@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 {222, title = {(Not so) intuitive results from a smart agriculture low-power wireless mesh deployment}, booktitle = {Proceedings of the Eleventh ACM Workshop on Challenged Networks}, year = {2016}, publisher = {ACM}, organization = {ACM}, address = {New York city}, abstract = {
A 21-node low-power wireless mesh network is deployed in a peach orchard. The network serves as a frost event prediction system. On topof sensor values, devices also report network statistics. In 3 months of operations, the network has produced over 4 million temperature values, and over 350,000 network statistics. This paper presents an in-depth analysis of the statistics, in order to precisely understand the performance of the network. Nodes in the network exhibit an expected lifetime between4 and 16 years, with an end-to-end reliability of 100\%. We show how {\textendash}contrary to popular belief {\textendash} wireless links are symmetric. Thanks to the use of Time Slotted Channel Hopping (TSCH), the network topology is very stable, with less than 5 link changes per day in the entire network.
}, doi = {https://doi.org/10.1145/2979683.2979696}, url = {https://hal.inria.fr/hal-01361333/document}, author = {Brun-Laguna, Keoma and Diedrichs, Ana Laura and Dujovne, Diego and L{\'e}one, R{\'e}my and Vilajosana, Xavier and Watteyne, Thomas} } @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} } @conference {251, title = {S3LF: a Socio-Technical System for Self-Determinant Governance in Collaborative Organizations}, booktitle = {23RD INTERNATIONAL PUBLIC MANAGEMENT NETWORK (IPMN) CONFERENCE}, year = {2019}, abstract = {

This work presents S3LF, a socio-technical system in the form of a mobile application for
facilitating a ​ digital self-determinant governance experience in collaborative organizations. It
consists of a technologically mediated adaptation of the decision making framework Sociocracy 3.0
a.k.a S3, turning it into an asynchronous and remote process with a facilitation mediated through the
user experience (UX) and interaction design. S3LF enables digital and distributed organizations
such as Collaborative Networked Organizations (CNOs) to adopt self-determinant governancemethodologies such as S3, and promises a faster onboarding into self-determinant governance and
higher throughput of self-determinant decision making. Furthermore, we introduce in S3LF a
self-diagnosis module that reports the level of self-determination perceived by each decision maker
of the group for each decision made, over different contextual situations; whose analysis has the
potential to allow organizations to self-correct dominant situations.

}, keywords = {collaborative organizations, consent-based decision making, digital governance, socio-technical system, Sociocracy 3.0}, author = {Ribas, Alexandre and Bromberg, Facundo and Dinamarca, Agustina} } @proceedings {178, title = {Characterization of LQI behavior in WSN for glacier area in Patagonia Argentina}, journal = {Embedded Systems (SASE/CASE), 2013 Fourth Argentine Symposium and Conference on}, year = {2013}, month = {08/2013}, pages = {1--6}, publisher = {IEEE}, address = {Buenos Aires, Argentina}, abstract = {

One of the most important aspects before installing a Wireless Sensor Network (WSN) is a previous study of connectivity constraints that exist in the area to be covered. This study is critical to the final distribution of the sensors, with an important impact in the life of the network by reducing consumption, and on the robustness by contemplating redundancy of paths and sensors. In this paper, we present a summary of the most important aspects of a preliminary empirical study of the Link Quality Indicator (LQI), on different landscapes in the glaciers area of Patagonia Argentina. The landscapes covered varied in geographical structures with different levels of attenuation and extreme environmental conditions. Through the analysis of the Cumulative Distribution Function (CDF) of the measured LQI values, we can characterize the behavior of four different scenarios and correlate the combined effects of the environmental structure with the distance from the transmitter. The measurements performed were designed for characterizing the links at the physical layer with the purpose of defining models to estimate the Packet Error Rate (PER) for the WSN deployment stage.

