Literature DB >> 34372355

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

Pisana Placidi1, Renato Morbidelli2, Diego Fortunati1, Nicola Papini1, Francesco Gobbi1, Andrea Scorzoni1.   

Abstract

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.

Entities:  

Keywords:  IoT measurements; LoRa; LoRaWAN™; Precision Agriculture; distributed sensing; moisture sensor; sensor networks; soil water content; wireless communication

Year:  2021        PMID: 34372355     DOI: 10.3390/s21155110

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  2 in total

Review 1.  Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria.

Authors:  Alessandro Massaro; Giuseppe Starace
Journal:  Sensors (Basel)       Date:  2022-06-29       Impact factor: 3.847

2.  A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming.

Authors:  Navod Neranjan Thilakarathne; Muhammad Saifullah Abu Bakar; Pg Emerolylariffion Abas; Hayati Yassin
Journal:  Sensors (Basel)       Date:  2022-08-22       Impact factor: 3.847

  2 in total

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