Agricultural pest identification method based on convolutional neural network, terminal and readable storage medium

A technology of convolutional neural network and agricultural pests, which is applied in the field of agricultural pest identification methods based on convolutional neural networks, terminals and readable storage media, which can solve the problems of crop yield reduction, network accuracy decline, and inability to accurately identify pest images, etc. , to achieve the effect of improving the monitoring ability, improving the detection accuracy, and improving the degree of identification

Pending Publication Date: 2020-11-13
威海精讯畅通电子科技有限公司
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Problems solved by technology

Infestation by pests will accompany the entire growth period of crops. If the pests and diseases cannot be dealt with in time, it will cause a large reduction in crop production
Nowadays, many methods have been proposed for pest image recognition technology, among which there are many image recognition algorithms based on convol...

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  • Agricultural pest identification method based on convolutional neural network, terminal and readable storage medium
  • Agricultural pest identification method based on convolutional neural network, terminal and readable storage medium
  • Agricultural pest identification method based on convolutional neural network, terminal and readable storage medium

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Embodiment Construction

[0058]Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the relationship between hardware and software Interchangeability. In the above description, the composition and steps of each example have been generally described according to their functions. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present invention.

[0059] The block diagrams shown in the drawings are merely functional entities and do not necessarily correspond to physically separate entitie...

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Abstract

The invention provides an agricultural pest identification method based on a convolutional neural network, a terminal and a readable storage medium. The method comprises the steps of collecting insectimage information; setting a label of the insect image information, and performing feature extraction to obtain feature mapping information; judging state information of the feature region information; mapping the candidate region to a ROIs region with a fixed size obtained on the feature map, and judging a target category; inputting the test set into a training model, and detecting insect information in the candidate region pictures; and when the precision of the detected candidate area picture reaches a predetermined value, completing the establishment of the training model, and detecting insect information in the image through the training model. According to the invention, the identification degree of insect image information is improved, and the detection precision of insect identification is improved. By effectively utilizing the agricultural pest identification method based on the convolutional neural network, pest images can be identified in agricultural pest image identification, so that the monitoring capability of agricultural automation is improved.

Description

technical field [0001] The present invention relates to the technical field of pest image recognition, in particular to a method for recognizing agricultural pests based on a convolutional neural network, a terminal and a readable storage medium. Background technique [0002] With the progress and development of scientific and technological innovation, technological agriculture has gradually emerged, and many agricultural technologies have been derived, including pest image recognition technology. Infestation by pests will accompany the entire growth period of crops. If the pests and diseases cannot be dealt with in time, it will cause a large reduction in crop yield. Nowadays, many methods have been proposed for pest image recognition technology, among which there are many image recognition algorithms based on convolutional neural network. The performance of the deeper model is better, and the detection results are more accurate. However, in practice, as the depth of the n...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V20/10G06N3/045G06F18/214
Inventor 王相赵晓龙于进福
Owner 威海精讯畅通电子科技有限公司
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