Method and system for detecting wheat scab based on depth convolution

Abstract

The invention discloses a detection method of wheat scab based on depth convolution, which comprises the following steps: S1, collecting a hyperspectral image pixel of wheat and wheat ear, and undersampling the hyperspectral image pixel to solve the problem of unbalanced data set, so as to obtain target data; S2, remodeling the two-dimensional image of the target data to obtain a gray-scale image,and preprocessing the gray-scale image based on the gray-scale image; S3, training the data by using the depth convolution neural network based on the preprocessed data; S4, analyzing the classification effect of the deep convolution neural network model based on the training results. The invention utilizes the hyperspectral imaging technology to carry out early rapid and non-destructive detection of wheat scab, and improves the accuracy of the regional classification result of the tested wheat.

Description

technical field [0001] The invention relates to the field of diagnostic methods for wheat scab, in particular to a detection method and system for wheat scab based on depth convolution. Background technique [0002] Wheat scab is an important disease of wheat. It is mainly distributed in humid and semi-humid regions, especially in temperate regions with humid and rainy climate. It has always been one of the most serious diseases in the wheat regions south of the Huaihe River and the middle and lower reaches of the Yangtze River in my country. . In recent years, the wheat head blight in Anhui has advanced from the Huainan wheat area in the frequent occurrence area to the Huaibei wheat area. Improper prevention and control of wheat scab infection will result in reduced yields, and severe crop failure, resulting in serious yield loss and quality impact on production. When wheat is infected by fungi, it will produce a variety of mycotoxins, the most serious of which is deoxyniv...

AI Technical Summary

Problems solved by technology

[0006] The invention provides a detection method of wheat scab based on deep convolution, which solves the problems of locality, hysteresis, destructiveness and indirectness in current mainstream disease detection methods. The model diagnoses wheat scab The speed is fast. During the learning process of this model, the convolutional structure can effectively reduce the dimensionality of high-dimensional hyperspectral image data, and the convolutional neural network model can effectively limit the number of parameters in the training process, so the depth Convolutional Neural Network Diagnoses Wheat Fusarium Fast

[0008] S1. Collect hyperspectral image pixels of wheat ears, and sample undersampling of hyperspectral image pixels to solve the problem of unbalanced data sets, so as to obtain three characteristics of background, health and disease for training depth volume The target data of the product neural network model;

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