DCNN Transfer Learning and Multi-model Integration for Disease and Weed Identification

Abstract

For the complex image segmentation problem and high complexity of model caused by digital processing technology, we first use data enhancement technology to expand dataset size, and then use deep convolutional neural networks (CNNs) multi-model integration method combined transfer learning to identify crop disease and weed. On the one hand, we make full use of the prior knowledge learned from big dataset of four single deep CNNs (VGG, Inception-v3, ResNet and DenseNet). By parameter fine-tuning, the CNNs are reused in the agricultural field to alleviate the over-fitting problem caused by insufficient data sources. On the other hand, two or more CNNs are combined by the direct average method to complete multi-model integration. We directly average the category confidence generated by different models to obtain the final prediction result. The experimental results show that the combination of deep CNNs and transfer learning is effective and the CNNs multi-model integration method can further improve the identification accuracy compared to the single CNN model. The validation accuracy of crop disease and weed dataset can reach 97.14% and 99.22% respectively by using multi-model integration and transfer learning.