Pecan common pest recognition method based on deep learning

Abstract

The invention discloses a hickory nut common pest recognition method based on deep learning, and belongs to the technical field of agricultural and forestry pest recognition and classification, the method comprises the following steps: collecting hickory nut pest sample image data; processing the pecan pest sample image data to obtain a processed pecan pest sample image data set; adopting a VGG convolutional neural network model, and optimizing the VGG convolutional neural network model; carrying out target pest identification and classification by utilizing the optimized VGG convolutional neural network model. Based on the VGGNet network structure, a set of pest identifying and classifying method suitable for hickory is developed, rapid identification and classification of common hickorypests can be achieved, automatic identification and classification can be achieved, and the identification accuracy is high.

Description

technical field

[0001] The invention belongs to the technical field of identification and classification of agricultural and forestry pests, and in particular relates to a method for identifying and classifying hickory pests based on deep learning. Background technique

[0002] Our country is a large agricultural country. During the cultivation of crops, different kinds of pests are encountered every year, which makes the yield and quality of crops decline to varying degrees. When the disaster is severe, it may even lead to large-scale failure of crops. Accurate and effective classification, identification and identification of insects is an important prerequisite for timely pest control and avoiding huge economic losses of crops. Insects are the most diverse animals in the natural environment, and it is very difficult to identify them due to their varied shapes and rich textures. Traditional insect taxonomy and identification mainly rely on insect experts or insect taxonom...

Images