Crop disease identification method based on incremental learning

Abstract

The invention provides a crop disease identification method based on incremental learning. When new data arrive, continuous learning is carried out based on an original learning result, and the capability of progressive learning is achieved, which means that new knowledge can be obtained from new samples obtained by batch and the performance is gradually improved under a condition that original knowledge is effectively kept. Firstly, a crop disease sample database is collected, and simulation incremental learning of disease images in the sample database is carried out using a negative correlation integrated neural network as main technical means, so that an initial parameter of a negative correlation learning system is determined, an integrated neural network classifier based on negative correlation learning is initialized based on the initial parameter, and the classifier is trained using a sample in an initial stage; in an incremental learning stage, when an expert adds a new sample in the sample database, the integrated neural network classifier based on negative correlation learning only is updated by only training the newly-added sample data, so that the object of incremental learning is achieved; and finally, a diagnosis result of a disease picture and control measures are fed back to a user, so that the pest and disease can be accurately identified and diagnosed, and the object of comprehensive crop control is achieved.

Description

technical field [0001] The invention relates to the field of pattern recognition and machine learning, in particular to a method for identifying crop diseases based on incremental learning. Background technique [0002] The traditional crop disease identification method usually uses the feature vector extracted from the disease sample database to train a classifier using a neural network. When the user uploads a disease image, the classifier is trained to identify the disease type. In order to obtain a higher classification accuracy, the training sample set is required to be as complete as possible. However, in practical applications, it is difficult to obtain a complete sample set. Due to the limitations of understanding the problem and the complexity of practical applications, it is difficult to accurately and Completely define the training sample set, so that the cost of obtaining and saving all the data at one time will become higher and higher as time increases; many pr...

AI Technical Summary

Problems solved by technology

In order to obtain a higher classification accuracy, the training sample set is required to be as complete as possible, but in practical applications, it is difficult to obtain a complete sample set. Due to the limitations of understanding the problem and the complexity of practical applications, it is difficult to accurately Completely define the training sample set, so that the cost of obtaining and saving all the data at one time will become higher and higher with time; many practical problems do not allow learning after obtaining all the data. When new samples are added, In order to obtain more accurate learning results, it is necessary to combine the data in the previous training set with the data in the new training set for training

This operation is complicated, and the neural network needs to be retrained every time, which also consumes a lot of time and memory capacity.

In particular, if the training data is particularly large, the memory capacity may not be sufficient for training the neural network

Images