Liu Kangchen, Zhang Xiujun
IEEE/ACM Trans Comput Biol Bioinform. 2023 Mar-Apr;20(2):1278-1288. doi: 10.1109/TCBB.2022.3195291. Epub 2023 Apr 3.
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.
随着植物表型组学的发展，从叶片图像中识别植物病害已成为植物病害科学中一种有效且经济的方法。在植物病害识别方法中，卷积神经网络（CNN）因其卓越的性能而最为流行。然而，CNN在处理小数据集时的表征能力仍是一个挑战，这极大地影响了其推广。在这项工作中，我们基于预训练的Inception-V3卷积神经网络和迁移学习提出了一种新方法，即PiTLiD，用于从小样本量的植物叶片表型数据中识别植物叶片病害。为了评估所提方法的稳健性，我们在几个小规模样本数据集上进行了实验。结果表明，PiTLiD的性能优于对比方法。本研究为植物表型组学提供了一种基于深度学习算法的植物病害识别工具。所有源数据和代码可在https://github.com/zhanglab-wbgcas/PiTLiD获取。
