- Chinese Core Journal
- Chinese Science Citation Database (CSCD) Source journal
- Journal of Citation Report of Chinese S&T Journals (Core Edition)
| Citation: |
DAI Zehan, ZHENG Zheng, HUANG Lishu, et al. Recognition of Huanglongbing symptom based on deep convolutional neural network[J]. Journal of South China Agricultural University, 2020, 41(4): 111-119. DOI: 10.7671/j.issn.1001-411X.201909031
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To explore the capability of deploying deep learning to the detection of Huanglongbing (HLB) symptom inCitrus spp., and evaluate the classification accuracies of the classifiers.
Two-class classifiers(I-2-C and M-2-C) and eight-class classifiers(I-8-C and M-8-C) were constructed using images of diseased leaves caused by HLB/non-HLB and healthy leaves based on convolutional neural networks and transfer learning.
The overall classification performance of M-8-C stood out in all classifiers with accuracy of 93.7%, implying great capability in deep convolutional neural networks for classifying HLB symptoms. The mean F1 socres of I-8-C and M-8-C were 77.9% and 88.4% respectively, which were higher than those of I-2-C(56.3%) and M-2-C(52.5%). This indicated that subtyping symptoms could help improve the recognition ability of models. The slightly higher mean F1 score of M-8-C compared with I-8-C indicated that the eight-class model based on MobileNetV1 had better performance than the one based on InceptionV3. An optimized model, namely M-8f-C, was developed based on M-8-C and was successfully mounted on mobile phone. The field tests showed that M-8f-C was of decent performance under field conditions.
Classifier based on deep learning and transfer learning has high accuracy for recognizing HLB symptom leaves.
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