Crop classification and identification method under strong noise background

Abstract

The invention discloses a crop classification and identification method under a strong noise background. The method comprises the following steps of: shooting a plurality of pictures of various cropsby using a multispectral camera to form a picture set; obtaining the NDVI value of each pixel point and segmenting the NDVI value into plant areas; replacing the non-plant area with a pure color background to highlight the plant area, performing image preprocessing to form a multispectral data set, and dividing the multispectral data set into three data sets, namely training, testing and verifying; inputting the training data set into a preset convolutional neural network model for training through a transfer learning method to obtain a convolutional prediction neural network model, and inputting the test data set into the convolutional prediction neural network model for accuracy testing to obtain a qualified convolutional prediction neural network model; and inputting the verification data set into the convolutional prediction neural network model, carrying out classification and identification on crops in the verification data set, and obtaining a classification result. According tothe method, the influence of a strong noise background on crop classification and recognition is reduced, and the recognition efficiency and prediction capability of the model are improved.

Description

technical field [0001] The invention belongs to the field of crop classification and recognition, and in particular relates to a crop classification and recognition method under a strong noise background. Background technique [0002] In image classification and retrieval based on deep learning, how to extract features from images and which features (color, texture, shape, etc.) important role. At present, when using RGB images to identify crops through deep learning, the accuracy of deep learning has a significant downward trend due to the influence of interference features in a strong noise background and its own brightness. , to achieve the effect of classifying and identifying different crops, but this method cannot express the information of the color space distribution, and only records the information of the three bands of red, green and blue, and the other bands are lost, which is not conducive to the identification of crops in the background of strong noise. In mo...

AI Technical Summary

Problems solved by technology

At present, when using RGB images to identify crops through deep learning, the accuracy of deep learning has a significant downward trend due to the influence of interference features in a strong noise background and its own brightness. , to achieve the effect of classifying and identifying different crops, but this method cannot express the information of the color space distribution, only records the information of the three bands of red, green and blue, and the other bands are lost, which is not conducive to the identification of crops in the background of strong noise

In most deep learning, crop images are generally manually selected to avoid complex interference, but in practical applications, they may be affected by strong noise backgrounds, resulting in insufficient crop classification and recognition accuracy.

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