}, issn = {978-1-4799-1098-4}, doi = {10.1109/SASE-CASE.2013.6636777}, url = {http://dx.doi.org/10.1109/SASE-CASE.2013.6636777}, author = {Diedrichs, Ana Laura and Robles, Mar{\'\i}a In{\'e}s and Bromberg, Facundo and Mercado, Gustavo and Dujovne, Diego} } @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 {233, title = {Grapevine buds detection and localization in 3D space based on Structure from Motion and 2D image classification}, journal = {Computers in Industry}, volume = {99C }, year = {2018}, month = {04/2018}, pages = {303-312}, chapter = {303}, abstract = {

In viticulture, there are several applications where 3D bud detection and localization in vineyards is a necessary task susceptible to automation: measurement of sunlight exposure, autonomous pruning, bud counting, type-of-bud classification, bud geometric characterization, internode length, and bud development stage.\ This paper presents a workflow to achieve quality 3D localizations of grapevine buds based on well-known computer vision and machine learning algorithms when provided with images captured in natural field conditions (i.e., natural sunlight and the addition of no artificial elements), during the winter season and using a mobile phone RGB camera. Our pipeline combines the Oriented FAST and Rotated BRIEF (ORB) for keypoint detection, a Fast Local Descriptor for Dense Matching (DAISY) for describing the keypoint, and the Fast Approximate Nearest Neighbor (FLANN) technique for matching keypoints, with the Structure from Motion multi-view scheme for generating consistent 3D point clouds. Next, it uses a 2D scanning window classifier based on Bag of Features and Support Vectors Machine for classification of 3D points in the cloud. Finally, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for 3D bud localization is applied. Our approach resulted in a maximum precision\ of 1.0\ (i.e., no false detections), a maximum recall\ of 0.45\ (i.e. 45\%\ of the buds detected), and a localization error within the range of 259-554\ pixels (corresponding to approximately 3\ bud diameters, or 1.5cm) when evaluated over the whole range of user-given parameters of workflow components.

}, keywords = {computer vision, Grapevine bud detection, Precision viticulture}, issn = {0166-3615}, doi = {10.1016/j.compind.2018.03.033}, url = {https://www.sciencedirect.com/science/article/pii/S0166361517304815}, author = {Diaz, Carlos Ariel and P{\'e}rez, Diego Sebasti{\'a}n and Miatello, Humberto and Bromberg, Facundo} } @article {210, title = {Image classification for detection of winter grapevine buds in natural conditions using scale-invariant features transform, bag of features and support vector machines}, journal = {Computers and Electronics in Agriculture}, volume = {135}, year = {2017}, month = {04/2017}, pages = {81-95}, chapter = {81}, abstract = {

In viticulture, there are several applications where bud detection in vineyard images is a necessary task, susceptible of being automated through the use of computer vision methods. A common and effective family of visual detection algorithms are the scanning-window type, that slide a (usually) fixed size window along the original image, classifying each resulting windowed-patch as containing or not containing the target object. The simplicity of these algorithms finds its most challenging aspect in the classification stage. Interested in grapevine buds detection in natural field conditions, this paper presents a classification method for images of grapevine buds ranging 100 to 1600 pixels in diameter, captured in outdoor, under natural field conditions, in winter (i.e., no grape bunches, very few leaves, and dormant buds), without artificial background, and with minimum equipment requirements. The proposed method uses well-known computer vision technologies: Scale-Invariant Feature Transform for calculating low-level features, Bag of Features for building an image descriptor, and Support Vector Machines for training a classifier. When evaluated over images containing buds of at least 100 pixels in diameter, the approach achieves a recall higher than 0.9 and a precision of 0.86 over all windowed-patches covering the whole bud and down to 60\% of it, and scaled up to window patches containing a proportion of 20\%-80\% of bud versus background pixels. This robustness on the position and size of the window demonstrates its viability for use as the classification stage in a scanning-window detection algorithms.

}, keywords = {computer vision, Grapevine bud, Image classification, Precision viticulture, Scanning-window detection}, issn = {0168-1699}, doi = {10.1016/j.compag.2017.01.020}, url = {http://www.sciencedirect.com/science/article/pii/S0168169916301818}, author = {P{\'e}rez, Diego Sebasti{\'a}n and Bromberg, Facundo and Diaz, Carlos Ariel} } @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} } @article {242, title = {Prediction of frost events using machine learning and IoT sensing devices}, journal = {IEEE Internet of Things Journal}, volume = {5}, year = {2018}, month = {27 August 2018}, pages = { 4589-4597}, type = {Full}, chapter = {4589}, abstract = {

