The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A comparative study of fine-tuning deep learning models for plant disease identification
Abstract
Abstract Deep learning has recently attracted a lot of attention with the aim to develop a quick, automatic and accurate system for image identification and classification. In this work, the focus was on fine-tuning and evaluation of state-of-the-art deep convolutional neural network for image-based plant disease classification. An empirical comparison of the deep learning architecture is done. The architectures evaluated include VGG 16, Inception V4, ResNet with 50, 101 and 152 layers and DenseNets with 121 layers. The data used for the experiment is 38 different classes including diseased and healthy images of leafs of 14 plants from plantVillage. Fast and accurate models for plant disease identification are desired so that accurate measures can be applied early. Thus, alleviating the problem of food security. In our experiment, DenseNets has tendency’s to consistently improve in accuracy with growing number of epochs, with no signs of overfitting and performance deterioration. Moreover, DenseNets requires a considerably less number of parameters and reasonable computing time to achieve state-of-the-art performances. It achieves a testing accuracy score of 99.75% to beat the rest of the architectures. Keras with Theano backend was used to perform the training of the architectures.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Identity Mappings in Deep Residual Networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · arXiv · doi:10.48550/arxiv.1603.05027 · Cited by 10,665
Research works printed but not linked: 21
- Athanikar, G., Badar, M.P., 2016. Potato Leaf Diseases Detection and Classification System, 5(2), 76–88.
- Chetlur, S. et al., 2014. cuDNN: Efficient Primitives for Deep Learning, pp. 1–9.
- Deng, L., Yu, D., 2014. Deep Learning: Methods and Applications. Foundations and Trends® in Signal Processing. pp. 3–4. · DOI · Paper page
- Dyrmann, 2016, Plant species classification using deep convolutional neural network, Biosyst. Eng., 151, 72, 10.1016/j.biosystemseng.2016.08.024 · DOI · Paper page
- Glorot, X., Bordes, A., Bengio, Y., 2011. Deep sparse rectifier neural networks. AISTATS ’11 Proc. 14th Int. Conf. Artif. Intell. Stat., vol. 15, pp. 315–323, 2011.
- He, K., Zhang, X., Ren, S., Sun, J., 2015. Deep Residual Learning for Image Recognition, Arxiv.Org, vol. 7, no. 3, pp. 171–180.
- Hughes, D., Salathe, Marcel, 2015. An open access repository of images on plant health to enable the development of mobile disease diagnostics, pp. 1–13.
- Le, Q.V., Coates, A., Prochnow, B., Ng, A.Y., 2011. On Optimization Methods for Deep Learning,. In: Proc. 28th Int. Conf. Mach. Learn., pp. 265–272.
- Melorose, J., Perroy, R., Careas, S., 2015. World population prospects, United Nations 1(6042), 587–592.
- Mishkin, D., Matas, J., 2016. All you need is a good init, pp. 1–13.
- Mohanty, 2016, Using deep learning for image-based plant disease detection, Front. Plant Sci., 7, 1
- Pan, S.J., Fellow, Q.Y., 2009. A Survey on Transfer Learning, pp. 1–15.
- Ren, S., He, K., Girshick, R., Sun, J., 2015. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Nips, pp. 1–10.
- Reyes, 2015, Fine-tuning deep convolutional networks for plant recognition, CEUR Workshop Proc., 1391
- Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., Mccool, C., 2016. DeepFruits : A Fruit Detection System Using Deep Neural Networks. · DOI · Paper page
- Simonyan, 2015, Very deep convolutional networks for large-scale image recognition, Int. Conf. Learn. Represent., 1
- Sladojevic, 2016, Deep neural networks based recognition of plant diseases by leaf image classification, Comput. Intell. Neurosci., 2016
- Szegedy, C., Com, S.G., 2015. Batch Normalization : Accelerating Deep Network Training by Reducing Internal Covariate Shift, vol. 37.
- Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A., 2016. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. · DOI · Paper page
- Wang, H., Li, G., Ma, Z., Li, X., 2012. Application of neural networks to image recognition of plant diseases. 2012 Int. Conf. Syst. Informatics, ICSAI 2012, no. Icsai, pp. 2159–2164. · doi:10.1109/icsai.2012.6223479
- Yu, D., et al., Deep convolutional neural networks with layer-wise context expansion and attention. In: Proc. Annu. Conf. Int. Speech Commun. Assoc. INTERSPEECH, vol. 08–12–Sept, 2016, pp. 17–21. · DOI
Cited by
COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images
Hemdan, E. E., Shouman, M. A., & Karar, M. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11055 · Cited by 913
New perspectives on plant disease characterization based on deep learning
Lee, S. H., Goëau, H., Bonnet, P., & Joly, A. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105220 · Cited by 348
Review of Weed Detection Methods Based on Computer Vision
Wu, Z., Chen, Y., Zhao, B., Kang, X., & Ding, Y. (2021) · Sensors · doi:10.3390/s21113647 · Cited by 303
Pham, T. N., Tran, L. V., & Dao, S. V. T. (2020) · IEEE Access · doi:10.1109/access.2020.3031914 · Cited by 262
“[8] conducted a review of the performances of different deep learning models namely Visual Geometry Group (VGG), Inception V4, ResNet, and DenseNet in disease classification using the ‘‘Plant Village’’ dataset, in which DenseNet was the most efficient with 99.”
MethodSaleem, M. H., Potgieter, J., & Arif, K. M. (2020) · Plants · doi:10.3390/plants9101319 · Cited by 259
Metadata
- Authors
- Too, E. C., Yujian, L., Njuki, S., & Yingchun, L.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2019-06
- Volume
- 161
- Pages
- 272-279
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2795016359
- Cited by
- 1,487
- References
- 6
- FWCI
- 59.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A comparative study of fine-tuning deep learning models for plant disease identification. https://app.cochat.ai/papers/10.1016/j.compag.2018.03.032
- The paper
- Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2018.03.032
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
