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

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...

AI Technical Summary

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 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 network deepens to a certain extent, the accuracy of the network decreases, resulting in the inability to accurately identify the image information of pests. affect crop growth

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