Reference health
Depthwise separable convolution architectures for plant disease classification
11/11 checkable references clean · checked 2026-07-24
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
23 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 11 checked references that resolve
resolves10.1073/pnas.1716999115
An explainable deep machine vision framework for plant stress phenotyping
resolves10.1016/j.neucom.2017.01.018
Plant identification using deep neural networks via optimization of transfer learning parameters
resolves10.1016/j.compag.2011.03.004
Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards
The 23 references without a DOI — listed, not checked
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no DOI — not checkedLearning to prune deep neural networks via layer-wise optimal brain surgeon
no DOI — not checkedGoeau, H., Bonnet, P., Joly, A., 2017. Plant identification based on noisy web data: the amazing performance of deep learning (lifeclef 2017). In: CLEF 2017-Conference and Labs of the Evaluation Forum, pp. 1–13.
no DOI — not checkedHan, S., Mao, H., Dally, W.J., 2016. Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding. In: 4th International Conference on Learning Representations, ICLR 2016. http://arxiv.org/abs/1510.00149.
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no DOI — not checkedHoward, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
no DOI — not checkedHughes, D.P., Salathé, M., 2015. An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing. arXiv preprint arXiv:1511.08060, 1–13.
no DOI — not checkedKrizhevsky, A., Hinton, G., 2009. Learning multiple layers of features from tiny images. Technical Report. University of Toronto.
no DOI — not checkedImagenet classification with deep convolutional neural networks
no DOI — not checkedDeep-plant: Plant identification with convolutional neural networks
no DOI — not checkedPlant leaf identification based on the multi-feature fusion and deep belief networks method
no DOI — not checkedIdentifying two of tomatoes leaf viruses using support vector machine
no DOI — not checkedNarang, S., Elsen, E., Diamos, G., Sengupta, S., 2017. Exploring sparsity in recurrent neural networks. arXiv preprint arXiv:1704.05119.
no DOI — not checkedReyes, A.K., Caicedo, J.C., Camargo, J.E., 2015. Fine-tuning deep convolutional networks for plant recognition, in: CLEF (Working Notes).
no DOI — not checkedA quantization-friendly separable convolution for mobilenets
no DOI — not checkedSimonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
no DOI — not checkedDeep learning for plant identification in natural environment
no DOI — not checkedGoing deeper with convolutions
no DOI — not checkedClassification of plant leaf images with complicated background
no DOI — not checkedFeature extraction and automatic recognition of plant leaf using artificial neural network
no DOI — not checkedZhu, M., Gupta, S., 2017. To prune, or not to prune: exploring the efficacy of pruning for model compression. arXiv preprint arXiv:1710.01878.
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