@article{picon_deep_2018,
title = {Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild},
author = {Artzai Picon and Aitor Alvarez-Gila and Maximiliam Seitz and Amaia Ortiz-Barredo and Jone Echazarra and Alexander Johannes},
url = {http://www.sciencedirect.com/science/article/pii/S0168169917312619
https://computervision.tecnalia.com/wp-content/uploads/2018/10/preprint_computer_and_electronics_10.1016@j.compag.2018.04.002-1.pdf},
doi = {10.1016/j.compag.2018.04.002},
issn = {0168-1699},
year = {2018},
date = {2018-01-01},
urldate = {2018-06-26},
journal = {Computers and Electronics in Agriculture},
abstract = {Fungal infection represents up to 50% of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with 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. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita). The analysis was done using different mobile devices, and more than 8178 images were captured in two pilot sites in Spain and Germany during 2014,2015 and 2016. Obtained results reveal an overall improvement of the balanced accuracy from 0.78 (Johannes et al., 2017) up to 0.87 under exhaustive testing, and balanced accuracies greater than 0.96 on a pilot test performed in Germany.},
keywords = {cnn, convolutional neural networks, deep learning, disease identification, early pest, image processing, phytopathology, plant disease, precision agriculture},
pubstate = {published},
tppubtype = {article}
}
Fungal infection represents up to 50% of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with 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. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita). The analysis was done using different mobile devices, and more than 8178 images were captured in two pilot sites in Spain and Germany during 2014,2015 and 2016. Obtained results reveal an overall improvement of the balanced accuracy from 0.78 (Johannes et al., 2017) up to 0.87 under exhaustive testing, and balanced accuracies greater than 0.96 on a pilot test performed in Germany.
@inproceedings{galdran2017efficient,
title = {An efficient non-uniformity correction technique for side-scan sonar imagery},
author = {Adrian Galdran and Alberto Isasi and Mohammed Al-Rawi and Jonathan Rodriguez and Joaquim Bastos and Fredrik Elmgren and Marc Pinto},
url = {https://ieeexplore.ieee.org/document/8084577/},
doi = {doi: 10.1109/OCEANSE.2017.8084577},
year = {2017},
date = {2017-01-01},
booktitle = {OCEANS 2017-Aberdeen},
pages = {1--6},
organization = {IEEE},
keywords = {image processing},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{vazquez2016image,
title = {Image processing applications through a variational perceptually-based color correction related to Retinex},
author = {Javier Vazquez-Corral and Syed Waqas Zamir and Adrian Galdran and David Pardo and Marcelo Bertalmío},
url = {http://www.dtic.upf.edu/~jvazquez/VazquezCorralEI2016.pdf},
year = {2016},
date = {2016-01-01},
journal = {Electronic Imaging},
volume = {2016},
number = {6},
pages = {1--6},
publisher = {Society for Imaging Science and Technology},
keywords = {image processing},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{al2016intensity,
title = {Intensity normalization of sidescan sonar imagery},
author = {MS Al-Rawi and Adrián Galdrán and Xin Yuan and Martina Eckert and José-Fernán Martinez and Fredrik Elmgren and Baran Cürüklü and Jonathan Rodriguez and Joaquim Bastos and Marc Pinto},
url = {https://ieeexplore.ieee.org/document/7820967/},
year = {2016},
date = {2016-01-01},
booktitle = {Image Processing Theory Tools and Applications (IPTA), 2016 6th International Conference on},
pages = {1--6},
organization = {IEEE},
keywords = {image processing},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{bereciartua2015automatic,
title = {Automatic 3D model-based method for liver segmentation in MRI based on active contours and total variation minimization},
author = {Arantza Bereciartua and Artzai Picon and Adrian Galdran and Pedro Iriondo},
