Deep learning models for plant disease detection and diagnosis
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2018/02/01
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Indian UGC (journal)
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Refrences22
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Citations886
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Konstantinos P. Ferentinos
Cite
Ferentinos, Konstantinos P. “Deep Learning Models for Plant Disease Detection and Diagnosis”. Computers and Electronics in Agriculture, vol. 145, 2018, pp. 311-8, https://doi.org/10.1016/j.compag.2018.01.009.
Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318. https://doi.org/10.1016/j.compag.2018.01.009
Ferentinos, Konstantinos P. “Deep Learning Models for Plant Disease Detection and Diagnosis”. Computers and Electronics in Agriculture 145 (2018): 311-18. https://doi.org/10.1016/j.compag.2018.01.009.
1.
Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture. 2018;145:311-8.
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 neural networks for acoustic modeling in speech recognition: The shared views of four research groups | 2012 | |||
| Gradient-based learning applied to document recognition | Proceedings of the IEEE |
| 18,984 | 1998 |
| Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case | Computers and Electronics in Agriculture |
| 187 | 2017 |
| A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition | Sensors |
| 572 | 2017 |
| Deep learning for plant identification using vein morphological patterns | Computers and Electronics in Agriculture |
| 301 | 2016 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa | Pest Management Science |
| 2024 | |
Detection and coverage estimation of purple nutsedge in turf with image classification neural networks | Pest Management Science |
| 1 | 2024 |
| Performance evaluation and optimization of convolutional neural network architectures for Tomato plant disease eleven classes based on augmented leaf images dataset | Neural Computing and Applications |
| 2024 | |
| Diagnosis of fungi affected apple crop disease using improved ResNeXt deep learning model | Multimedia Tools and Applications |
| 2024 | |
| Intelligent detection for sustainable agriculture: A review of IoT-based embedded systems, cloud platforms, DL, and ML for plant disease detection | Multimedia Tools and Applications |
| 2024 |
Citations Analysis
The category
Agriculture: Plant culture 294
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Comparison of SIFT Encoded and Deep Learning Features for the Classification and Detection of Esca Disease in Bordeaux Vineyards and was published in 2018. The most recent citation comes from a 2024 study titled Automatic Maize Leaf Disease Recognition Using Deep Learning. This article reached its peak citation in 2022, with 235 citations. It has been cited in 292 different journals, 24% of which are open access. Among related journals, the Computers and Electronics in Agriculture cited this research the most, with 82 citations. The chart below illustrates the annual citation trends for this article.