基于迁移学习和残差网络的农作物病害分类

作物病害对农业产品的质量和产量有重要影响,单一物种病害的分类模型难以应对复杂的农业生产环境。该研究对深度残差网络SE-ResNeXt-101模型进行改进,并基于迁移学习(TransferLearning,TL)提出了一种农作物病害分类模型TL-SE-ResNeXt-101,用于不指定农作物种类的病害检测分类。在重构的AI Challenger 2018农作物病害数据集上,将该模型与VGG-16、GoogLeNet、ResNet-50和DenseNet-121卷积神经网络模型进行比较。结果表明,相同试验条件下,本文模型对不同作物种类的不同病害分类平均准确率达到98%,分类效果优于其他模型;在真实农业生产环境下该模型的分类效果也优于其他模型,平均准确率达到47.37%。该模型具有较高的识别准确率与较强的鲁棒性,可为复杂农业生产环境下对不同作物种类不同病害的识别分类提供参考。

Crop disease classification with transfer learning and residual networks

Abstract: Crop diseases have posed a major threat to food production and the agricultural ecosystem worldwide. Automatic detection and classification of crop diseases are highly urgent to facilitate targeted and timely disease control in modern agriculture. The complex crop disease is ever-deepening the difficulty of feature extraction in traditional machine learning, where the human selection of features requires lots of experiments and experience. Alternatively, the convolutional neural network (CNN) can automatically extract relevant features from the input images, thereby imitating the human visual process. The learned features are also the key to the success of deep learning. However, the CNN identification model is difficult to deal with the complex planting structure in the actual production, if only a single species is identified. In this study, a feasible classification of crop disease was proposed using the improved deep residual network (SE-ResNeXt-101) and transfer learning (TL-SE-ResNeXt-101) for the detection task without specifying crop species. The dataset of crop disease was used from the AI Challenger 2018 competition, containing 27 types of diseases on 10 plant species: apple, cherry, corn, grape, citrus, peach, pepper, potato, strawberry, and tomato. In the reconstructed dataset, the "species-disease" approach was utilized to extract 33 categories of crop diseases with a total of 35 332 leaves images of different sizes. The training set, validation set, and test set were divided at the ratio of 8:1:1. In the test, the dataset was first preprocessed to uniformly transform the images of different sizes into 224×224×3, and then the image pixels were de-averaged and normalized to reduce the computational effort, in order to prevent the gradient explosion in the training of deep learning models for the convergence. Data enhancement techniques were used to increase the diversity of samples, including color enhancement, random angle, random crop, and horizontal random flip. The color enhancement was utilized to adjust the brightness, contrast, saturation, and chromaticity. The random angle was to rotate the image randomly between -15°and 15°. The random crop was to obtain a portion of the image arbitrarily between the scales of 0.1 to 1, and then convert it to 224×224 image size. The horizontal random flip was to randomly flip the image into a mirror image. The randomization probability of all data enhancement was 50%. The proposed model was compared with the VGG-16, GoogLeNet, ResNet-50, and DenseNet-121 models under the same experimental conditions. The experimental results show that the transfer learning significantly improved the convergence speed and classification performance of the model, thereby training a better model in a shorter period. Data enhancement techniques reduced the dependence of the model on certain attributes without overfitting for better performance and generalization. The average accuracy and weighted F1 value reached 98% in the TL-SE-ResNeXt-101 model, better than other models. The TL-SE-ResNeXt-101 model also had an excellent classification effect on the image samples in real agricultural production. The average accuracy of the TL-SE-ResNext-101 model reached 47.37% on the PlantDoc test set, indicating strong robustness to serve as a promising technology for crop disease identification.