Wireless sensor networks for greenhouse climate and plant condition assessment

Ferentinos, Konstantinos P.; Katsoulas, Ν.; Tzounis, A.; Bartzanas, Thomas; Kittas, C.

doi:10.1016/j.biosystemseng.2016.11.005

Cited by 93 publications

(45 citation statements)

References 24 publications

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“…Kim et al also developed a fuzzy logical model based on energy balance principles [39]. Ferentinos et al [40] analyzed the spatial distribution of temperature, humidity, condensation condition risk and transpiration variability inside a greenhouse; all of these environment parameters has a spatial variation that is due to the measurement error caused by the limitation of the sensors. More importantly, calibration parameters of leaf wetness models are also an important reason for the different results of estimation of LWD for each location because the empirical models rely on local climate data and empirical input conditions.…”

Section: Discussionmentioning

confidence: 99%

Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning

Wang

Sánchez-Molina

et al. 2019

Water

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Leaf wetness duration (LWD) is a key driving variable for peat and disease control in greenhouse management, and depends upon irrigation, rainfall, and dewfall. However, LWD measurement is often replaced by its estimation from other meteorological variables, with associated uncertainty due to the modelling approach used and its calibration. This study uses the decision learning tree method (DLT) for calibrating four LWD models-RH threshold model (RHM), the dew parameterization model (DPM), the classification and regression tree model (CART) and the neural network model (NNM)-whose performances in reproducing measured data are assessed using a large dataset. The relative importance of input variables in contributing to LWD estimation is also computed for the models tested. The LWD models were evaluated at two different greenhouse locations: in a Chinese (CN) greenhouse over three planting seasons (April 2014-October 2015 and in a Spanish (ES) greenhouse over four planting seasons (April 2016-February 2018). Based on multi-evaluation indicators, the models were given a ranking for their assessment capabilities during calibration (in the Spanish greenhouse from . The models were then evaluated on an independent set of data, and the obtained areas under the receiver operating characteristic curve (AUC) of the LWD models were over 0.73. Therein, the best LWD model in this case was the NNM, with positive predict values (PPVs) of 0.82 (SP) and 0.90 (CN), and mean absolute errors (MAEs) of 1.85 h (SP) and 1.30 h (CN). Consequently, the DLT can decrease LWD estimation error by calibrating the model threshold and choosing black box model input variables. simulation were developed, and are routinely used to provide inputs to early disease warning systems of disease outbreaks [6,7]. The LWD models can be divided into two broad categories: empirical and physical models. The latter are data demanding because they require inputs that are not always available, such as cloud cover and net radiation [8,9]. Conversely, estimation of LWD is based on its relationships with meteorological variables available in standard agro-meteorological stations. Some examples of empirical LWD models are the simple relative humidity threshold model (RHM), which simulates the leaf wetness occurrence when humidity is above a threshold [10,11]; the dew point depression method (DPM), based on the principle of dew formation [12,13]; and the classification and regression tree (CART) model, which considers the non-linear relationship between leaf wetness, wind speed, rainfall, dew temperature and relative humidity in decision nodes to determine leaf wetness [14]. Kim et al. demonstrated the spatial portability of the CART model through its application in different environments [15]. Gillespie et al. achieved good results when estimating LWD by a dew point depression model [16]. Further attempts have been made for estimation of LWD using neural network models (NNMs), which can be considered black box models. These are self-adaption models trained with refe...

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Section: Discussionmentioning

confidence: 99%

Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning

Wang

Sánchez-Molina

et al. 2019

Water

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“…e WSN motes were also designed with real application and deployed in the data collection. As the climate is a significant factor for crop yield and diseases, in [27], the authors realized a distributed monitoring system for a greenhouse using a WSN and analyzed the raw data to explore the condition of growing crops. In [28], the authors designed a farmland information collection system containing a portable farmland information collection device, a remote server, and a mobile phone APP.…”

Section: Information Collection In Agriculturementioning

confidence: 99%

A Cloud-Assisted Region Monitoring Strategy of Mobile Robot in Smart Greenhouse

Chu

et al. 2019

Mobile Information Systems

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In smart agricultural systems, the macroinformation sensing by adopting a mobile robot with multiple types of sensors is a key step for sustainable development of agriculture. Also, in a region monitoring strategy that meets the real-scene requirements, optimal operation of mobile robots is necessary. In this paper, a cloud-assisted region monitoring strategy of mobile robots in a smart greenhouse is presented. First, a hybrid framework that contains a cloud, a wireless network, and mobile multisensor robots is deployed to monitor a wide-region greenhouse. Then, a novel strategy that contains two phases is designed to ensure valid region monitoring and meet the time constraints of a mobile sensing robot. In the first phase, candidate region monitoring points are selected using the improved virtual forces. In the second phase, a moving path for the mobile node is calculated based on Euclidean distance. Subsequently, the applicability of the proposed strategy is verified by the greenhouse test system. The verification results show that the proposed algorithm has better performance than the conventional methods. The results also demonstrate that, by applying the proposed algorithm, the number of monitoring points and time consumption can reduce, while the valid monitoring region area is enlarged.

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“…A wireless sensor network (WSN) is a network formed by a large number of sensor nodes where each node is equipped with a sensor to detect physical phenomena such as light, heat, pressure, etc. WSNs are regarded as a revolutionary information gathering method to build the information and communication system which will greatly improve the reliability and efficiency of infrastructure systems [5,8,9,10].…”

Section: Evolution Of Wireless Sensor Networkmentioning

confidence: 99%