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Aitor Alvarez-Gila
Researcher at Autonomous University of Barcelona
Publications - 15
Citations - 1723
Aitor Alvarez-Gila is an academic researcher from Autonomous University of Barcelona. The author has contributed to research in topics: Plant disease & Convolutional neural network. The author has an hindex of 10, co-authored 15 publications receiving 908 citations. Previous affiliations of Aitor Alvarez-Gila include University of Cassino.
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Automatic Red-Channel underwater image restoration
TL;DR: A Red Channel method is proposed, where colors associated to short wavelengths are recovered, as expected for underwater images, leading to a recovery of the lost contrast, and achieves a natural color correction and superior or equivalent visibility improvement when compared to other state-of-the-art methods.
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Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild
Artzai Picon,Aitor Alvarez-Gila,Maximiliam Seitz,Amaia Ortiz-Barredo,Jone Echazarra,Alexander Johannes +5 more
TL;DR: This work analyses the performance of early identification of three relevant European endemic wheat diseases using an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed.
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Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case
Alexander Johannes,Artzai Picon,Aitor Alvarez-Gila,Jone Echazarra,Sergio Rodriguez-Vaamonde,Ana Dez Navajas,Amaia Ortiz-Barredo +6 more
TL;DR: A novel image processing algorithm based on candidate hot-spot detection in combination with statistical inference methods is proposed to tackle disease identification in wild conditions of three European endemic wheat diseases septoria, rust and tan spot.
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Few-Shot Learning approach for plant disease classification using images taken in the field
David Argüeso,Artzai Picon,Unai Irusta,Alfonso Medela,Miguel G San-Emeterio,Arantza Bereciartua,Aitor Alvarez-Gila +6 more
TL;DR: It is possible to learn new plant leaf and disease types with very small datasets using deep learning Siamese networks with Triplet loss, achieving almost a 90% reduction in training data needs and outperforming classical learning techniques for small training sets.
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Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions
Artzai Picon,Maximiliam Seitz,Aitor Alvarez-Gila,Patrick Mohnke,Amaia Ortiz-Barredo,Jone Echazarra +5 more
TL;DR: Three different CNN architectures that incorporate contextual non-image meta-data such as crop information onto an image based Convolutional Neural Network are proposed that combines the advantages of simultaneously learning from the entire multi-crop dataset while reducing the complexity of the disease classification tasks.