Publicaciones Científico Tecnológicas

@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}
}

@article{johannes2017automatic,
title = {Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case},
author = {Alexander Johannes and Artzai Picon and Aitor Alvarez-Gila and Jone Echazarra and Sergio Rodríguez-Vaamonde and Ana Díez Navajas and Amaia Ortiz-Barredo},
url = {https://www.sciencedirect.com/science/article/pii/S016816991631050X
https://computervision.tecnalia.com/wp-content/uploads/2018/10/10.1016j.compag.2017.04.013_preprint.pdf
},
year = {2017},
date = {2017-01-01},
journal = {Computers and Electronics in Agriculture},
volume = {138},
pages = {200--209},
publisher = {Elsevier},
keywords = {plant disease},
pubstate = {published},
tppubtype = {article}
}

@article{gil2015implementation,
title = {Project LIFE-FITOVID-Implementation of demonstrative & innovative strategies to reduce the use of plant protection products in viticulture},
author = {E.Gil and M.Gallart and A.Picón and J.Echazarra and R.Barrio and C.Sampedro and Á.Barranco and S.Rainieri and A.Ortiz and A.M.Díez-Navajas},
url = {https://computervision.tecnalia.com/wp-content/uploads/2015/11/fitovid_2015.pdf},
year = {2015},
date = {2015-01-01},
keywords = {plant disease},
pubstate = {published},
tppubtype = {article}
}