Multiparametric Monitoring in Equatorian Tomato Greenhouses (I): Wireless Sensor Network Benchmarking

214

112

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.

Section: Discussionmentioning

confidence: 99%

A Systematic Review of IoT Solutions for Smart Farming

Navarro

Costa

Pereira

2020

214

112

“…In addition, Figure 14 presents the inverse correlation of air temperature and air relative humidity as well as the direct relation of UV radiation and air temperature. The continuous monitoring of environmental variables allows the analysis of climatic changes and hence to determine the optimal limits to prevent unwanted effects on growing tomatoes [ 12 ]. The analysis of the direct and inverse relationship of environmental variables allows the farmer to understand how greenhouse conditions influence crop growth, and to react to changes that are outside the permitted ranges to maximize productivity.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In the first of the companion papers [ 12 ], a system is proposed to characterize the performance of the ZigBee (star and mesh topology), and WiFi standards for monitoring greenhouses. We summarize here the fundamental elements to understand the present work.…”

Section: Summary Of the Proposed System For Tomato Greenhouse Monimentioning

confidence: 99%

“…One of the key technological aspects in this setting is the current possibility of monitoring climatic variables with sensor networks, which is receiving increasing attention. In the first companion paper [ 12 ], we described the analysis of the WSN topologies and their configuration, in terms of the design and implementation of hardware and software for the nodes with different communication technologies. For this purposes, three WSNs were designed and implemented: ZigBee technology with star topology, ZigBee with mesh topology (referred to here as DigiMesh); and WiFi technology (access point topology).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiparametric Monitoring in Equatorian Tomato Greenhouses (III): Environmental Measurement Dynamics

Erazo-Rodas

Sandoval-Moreno

Muñoz-Romero

et al. 2018

Self Cite

World population growth currently brings unequal access to food, whereas crop yields are not increasing at a similar rate, so that future food demand could be unmet. Many recent research works address the use of optimization techniques and technological resources on precision agriculture, especially in large demand crops, including climatic variables monitoring using wireless sensor networks (WSNs). However, few studies have focused on analyzing the dynamics of the environmental measurement properties in greenhouses. In the two companion papers, we describe the design and implementation of three WSNs with different technologies and topologies further scrutinizing their comparative performance, and a detailed analysis of their energy consumption dynamics is also presented, both considering tomato greenhouses in the Andean region of Ecuador. The three WSNs use ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The present study provides a systematic and detailed analysis of the environmental measurement dynamics from multiparametric monitoring in Ecuadorian tomato greenhouses. A set of monitored variables (including CO2, air temperature, and wind direction, among others) are first analyzed in terms of their intrinsic variability and their short-term (circadian) rhythmometric behavior. Then, their cross-information is scrutinized in terms of scatter representations and mutual information analysis. Based on Bland–Altman diagrams, good quality rhythmometric models were obtained at high-rate sampling signals during four days when using moderate regularization and preprocessing filtering with 100-coefficient order. Accordingly, and especially for the adjustment of fast transition variables, it is appropriate to use high sampling rates and then to filter the signal to discriminate against false peaks and noise. In addition, for variables with similar behavior, a longer period of data acquisition is required for the adequate processing, which makes more precise the long-term modeling of the environmental signals.

“…We also used bootstrap resampling techniques to provide with non-parametric statistical description of confidence intervals (CIs) when convenient, as well as the so-called M-mode plots to represent and compare the statistical properties of the several realizations. The two companion papers are devoted to the analysis of the WSN topologies and their configuration and to the study of the dynamics of the environmental measurements, both of them in Equadorian tomato greenhouse monitoring [ 14 , 15 ]. The first one has the detailed description of the complete system and network setup, and the second one has a short summary of the proposed system for tomato greenhouse monitoring.…”

Section: Introductionmentioning

confidence: 99%

Multiparametric Monitoring in Equatorian Tomato Greenhouses (II): Energy Consumption Dynamics

Erazo-Rodas

Sandoval-Moreno

Muñoz-Romero

et al. 2018

Self Cite

Tomato greenhouses are a crucial element in the Equadorian economy. Wireless sensor networks (WSNs) have received much attention in recent years in specialized applications such as precision farming. The energy consumption in WSNs is relevant nowadays for their adequate operation, and attention is being paid to analyzing the affecting factors, energy optimization techniques working on the network hardware or software, and characterizing the consumption in the nodes (especially in the ZigBee standard). However, limited information exists on the analysis of the consumption dynamics in each node, across different network technologies and communication topologies, or on the incidence of data transmission speed. The present study aims to provide a detailed analysis of the dynamics of the energy consumption for tomato greenhouse monitoring in Ecuador, in three types of WSNs, namely, ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The networks were installed and maintained in operation with a line of sight between nodes and a 2-m length, whereas the energy consumption measurements of each node were acquired and stored in the laboratory. Each experiment was repeated ten times, and consumption measurements were taken every ten milliseconds at a rate of fifty thousand samples for each realization. The dynamics were scrutinized by analyzing the recorded time series using stochastic-process analysis methods, including amplitude probability functions and temporal autocorrelation, as well as bootstrap resampling techniques and representations of various embodiments with the so-called M-mode plots. Our results show that the energy consumption of each network strongly depends on the type of sensors installed in the nodes and on the network topology. Specifically, the CO2 sensor has the highest power consumption because its chemical composition requires preheating to start logging measurements. The ZigBee network is more efficient in energy saving independently of the transmission rate, since the communication modules have lower average consumption in data transmission, in contrast to the DigiMesh network, whose consumption is high due to its topology. Results also show that the average energy consumption in WiFi networks is the highest, given that the coordinator node is a Meshlium™ router with larger energy demand. The transmission duration in the ZigBee network is lower than in the other two networks. In conclusion, the ZigBee network with star topology is the most energy-suitable one when designing wireless monitoring systems in greenhouses. The proposed methodology for consumption dynamics analysis in tomato greenhouse WSNs can be applied to other scenarios where the practical choice of an energy-efficient network is necessary due to energy constrains in the sensor and coordinator nodes.