Monitoring Soil and Ambient Parameters in the IoT Precision Agriculture Scenario: An Original Modeling Approach Dedicated to Low-Cost Soil Water Content Sensors

被引:63
作者
Placidi, Pisana [1 ]
Morbidelli, Renato [2 ]
Fortunati, Diego [1 ]
Papini, Nicola [1 ]
Gobbi, Francesco [1 ]
Scorzoni, Andrea [1 ]
机构
[1] Univ Perugia, Dipartimento Ingn, I-06125 Perugia, Italy
[2] Univ Perugia, Dipartimento Ingn Civile & Ambientale, I-06125 Perugia, Italy
关键词
soil water content; sensor networks; distributed sensing; IoT measurements; Precision Agriculture; moisture sensor; wireless communication; LoRa; LoRaWAN (TM); FOOD-INDUSTRY; MOISTURE; NETWORK; INFILTRATION; OPTIMIZATION; PREDICTION; DESIGN;
D O I
10.3390/s21155110
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
A low power wireless sensor network based on LoRaWAN protocol was designed with a focus on the IoT low-cost Precision Agriculture applications, such as greenhouse sensing and actuation. All subsystems used in this research are designed by using commercial components and free or open-source software libraries. The whole system was implemented to demonstrate the feasibility of a modular system built with cheap off-the-shelf components, including sensors. The experimental outputs were collected and stored in a database managed by a virtual machine running in a cloud service. The collected data can be visualized in real time by the user with a graphical interface. The reliability of the whole system was proven during a continued experiment with two natural soils, Loamy Sand and Silty Loam. Regarding soil parameters, the system performance has been compared with that of a reference sensor from Sentek. Measurements highlighted a good agreement for the temperature within the supposed accuracy of the adopted sensors and a non-constant sensitivity for the low-cost volumetric water contents (VWC) sensor. Finally, for the low-cost VWC sensor we implemented a novel procedure to optimize the parameters of the non-linear fitting equation correlating its analog voltage output with the reference VWC.
引用
收藏
页数:28
相关论文
共 70 条
[1]   Application of Artificial Neural Network modeling for optimization and prediction of essential oil yield in turmeric (Curcutna longa L.) [J].
Akbar, Abdul ;
Kuanar, Ananya ;
Patnaik, Jeetendranath ;
Mishra, Antaryami ;
Nayak, Sanghamitra .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2018, 148 :160-178
[2]  
[Anonymous], 2014, MATH THEORIES DISTRI
[3]  
[Anonymous], What is the LoRaWAN Specification?
[4]   A review of wireless sensors and networks' applications in agriculture [J].
Aqeel-ur-Rehman ;
Abbasi, Abu Zafar ;
Islam, Noman ;
Shaikh, Zubair Ahmed .
COMPUTER STANDARDS & INTERFACES, 2014, 36 (02) :263-270
[5]   An analysis of energy efficiency in Wireless Sensor Networks (WSNs) applied in smart agriculture [J].
Bandur, Doko ;
Jaksic, Branimir ;
Bandur, Milos ;
Jovi, Srdan .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2019, 156 :500-507
[6]  
BeechamRes, SMART FARM AGR EMBR
[7]   Machine Learning in Agriculture: A Comprehensive Updated Review [J].
Benos, Lefteris ;
Tagarakis, Aristotelis C. ;
Dolias, Georgios ;
Berruto, Remigio ;
Kateris, Dimitrios ;
Bochtis, Dionysis .
SENSORS, 2021, 21 (11)
[8]   Effective Calibration of Low-Cost Soil Water Content Sensors [J].
Bogena, Heye Reemt ;
Huisman, Johan Alexander ;
Schilling, Bernd ;
Weuthen, Ansgar ;
Vereecken, Harry .
SENSORS, 2017, 17 (01)
[9]   Internet of things for smart farming and frost intelligent control in greenhouses [J].
Castaneda-Miranda, Alejandro ;
Castano-Meneses, Victor M. .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2020, 176
[10]   Smart frost measurement for anti-disaster intelligent control in greenhouses via embedding IoT and hybrid AI methods [J].
Castaneda-Miranda, Alejandro ;
Castano-Meneses, Victor M. .
MEASUREMENT, 2020, 164