Crop disease image recognition based on transfer learning

Abstract

Machine learning has been widely applied to the crop disease image recognition. Traditional machine learning needs to satisfy two basic assumptions: (1) The training and test data should be under the same distribution; (2) A large scale of labeled training samples is required to learn a reliable classification model. However, in many cases, these two assumptions cannot be satisfied. In the field of agriculture, there are not enough labeled crop disease images. In order to solve this problem, the paper proposed a method which introduced transfer learning to the crop disease image recognition. Firstly, the double Otsu method was applied to obtain the spot images of five kinds of cucumber and rice diseases. Then, color feature, texture feature and shape feature of spot images were extracted. Next, the TrAdaBoost-based method and other baseline methods were used to identify diseases. And experimental results indicate that the TrAdaBoost-based method can implement samples transfer between the auxiliary and target domain and achieve the better results than the other baseline methods. Meanwhile, the results show that transfer learning is helpful in the crop disease image recognition while the training sample is not enough.