@conference {230, title = {Aplicaciones de Internet de las Cosas SIPIA6-Red de Sensores Inal{\'a}mbricos con IPv6}, booktitle = {XV Workshop de Investigadores en Ciencias de la Computaci{\'o}n}, year = {2013}, author = {Mercado, Gustavo and Borgo, Roberto and Gonzalez Antivilo, Francisco and Taffernaberry, Juan Carlos and Diedrichs, Ana and Aguirre, Mat{\'\i}as and Robles, Mar{\'\i}a In{\'e}s and Grunwaldt, Guillermo and Tabacchi, Germ{\'a}n and Tromer, Sebasti{\'a}n and others} } @conference {223, title = {A demo of the PEACH IoT-based frost event prediction system for precision agriculture}, booktitle = {Sensing, Communication, and Networking (SECON), 2016 13th Annual IEEE International Conference on}, year = {2016}, publisher = {IEEE}, organization = {IEEE}, doi = {10.1109/SAHCN.2016.7732963}, url = {https://hal.inria.fr/hal-01311527/document}, author = {Brun-Laguna, Keoma and Diedrichs, Ana Laura and Chaar, Javier Emilio and Dujovne, Diego and Taffernaberry, Juan Carlos and Mercado, Gustavo and Watteyne, Thomas} } @conference {183, title = {Low-power wireless sensor network for frost monitoring in agriculture research}, booktitle = {Biennial Congress of Argentina (ARGENCON), 2014 IEEE}, year = {2014}, publisher = {IEEE}, organization = {IEEE}, address = {Bariloche}, abstract = {

This work presents the development of a wireless sensor network (WSN), based on IEEE-802.15.4, in order to be used for frost characterization in precision agriculture by measuring temperature. Our key objective is to reduce the power consumption of the network to the minimum, allowing several measurement points per node and the remote monitoring of the sensors behaviour. For the communication interface between a WSN node and the sensors, we have developed a serial protocol inspired in SDI-12. Preliminary results show a low-cost and low-power WSN. The user can access and use the data for agronomic research.

}, isbn = {978-1-4799-4270-1}, doi = {10.1109/ARGENCON.2014.6868546}, url = {http://dx.doi.org/10.1109/ARGENCON.2014.6868546}, author = {Diedrichs, Ana Laura and Tabacchi, Germ{\'a}n and Grunwaldt, Guillermo and Pecchia, Mat{\'\i}as and Mercado, Gustavo and Gonzalez Antivilo, Francisco} } @conference {232, title = {RED SIPIA: Red de Sensores Inal{\'a}mbricos para Investigaci{\'o}n Agron{\'o}mica}, booktitle = {XIII Workshop de Investigadores en Ciencias de la Computaci{\'o}n}, year = {2011}, author = {Mercado, Gustavo and Borgo, Roberto and Gonzalez Antivilo, Francisco and Ortiz Uriburu, Gisela and Diedrichs, Ana and Farreras, Pablo and Aguirre, Mat{\'\i}as and Battaglia, Fernando and Tabacchi, Germ{\'a}n and Tromer, Sebasti{\'a}n} } @proceedings {257, title = {Multi species weed detection with Retinanet one-step network in a maize field}, journal = {Precision agriculture{\textquoteright}21}, year = {2021}, month = {06/2021}, pages = {2223{\textendash}2228}, publisher = {Wageningen Academic Publishers}, address = {Budapest, Hungary}, abstract = {

Weed density and composition are not uniform throughout the field, nevertheless, the conventional approach is to carry out a uniform application. Object Detection Networks have already arrived in agricultural applications that can be used for weed management. The current study developed a detection and classification of weeds system in a one-step procedure using RetinaNet Object Detection Network. The procedure was based on identifying Solanum nigrum L., Cyperus rotundus L. and Echinochloa crus-galli L. and two growth stages both for a broadleaf species (S. nigrum) as well as narrow-leaved species (C. rotundus) in a maize field. The predictions were evaluated by mAP metric. The result obtained was 0.88 with values between 0.98 and 0.75 depending on the class.

}, keywords = {deep learning, object detection networks, RetinaNet, site-specific weed management}, isbn = {978-90-8686-916-9}, issn = {978-90-8686-363-1}, doi = {https://doi.org/10.3920/978-90-8686-916-9}, url = {https://www.wageningenacademic.com/doi/abs/10.3920/978-90-8686-916-9}, author = {Correa, JM L{\'o}pez and Todeschini, M and P{\'e}rez, DS and Karouta, J and Bromberg, F and Ribeiro, A and And{\'u}jar, D} } @proceedings {225, title = {Sonda de temperatura para uso agr{\'\i}cola}, journal = {CASE Congreso Argentino de Sistemas Embebidos}, year = {2015}, edition = {2015}, issn = {978-987-45523-3-4}, author = {Guillermo Gr{\"u}nwaldt and Mat{\'\i}as Pecchia and Ana Laura Diedrichs and Germ{\'a}n Tabacchi and Gustavo Mercado} } @article {224, title = {Peach: Predicting frost events in peach orchards using iot technology}, journal = {EAI Endorsed Transactions on the Internet of Things}, year = {2016}, abstract = {

In 2013, 85\% of the peach production in the Mendoza region (Argentina) was lost because of frost. In a couple of hours, farmers can lose everything. Handling a frost event is possible, but it is hard to predict when it is going to happen. The goal of the PEACH project is to predict frost events by analyzing measurements from sensors deployed around an orchard. This article provides an in-depth description of a complete solution we designed and deployed: the low-power wireless network and the back-end system. The low-power wireless network is composed entirely of commercial o -the-shelf devices. We develop a methodology for deploying the network and present the open-source tools to assist with the deployment and to monitor the network. The deployed low-power wireless mesh network is 100\% reliable, with end-to-end latency below 2 s, and over 3 years of battery lifetime. This article discusses how the technology used is the right one for precision agriculture applications.

}, doi = {http://dx.doi.org/10.4108/eai.1-12-2016.151711}, author = {Watteyne, Thomas and Diedrichs, Ana Laura and Brun-Laguna, Keoma and Chaar, Javier Emilio and Dujovne, Diego and Taffernaberry, Juan Carlos and Mercado, Gustavo} } @unpublished {226, title = {Using SmartMesh IP in Smart Agriculture and Smart Building Deployments: it Just Works}, journal = {Computer Communications journal}, year = {2017}, publisher = {elsevier}, type = {Journal article}, abstract = {

article sent, status: under review

}, author = {Keoma Brun-Laguna and Ana Laura Diedrichs and Thomas Watteyne and Diego Dujovne and Carlos Taffernaberry and Remy Leone and Xavier Vilajosana} }