Internet of Things (IoT) in agriculture applications have evolved to solve several relevant problems from producers. Here, we describe a component of an IoT-enabled frost prediction system. We follow current approaches for prediction that use machine learning algorithms trained by past readings of temperature and humidity sensors to predict future temperatures. However, contrary to current approaches, we assume that the surrounding thermodynamical conditions are informative for prediction. For that, a model was developed for each location, including in its training information of sensor readings of all other locations, autonomously selecting the most relevant ones (algorithm dependent). We evaluated our approach by training regression and classification models using several machine learning algorithms, many already proposed in the literature for the frost prediction problem, over data from five meteorological stations spread along the Mendoza Province of Argentina. Given the scarcity of frost events, data was augmented using the synthetic minority oversampling technique (SMOTE). The experimental results show that selecting the most relevant neighbors and training the models with SMOTE reduces the prediction errors of both regression predictors for all five locations, increases the performance of Random Forest classification predictors for four locations while keeping it unchanged for the remaining one, and produces inconclusive results for logistic regression predictor. These results demonstrate the main claim of these works: that thermodynamic information of neighboring locations can be informative for improving both regression and classification predictions, but also are good enough to suggest that the present approach is a valid and useful resource for decision makers and producers.

}, author = {Diedrichs, Ana Laura and Bromberg, Facundo and Dujovne, Diego and Brun-Laguna, Keoma and Watteyne, Thomas} } @article {256, title = {Towards practical 2D grapevine bud detection with fully convolutional networks}, journal = {Computers and Electronics in Agriculture}, volume = {182}, year = {2021}, month = {03/2021}, pages = {105947}, abstract = {

In Viticulture, visual inspection of the plant is a necessary task for measuring relevant variables. In many cases, these visual inspections are susceptible to automation through computer vision methods. Bud detection is one such visual task, central for the measurement of important variables such as: measurement of bud sunlight exposure, autonomous pruning, bud counting, type-of-bud classification, bud geometric characterization, internode length, bud area, and bud development stage, among others. This paper presents a computer method for grapevine bud detection based on a Fully Convolutional Networks MobileNet architecture (FCN-MN). To validate its performance, this architecture was compared in the detection task with a strong method for bud detection, Scanning Windows (SW) based on a patch classifier, showing improvements over three aspects of detection: segmentation, correspondence identification and localization. The best version of FCN-MN showed a detection F1-measure of 88.6\% (for true positives defined as detected components whose intersection-over-union with the true bud is above 0.5), and false positives that are small and near the true bud. Splits {\textendash}false positives overlapping the true bud{\textendash} showed a mean segmentation precision of 89.3\%(21.7), while false alarms {\textendash}false positives not overlapping the true bud{\textendash} showed a mean pixel area of only 8\% the area of a true bud, and a distance (between mass centers) of 1.1 true bud diameters. The paper concludes by discussing how these results for FCN-MN would produce sufficiently accurate measurements of bud variables such as bud number, bud area, and internode length, suggesting a good performance in a practical setup.

}, keywords = {computer vision, Fully convolutional network, Grapevine bud detection, Precision viticulture}, issn = {0168-1699}, doi = {https://doi.org/10.1016/j.compag.2020.105947}, url = {https://www.sciencedirect.com/science/article/pii/S0168169920331525}, author = {Wenceslao Villegas Marset and Diego Sebastian Perez and Carlos Ariel D{\'\i}az and Facundo Bromberg} } @article {231, title = {Prediction of frost location using machine learning and wireless sensor networks}, year = {2014}, author = {Diedrichs, Ana} } @article {227, title = {SDCP: Streaming Distributed Control Protocol}, year = {2016}, url = {https://datatracker.ietf.org/doc/draft-perez-dispatch-sdcp/}, author = {Cristian Perez and Ana Laura Diedrichs} } @unpublished {255, title = {Deep Learning for 2D Grapevine Bud Detection}, year = {2020}, url = {https://arxiv.org/abs/2008.11872}, author = {Villegas Marset, Wenceslao and P{\'e}rez, Diego S and D{\'\i}az, Carlos A and Bromberg, Facundo} } @unpublished {229, title = {Prediction of frost events using Bayesian networks and Random Forest}, journal = {IEEE Internet of Things Journal}, year = {2017}, author = {Ana Laura Diedrichs and Facundo Bromberg and Diego Dujovne and Keoma Brun-Laguna and Thomas Watteyne} } @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} }