Deep learning for plant identification using vein morphological patterns
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2016/09/01
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Indian UGC (journal)
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Refrences29
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Citations301
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Guillermo L. Grinblat
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Lucas C. Uzal
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Mónica G. Larese
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Pablo M. Granitto
Cite
Grinblat, Guillermo L., et al. “Deep Learning for Plant Identification Using Vein Morphological Patterns”. Computers and Electronics in Agriculture, vol. 127, 2016, pp. 418-24, https://doi.org/10.1016/j.compag.2016.07.003.
Grinblat, G. L., Uzal, L. C., Larese, M. G., & Granitto, P. M. (2016). Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture, 127, 418-424. https://doi.org/10.1016/j.compag.2016.07.003
Grinblat, Guillermo L., Lucas C. Uzal, Mónica G. Larese, and Pablo M. Granitto. “Deep Learning for Plant Identification Using Vein Morphological Patterns”. Computers and Electronics in Agriculture 127 (2016): 418-24. https://doi.org/10.1016/j.compag.2016.07.003.
Grinblat GL, Uzal LC, Larese MG, Granitto PM. Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture. 2016;127:418-24.
Journal Categories
Agriculture
Agriculture (General)
Agriculture
Plant culture
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Deep learning | Nature |
| 36,146 | 2015 |
| Automatic classification of legumes using leaf vein image features | Pattern Recognition |
| 88 | 2014 |
| Multiscale recognition of legume varieties based on leaf venation images | Expert Systems with Applications |
| 25 | 2014 |
Leaf extraction and analysis framework graphical user interface: segmenting and analyzing the structure of leaf veins and areoles | Plant Physiology |
| 2011 | |
| Leaf shape based plant species recognition | Applied Mathematics and Computation |
| 103 | 2007 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Species identification through deep learning and geometrical morphology in oaks (Quercus spp.): Pros and cons | Ecology and Evolution |
| 2024 | |
| Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast | International Journal of Biometeorology |
| 2024 | |
| Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H2O2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.) | Plant Molecular Biology |
| 2024 | |
| OliVaR: Improving olive variety recognition using deep neural networks | Computers and Electronics in Agriculture |
| 2024 | |
| Composite descriptor based on contour and appearance for plant species identification | Engineering Applications of Artificial Intelligence |
| 2024 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 108
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Double-stream Convolutional Neural Networks for Machine Vision Inspection of Natural Products and was published in 2017. The most recent citation comes from a 2024 study titled Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast. This article reached its peak citation in 2022, with 65 citations. It has been cited in 147 different journals, 23% of which are open access. Among related journals, the Computers and Electronics in Agriculture cited this research the most, with 38 citations. The chart below illustrates the annual citation trends for this article.