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A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network
Abstract
Abstract Manual approaches to recognize cucumber diseases are often time-consuming, laborious and subjective. A deep convolutional neural network (DCNN) was proposed to conduct symptom-wise recognition of four cucumber diseases, i.e., anthracnose, downy mildew, powdery mildew, and target leaf spots. The symptom images were segmented from cucumber leaf images captured under field conditions. In order to decrease the chance of overfitting, data augmentation methods were utilized to enlarge the datasets formed by the segmented symptom images. With the augmented datasets containing 14,208 symptom images, the DCNN achieved good recognition results, with an accuracy of 93.4%. In order to compare the results of the DCNN, comparative experiments were conducted using conventional classifiers (Random Forest and Support Vector Machines), as well as AlexNet. Results showed that the DCNN was a robust tool for recognizing the cucumber diseases in field conditions.
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Gradient-based learning applied to document recognition
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Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
Powers, D. M. W. (2020) · arXiv · doi:10.48550/arxiv.2010.16061 · Cited by 5,440
Cited by
Using deep transfer learning for image-based plant disease identification
Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105393 · Cited by 1,052
Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion
Zhou, G., Zhang, W., Chen, A., He, M., & Ma, X. (2019) · IEEE Access · doi:10.1109/access.2019.2943454 · Cited by 253
Metadata
- Authors
- Ma, J., Du, K., Zheng, F., Zhang, L., Gong, Z., & Sun, Z.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 154
- Pages
- 18-24
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2891667148
- Cited by
- 553
- References
- 5
- FWCI
- 34.19
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ma, J., Du, K., Zheng, F., Zhang, L., Gong, Z., & Sun, Z. (2018). A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2018.08.048
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