Wheat field weed detection method based on deep learning

Abstract

The invention discloses a wheat field weed detection method based on deep learning, and the method comprises the steps: collecting a large number of wheat and wheat field main weed pictures at different growth stages, building a data set, and dividing the data set into a training set and a test set; inputting the training set into a preset convolutional neural network model for training through atransfer learning method to obtain a crop weed classification recognizer, and testing the crop weed classification recognizer by using a test set to obtain a classification recognition result so as toperform fine adjustment; generating a large number of interest domains with different sizes on the to-be-detected picture by adopting a sliding window method, and inputting each interest domain intoa crop weed classification recognizer for classification and recognition to obtain a corresponding prediction category and a correct probability; and screening out an interest domain corresponding tothe local maximum correct probability of each type from all interest domains by applying a non-maximum suppression algorithm, and outputting a classification and positioning prediction result. According to the method, crops and weeds can be quickly and accurately identified and positioned, and the requirement for data is low.

Description

technical field [0001] The invention belongs to the field of weed detection, and in particular relates to a method for detecting weeds in wheat fields based on deep learning. Background technique [0002] Weeds in wheat fields are a great threat to the normal development of wheat and seriously affect the high and stable yield of wheat. There are various types of weeds in wheat fields, and weeds grow in all seasons, requiring different types of herbicides for control. The traditional extensive large-scale chemical weeding has produced many negative effects, such as polluting the environment and threatening food safety. The precise variable spraying technology sprays herbicides at fixed points and quantitatively according to the distribution of weeds and crops, which can reduce the impact on the field ecological environment. In addition, it can reduce the economic cost and improve the weeding efficiency. Therefore, in combination with the current development trend of automat...

AI Technical Summary

Problems solved by technology

The computer vision technology method is to collect RGB images of weeds and crops in the field by image acquisition equipment, and analyze the morphological characteristics of weeds and crops to realize the distinction between the two. This method has requirements for image acquisition environment and image preprocessing Higher, the feature extraction process is more complicated, generally only applicable to the crops and weeds of the research object, and the universality is poor

The spectral technology method is to collect the respective spectral images of crops and weeds, and use the characteristics of different spectral reflection characteristics of different plants under the same lighting conditions to identify weeds. The requirements are relatively strict, and the price and learning cost of image acquisition instruments are high, and the analysis method is difficult to promote, so it is not conducive to putting into actual production

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