Rice lesion detection method and system based on deep learning

Abstract

The invention discloses a rice lesion detection method and system based on deep learning, belonging to the image processing field, the method comprising: providing a photo sample set and a manual labeling sample set, and cutting the photo sample set and the manual labeling sample set according to a proportion to form a second photo sample set and a second manual labeling sample set; inputting thesecond photo sample set and the second label sample set into the Linknet network model, and obtaining the optimal model by training the Linknet network model based on the Pytorch deep learning framework; using the optimal model to identify the rice lesion images needed to be detected at present, and calculating the proportion of rice lesion area and classifying the disease status. Through the Linknet network model of Pytorch deep learning framework, the generalization ability and field practicability of rice leaf lesion identification can be improved, and the utilization rate of information can be improved, which is conducive to the subsequent quantitative application of pesticides and reduce environmental pollution.

Description

technical field [0001] The present invention relates to the field of image processing, and more specifically, to a method and system for detecting rice disease spots based on deep learning. Background technique [0002] At present, there are two main technical methods for leaf lesion identification: one is to establish an artificial neural network model to identify photos or images of a single leaf sample; the other is to use support vector machines to establish color, texture, shape and other features. feature vectors for identification. However, the number of artificial neural network layers is small, and the feature extraction ability is insufficient; the support vector machine relies on several specific features for classification, but the specific features cannot completely or better represent the feature information of the lesion, so the classification accuracy is limited. Moreover, both methods use the limited leaf samples collected indoors to do the identification w...

AI Technical Summary

Problems solved by technology

[0003] In view of this, in order to solve the current technical problems of low rice leaf disease spot recognition efficiency, poor generalization ability and poor field practicability, the present invention provides a method and system for detecting rice disease spot based on deep learning

Images