PiTLiD: Identification of Plant Disease From Leaf Images Based on Convolutional Neural Network

With the development of plant phenomics, the identification of plant diseases from leaf images has become an effective and economic approach in plant disease science. Among the methods of plant diseases identification, the convolutional neural network (CNN) is the most popular one for its superior performance. However, CNN's representation power is still a challenge in dealing with small datasets, which greatly affects its popularization. In this work, we propose a new method, namely PiTLiD, based on pretrained Inception-V3 convolutional neural network and transfer learning to identify plant leaf diseases from phenotype data of plant leaf with small sample size. To evaluate the robustness of the proposed method, the experiments on several datasets with small-scale samples were implemented. The results show that PiTLiD performs better than compared methods. This study provides a plant disease identification tool based on a deep learning algorithm for plant phenomics. All the source data and code are accessible at https://github.com/zhanglab-wbgcas/PiTLiD.

Automated summary

With the development of plant phenomics, plant disease identification through leaf images using convolutional neural networks (CNN) has become an effective and economic approach. However, CNN's representation power remains a challenge in handling small datasets. In this study, a new method called PiTLiD is proposed, which utilizes pretrained Inception-V3 CNN and transfer learning to identify plant leaf diseases with small sample sizes. Experimental results show that PiTLiD outperforms other methods. This research provides a plant disease identification tool based on deep learning algorithm for plant phenomics.