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 p...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2023-03, Vol.20 (2), p.1278-1288
Main Authors: Liu, Kangchen, Zhang, Xiujun
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Convolutional codes
convolutional neural network
Convolutional neural networks
Datasets
Deep learning
Diseases
Economics
leaf image
Leaves
Machine learning
Medical imaging
Neural networks
Neural Networks, Computer
Phenomics
Phenotypes
plant disease
Plant Diseases
Plant Leaves - genetics
Plant phenomics
Plants
Plants (biology)
Task analysis
Training
Transfer learning
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Summary: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 .
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2022.3195291