url = {https://computervision.tecnalia.com/wp-content/uploads/2017/02/Liquid-steel-level-measurement-at-electric-arc-furnaces-without-increasing-the-power-off-time.pdf},
year = {2015},
date = {2015-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {20},
pages = {71--77},
publisher = {Elsevier},
keywords = {image processing, thermographic camera},
pubstate = {published},
tppubtype = {article}
}
@conference{belar2014imagen,
title = {Uso de la imagen histológica en biobancos como elemento de análisis, integración, estandarización y catalogación},
author = {O.Belar and I.Vicente and A.Egia and L.Marín and M.Carrera and G.Yañez and I.Gil-Aldea and M.Zelaya and V.Huerta and I.Arenaz and B.Vega and E.Lag and A.Brouchet and A.Viguri and B.Catón and R.Ruiz and J.J.Aguirre and R.Rezola and P.Elorriaga and E.DeMiguel and A.Saiz and S.Fernández and A.Gaafar and M.Saiz and R.Bilbao and BIOPOOL consortium},
url = {https://computervision.tecnalia.com/wp-content/uploads/2014/10/BIOCLOUDBIOPOOL1.pdf},
year = {2014},
date = {2014-01-01},
keywords = {image processing},
pubstate = {published},
tppubtype = {conference}
}
@misc{dejong2012requirements,
title = {Requirements for sharing of and searching on histo-morphological aspects in digital pathology images and associates data},
author = {B.deJong and R.Bilbao and P.H.J.Riegman},
year = {2012},
date = {2012-01-01},
keywords = {image processing},
pubstate = {published},
tppubtype = {presentation}
}
@article{isasi2012gazela,
title = {Gazela: asesor digital de redes sociales para adolescentes},
author = {Alberto Isasi-Andrieu and Ángel López-Carrera and Pilar Ruiz-Ibáñez and Iñaki Angulo-Redondo},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/2012_Gazela-Asesor-digital-de-redes-sociales-para-adolescentes.pdf},
year = {2012},
date = {2012-01-01},
journal = {El profesional de la información},
volume = {21},
number = {5},
pages = {514--519},
keywords = {biometrics, cyberbulling, image processing, social network},
pubstate = {published},
tppubtype = {article}
}
@article{garrote2011algorithms,
title = {Algorithms for colour image processing based on neurological models},
author = {Estíbaliz Garrote Contreras},
year = {2011},
date = {2011-01-01},
publisher = {Servicio Editorial de la Universidad del País Vasco/Euskal Herriko Unibertsitatearen Argitalpen Zerbitzua},
keywords = {color, image processing},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{picon2010automation,
title = {Automation of waste recycling using hyperspectral image analysis},
author = {Artzai Picón and Ovidiu Ghita and Pedro M Iriondo and Aranzazu Bereciartua and Paul F Whelan},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/2010_AutomationofWasteRecycling.pdf},
year = {2010},
date = {2010-01-01},
booktitle = {Emerging Technologies and Factory Automation (ETFA), 2010 IEEE Conference on},
pages = {1--4},
organization = {IEEE},
keywords = {hyperspectral, image processing},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{rodriguez2010usando,
title = {Usando Firmas Artificiales en Sistemas de Adquisición y Reconocimiento Web},
author = {Sergio Rodriguez-Vaamonde and Carlos VivarachoPascual and Juan Manuel Pascual},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/2010_UsandoFirmasArtificiales.pdf},
year = {2010},
date = {2010-01-01},
keywords = {image processing},
pubstate = {published},
tppubtype = {article}
}
@article{ruiz2004high,
title = {High resolution adaptive colour printing verification system for quality control of distorted elements.},
author = {Artzai Picón Ruiz and Estibaliz Garrote Contreras and Jose Angel Gutierrez Olabarria and Evaristo Kahoraho Bukubille and Jose Ignacio Larrauri Villamor and A Antton},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/2004_IntelligentSystem.pdf},
year = {2004},
date = {2004-01-01},
journal = {WSEAS Transactions on Circuits and Systems},
volume = {3},
number = {10},
pages = {2205--2210},
keywords = {color, image processing, quality control},
pubstate = {published},
tppubtype = {article}
}
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