In a couple of hours, farmers can lose everything because of frost events. Handling frost events is possible by using a number of countermeasures such as heating or removing the surrounding air among crops. Given the socio-economical implications of this problem, involving not only loss of jobs but also valuable resources, there have been a number of efforts to design a system to predict frost events, but with partial success: either they are based on formulas needing many unknown coefficients, or the prediction performance is poor. In this paper we propose a new approach, which instead of using the sensor's past information for prediction, as state of the art methods do, we assume that frost prediction in one location could be improved by using the information of the most relevant neighboring sensors. However, given the small amount of frost events during the year, available data, even after incorporating information of neighboring sensors, it is not enough to build an accurate frost forecasting system, which defines an unbalanced dataset problem. In order to overcome this disadvantage, we propose to use machine learning algorithms such as Bayesian networks and Random Forest where the training set includes new samples using SMOTE (Synthetic Minority Oversampling Technique). Our results show that selecting the most relevant neighbors and training the models with SMOTE increases significatively the frost detection rate of the predictor, turning the results into a useful resource for decision makers.

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

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.

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