problems solved by technology

The patent text discusses the importance of accurately detecting and identifying pests in grain storage facilities. Currently, traditional methods such as sampling and trapping are used, but they have limitations in terms of efficiency and accuracy. The text suggests using computer vision technology to automatically extract grain samples, capture images of pests, and then use image processing and pattern recognition techniques to identify and classify the pests. However, existing computer vision systems can only classify a limited number of pest species and cannot distinguish between live and dead pests. Therefore, the technical problem addressed by the patent is to develop an automatic detection method and device that can accurately identify and count live pests in grain storage facilities.

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