A method of tea cicada recognition and counting based on convolutional neural networks

A technology of convolutional neural network and green leafhopper, which is applied in the field of image processing, can solve the problems of low accuracy, long labor time, and high degree of visual acuity for judging experience, so as to achieve accurate recognition and calculation, enrich semantic information, and avoid image information. lost effect

A technology of convolutional neural network and green leafhopper, which is applied in the field of image processing, can solve the problems of low accuracy, long labor time, and high degree of visual acuity for judging experience, so as to achieve accurate recognition and calculation, enrich semantic information, and avoid image information. lost effect

CN112101455BActive Publication Date: 2022-08-09CHONGQING ACAD OF AGRI SCI

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  • A method of tea cicada recognition and counting based on convolutional neural networks
  • A method of tea cicada recognition and counting based on convolutional neural networks
  • A method of tea cicada recognition and counting based on convolutional neural networks

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Embodiment

[0036] like Figure 1 ~ 2 It is shown, a method of tea cicada recognition and counting based on convolutional neural networks, which is characterized by:

[0037] Including the following steps:

[0038] A. First of all, the standard VGG16 network structure is used as the main network of the main network, and the image samples containing all outlines with yellow boards are featured. At the same time, the shallow features of image samples are selected for regression operations;

[0039] Specifically: The image sample containing all outlines contained in the yellow board is an image frame obtained by using a high -definition camera shooting or any way to shoot in a mobile phone. extract;

[0040] In order to weaken the pixels of camera imaging pixels and the distance of the shooting distance, it is used to estimate the size of the size, so as to select the best feature layer in the backbone network as the input layer of the shallow network. The specific steps are:

[0041] First of al...

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Abstract

The invention provides a method for identifying and counting the green leafhoppers based on a convolutional neural network. The method uses a VGG16 network structure as a backbone network to perform feature extraction of green leafhoppers containing yellow plates, and selects shallow features. The regression operation is performed, and the deep features of the rich semantic information of the backbone network are superimposed layer by layer on the shallow features through the combination of upsampling and convolution layers, thereby enriching the semantic information of the shallow features and avoiding the The image information is lost, and the automatic identification, position regression and automatic calculation of the tea leafhopper are completed; the identification of the tea leafhopper by this method can reach 98% in the case of 1% missed detection; at the same time, for high-definition pictures, If the processing speed of 1080P data reaches the ms level, it can quickly and accurately identify and calculate the small green leafhoppers, and can be widely used in the prevention and control of tea small green leafhoppers such as tea trees and fruit trees.

Description

Technical field [0001] The present invention involves the field of image processing technology, which specifically involves a method of identifying and counting tea -based tea cicada -based tea cicadas based on convolutional neural networks. Background technique [0002] Tea small green leaf cicada, adult body length of 3 to 4mm, body width of 1mm, light yellow green to green, compound eye gray -brown to dark brown, no single eye, tentacle rigid hair shape, black at the end; resemblance. Tea small green leaf cicada, nymphs are the main juice of piercing tea tree leaves, and inhabited on the back of the crop leaf, and the adults are ends after eating, and the eggs are mostly produced under the skin. ~ 17th generation, the generation overlap. Therefore, the tea small green leaf cicada is one of the most widely distributed and harmful parts of the tea districts in my country, and one of the key prevention and treatment of pests that affect the yield and quality of tea. About %, and ...

Claims

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Application Information

Patent Timeline
09 Aug 2022
Publication
CN112101455B
IPC
G06V10/75; G06K9/62; G06V10/40; G06M11/00; G06N3/04; G06N3/08
CPC
G06N3/08; G06M11/00; G06V10/40; G06V10/751; G06N3/045
Inventors
陈世春; 王晓庆