Prediction of frost location using machine learning and wireless sensor networks

Abstract. The damage caused by the frost takes place when the temperatures are below than a tolerable limit for the plants. Each phenological state, e.g flowering, has a variable cold hardiness, so the lethal temperature is also variable. Freezing climatic events are the most dangerous, because they affect a large land surface. Mendoza is not an exception. According to the Instituto Nacional de Vitivinicultura (INV), in 2013 the loss of the vine crop reached up to 27% Previous works on frost prediction have worked with data taken from meteorological stations very distant between them [3][2][4] or using wireless sensor networks (WSN) We are exploring the variables relationships using the independence approach by learning Markov Network structures from the environmental data corroborating with the opinion of an expert. The analysis of the Markov blanket of particular sensors helps to identify which neighbor sensors could improve the prediction. Our research is focused about the use of Markov networks as a supervised machine learning technique, for the feature selection purpose, and we are considering to use Markov Networks for inference. On the other hand, we are also collaborating with a team of agronomic engineers, in order to study the traditional prediction techniques that they have used before. In this task, we are writing a survey about this topic, in order to highlight the open issues of the field

PEACH: predicting frost events in peach orchards using IoT technology

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 off-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.EEA JunínFil: Watteyne, Thomas. Institut National de Recherche en Informatique et en Automatique (INRIA). EVA Team; FranciaFil: Diedrichs, Ana Laura. Universidad Tecnológica Nacional (UTN), Mendoza; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Brun-Laguna, Keoma. Institut National de Recherche en Informatique et en Automatique (INRIA). EVA Team; FranciaFil: Chaar, Javier Emilio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Junín; ArgentinaFil: Dujovne, Diego. Universidad Diego Portales (UDP), Santiago; ChileFil: Taffernaberry, Juan Carlos. Universidad Tecnológica Nacional (UTN), Mendoza; ArgentinaFil: Mercado, Gustavo. Universidad Tecnológica Nacional (UTN), Mendoza; Argentin

(Not so) Intuitive Results from a Smart Agriculture Low-Power Wireless Mesh Deployment

International audienceA 21-node low-power wireless mesh network is deployed in a peach orchard. The network serves as a frost event prediction system. On top of 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 between 4 and 16 years, with an end-to-end reliability of 100%. We show how – contrary to popular belief – wireless links are symmetric. Thanks to the use of Time Slotted Channel Hopping (TSCH), the network topology is very stable, with ≤5 link changes per day in the entire network

Caracterización de jugos de uva comerciales en base al perfil mineral y análisis quimiométrico no supervisado

Actualmente el consumo de alimentos y bebidas naturales, libres de conservantes, ha aumentado significativamente. El jugo de uva es una bebida derivada del género Vitis sp., principalmente de V. vinífera, la cual presenta sustancias bioactivas de origen fenólico con propiedades oxidantes, cardioprotectoras y antiinflamatorias1. La identificación del origen geográfico de alimentos y bebidas es actualmente un aspecto relevante a la hora de identificar la calidad y autenticidad de un producto. En este contexto, el objetivo de este trabajo se propuso caracterizar jugos de uva comerciales de tres países de Sudamérica (Argentina, Brasil y Chile), principales productores y exportadores de este producto, utilizando técnicas de reconocimiento de pautas no supervisadas, en base a su perfil mineral. Se determinaron por espectrometría de masas con plasma acoplado inductivamente 11 elementos: As, Cr, Cu, Fe, Mn, Mo, Ni, Pd, Rb, V, Y en 31 muestras de jugos comerciales de uva. Las muestras se agruparon de acuerdo al país en el que fueron adquiridas, 16 de Argentina (ARG), 5 de Chile (CHL) y 10 de Brasil (BRZ). En primer término, previo al análisis multielemental se mineralizaron las muestras mediante digestión ácida en vaso abierto con HNO3 (65%) de elevada pureza. La calidad del método analítico se evaluó mediante el método de adición de estándar. Una vez obtenidos los resultados se distribuyeron siguiendo un arreglo matricial en el que se ubicaron en las filas a las muestras y las concentraciones elementales en las columnas.Para el análisis quimiométrico de los resultados se utilizaron distintas técnicas de reconocimiento de pautas no supervisadas: análisis de componentes principales (ACP), análisis de conglomerados k-medias (kM). Previo al análisis quimiométrico, los datos fueron estandarizados para evitar problemas de dimensionalidad. El ACP reveló diferencias en la composición mineral de las muestras provenientes de ARG, caracterizadas por elevados contenidos de Fe, Mn y Cu. Las muestras de BRZ y CHL presentaron gran superposición entre los autovalores correspondientes, indicando una gran similitud composicional entre las mismas. Por su parte, el método kM generaró resultados congruentes con los observados en el ACP. El método de agrupación kM es un método iterativo en los que la noción de similitud se deriva de la proximidad de un punto de datos al centroide de los agrupamientos. Esta metodología requiere que se suministre de antemano el número de grupos esperados en la matriz de datos (valor k). Se ensayaron distintos valores para el valor k, lográndose resultados óptimos al utilizar k = 3 con un 94% de exactitud global, se utilizó validación cruzada (k-fold = 15) para evitar falsos resultados por sobreajuste.Fil: Canizo, Brenda Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Laboratorio de Química Analítica para Investigación y Desarrollo; ArgentinaFil: Diedrichs, Ana Laura. Universidad Tecnológica Nacional; ArgentinaFil: Londonio, Agustin. Comisión Nacional de Energía Atómica; ArgentinaFil: Smichowski, Patricia. Comisión Nacional de Energía Atómica; ArgentinaFil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Wuilloud, Rodolfo German. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Laboratorio de Química Analítica para Investigación y Desarrollo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina10º Congreso Argentino de Química AnalíticaLa PampaArgentinaUniversidad Nacional de La PampaAsociación Argentina de Químicos Analítico

A Demo of the PEACH IoT-based Frost Event Prediction System for Precision Agriculture

International audienceIn 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 demo provides an overview of the 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 off-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, built around SmartMesh IP, is 100% reliable, with end-to-end latency below 2 s, and over 3 years of battery lifetime

Prediction of Frost Events using Bayesian networks and Random Forest

International audienceIoT 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 work: 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

Using SmartMesh IP in Smart Agriculture and Smart Building applications

We deploy two low-power wireless networks, one in a Smart Agriculture setting (a peach orchard), one in a Smart Building. Both networks use out-of-the-box SmartMesh IP technology to gather sensor values, as well as extensive network statistics. This article presents an in-depth analysis of the performance of both networks, and compares them. Nodes in both exhibit end-to-end reliability of 100%, with an expected lifetime between 4 and 8 years. We show how – contrary to popular belief – wireless links are symmetrical. Thanks to the use of Time Slotted Channel Hopping (TSCH), the network topology is stable, with at most 15 link changes on average per day in the network. We conclude that TSCH as implemented by SmartMesh IP is a perfectly suitable IoT solution for Smart Agriculture and Smart Building applications.Fil: Brun Laguna, Keoma. Institut National de Recherche en Informatique et en Automatique; FranciaFil: Diedrichs, Ana Laura. Universidad Tecnológica Nacional. Facultad Regional de Mendoza; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Dujovne, Diego. Universidad Diego Portales; ChileFil: Taffernaberry, Juan Carlos. Universidad Tecnológica Nacional; ArgentinaFil: Léone, Rémy. Institut National de Recherche en Informatique et en Automatique; FranciaFil: Vilajosana, Xavier. Institut National de Recherche en Informatique et en Automatique; FranciaFil: Watteyne, Thomas. Institut National de Recherche en Informatique et en Automatique; Franci