J. G. Arnal Barbedo
Plant Science · 2013–2019
A researcher in the Science corpus: 6 papers; trusts 44 researchers and is trusted by 67 researchers here, as inferred from the citation graph.
Subfields
- Plant Science — 45 citations into it; #61 of 8,232 by trust here, #1,760 by citations
- Analytical Chemistry — 23 citations into it; #89 of 1,254 by trust here, #205 by citations
- Computer Vision and Pattern Recognition — 17 citations into it
- Cell Biology — 16 citations into it
- Artificial Intelligence — 13 citations into it
- Representation & Self-Supervised Learning — 10 citations into it
- Genetics — 7 citations into it
Whom J. G. Arnal Barbedo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jeremy S. Smith
- Yoshua Bengio
- Chih-Wei Hsu
- Robert Martin Haralick
- Chih-Jen Lin
- David P. Hughes
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yann LeCun
- James J. Jiang
Who trusts J. G. Arnal Barbedo overall
- José G. M. Esgario
- Andre da Silva Abade
- Chowdhury Rafeed Rahman
- Diego Inácio Patrício
- Lili Li
- Justine Boulent
- Jun Liu
- Muhammad Hammad Saleem
- Vijaypal Singh Dhaka
- Halil Durmus
Papers from J. G. Arnal Barbedo's perspective
Ordered by how strongly the researchers J. G. Arnal Barbedo trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Going deeper with convolutions
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- A review on the main challenges in automatic plant disease identification based on visible range images (2016) — 609 citations
- Plant disease identification from individual lesions and spots using deep learning (2019) — 627 citations
- Detection of sprout damage in wheat kernels using NIR hyperspectral imaging (2018) — 60 citations
- Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification (2018) — 650 citations
- Factors influencing the use of deep learning for plant disease recognition (2018) — 543 citations
- Digital image processing techniques for detecting, quantifying and classifying plant diseases (2013) — 603 citations