@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 {231, title = {Prediction of frost location using machine learning and wireless sensor networks}, year = {2014}, author = {Diedrichs, Ana} } @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} } @article {71, title = {Point Cloud Library (PCL)}, year = {2011}, abstract = {

The Point Cloud Library (PCL) is a standalone, large scale, open project for 2D/3D image and point cloud processing.

PCL is released under the terms of the BSD license, and thus free for commercial and research use. We are financially supported by a consortium of commercial companies, with our own non-profit organization, Open Perception.

}, keywords = {2D image, 3D image, PCL, point cloud, Point Cloud Library}, url = {http://pointclouds.org/} }