Method for detecting content of heavy metals in lettuce based on multi-scale images

Abstract

The invention discloses a method for detecting the content of heavy metals in lettuce based on multi-scale images. The method is technically characterized by comprising the following steps of: 1, acquiring images of the lettuce by using three cameras, and normalizing the images; and 2, establishing a neural network learning model by taking the normalized lettuce image obtained in the step 1 as input, inputting the collected sample data into the neural network for learning, finally obtaining a multi-scale hierarchical neural network learning model, detecting and identifying the input lettuce image by using the multi-scale hierarchical neural network learning model obtained by learning, and judging whether the heavy metal content exceeds the standard or not. Data are collected through the optical camera, nondestructive and non-contact detection of the content of the heavy metal in the edible lettuce is achieved through analysis and modeling of imaging data of the camera, the speed is high, operation is convenient, environmental protection and safety are achieved, the industrial requirements of modern food safety can be met, and effective replacement of a traditional detection method is achieved.

Description

technical field [0001] The invention is oriented to the application field of food detection, and in particular, relates to a detection device and method for heavy metal content of edible lettuce based on multi-scale visual learning. Background technique [0002] Heavy metal pollution is one of the severe challenges faced by my country's agricultural development. At present, heavy metal pollutants caused by massive discharge of three wastes, mineral development and unscientific use of pesticides are gradually destroying the natural environment, especially causing irreversible damage to the atmosphere, water and soil. Due to the transferability, concealment and accumulation of heavy metal pollutants, it is quite difficult to prevent and control them. If heavy metals enter the human body through the food chain, it will cause irreparable damage to the human body. It is imminent to carry out rapid non-destructive detection of heavy metals in edible vegetables. [0003] Lettuce...

AI Technical Summary

Problems solved by technology

Although these chemical detection methods have the advantages of high precision and strong sensitivity, they are cumbersome to operate, pollute the environment, take a long time, and cannot achieve real-time detection, which is not conducive to popularization.

At present, there is also a method of using images to detect the heavy metal content of corn leaves and other plant leaves, but it is greatly affected by ambient light, and its detection relies on a single image, so it has high requirements for image acquisition devices

The inherent defects of the image acquisition device will have a great impact on the detection results

Moreover, the algorithm model is complex, the detection accuracy is low, and it takes a long time

Images