Development of an affordable soil moisture sensor system with mini-VNA Tiny and smartphone

This study attempts to develop a soil moisture measurement system with a monopole antenna sensor, mini-VNA Tiny and a mobile phone respectively. The mini-VNA Tiny is a compact vector network analyzer (VNA) with a USB connection to a smartphone or a tablet. There are 17 sets of data which have been collected from 15 different spots with varying soil moisture content. The actual moisture content on site was collected from TRIME-PICO 64/32 sensor. Upon collection, it was necessary to calibrate the resistance obtained from the mini-VNA between 1 MHz and 3 GHz. The data obtained from the study shows that the resonances of the antenna resistance shift to the left on the frequency spectrum as moisture content increases. A linear model relating the resistance and actual moisture content was developed from this study with coefficient of determination (R2) value of 0.723 at 13 MHz. This value is much less than the anticipated R2 = 0.95 for accurate measurement of soil moisture with monopole antenna at microwave frequency. This could be due to the 0.60 cm thickness of the monopole antenna which may not be suited for soil moisture measurement. Nonetheless, this study demonstrates the potential application of an inexpensive and portable mini-VNA Tiny and smartphone system for sensing applications

Ag-IoT for crop and environment monitoring: Past, present, and future

CONTEXT: Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task. OBJECTIVE: Ag-IoT systems enable a data pipeline for modern agriculture that includes data collection, transmission, storage, visualization, analysis, and decision-making. This review serves as a technical guide for Ag-IoT system design and development for crop, soil, and microclimate monitoring. METHODS: It highlighted Ag-IoT platforms presented in 115 academic publications between 2011 and 2021 worldwide. These publications were analyzed based on the types of sensors and actuators used, main control boards, types of farming, crops observed, communication technologies and protocols, power supplies, and energy storage used in Ag-IoT platforms

Calibration of Passive UHF RFID Tags Using Neural Networks to Measure Soil Moisture

This paper presents a system to monitor soil moisture using standard UHF RFID tags buried on the soil. An autonomous mobile robot is also presented, which is capable to navigate on the field and automatically read the sensors, even if they are completely buried on the soil. Thus, passive RFID tags are buried on the soil, allowing wireless moisture measurement without the need of batteries for long periods. The system dispenses external cables and antennas and may be composed of a single RFID tag buried on the soil or by several RFID tags buried at different depths on the soil. An antenna coupled to a RFID reader can be pointed to the place of installation of these tags, and by measuring the received signal strength indicator (RSSI) and other parameters, it allows to estimate the amount of water on the soil. The estimation of volumetric water content (VWC) on the soil was successfully obtained and calibrated with R2>0.9 using neural networks trained with experimental data from a reference capacitive soil moisture sensor. In addition to the simplified installation procedure, the system allows manual or automatic reading through irrigation systems or other systems to control irrigation systems. The system has been evaluated in several experiments, and nine tags were buried on the field, being used for at least three years. Experimental results show that it is possible to read tags at 40 cm deep in the soil with the RFID reader antenna 10 cm far from the soil surface

Proposal of architecture for IoT solution for monitoring and management of plantations

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 presents a systematic review of the existing literature on smart farming with IoT. The systematic review reveals an evolution in the way data are processed by IoT solutions in recent years. Traditional approaches mostly used data in a reactive manner. In contrast, recent approaches allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis. Based on the finds of the systematic review, this work proposes an architecture of an IoT solution that enables monitoring and management of crops in real time. The proposed architecture allows the usage of big data and machine learning to process the collected data. A prototype is implemented to validate the operation of the proposed architecture and a security risk assessment of the implemented prototype is carried out. The implemented prototype successfully validates the proposed architecture. The architecture presented in this work allows the implementation of IoT solutions in different scenarios of farming, such as indoor and outdoor

Dense and long-term monitoring of Earth surface processes with passive RFID -- a review

Billions of Radio-Frequency Identification (RFID) passive tags are produced yearly to identify goods remotely. New research and business applications are continuously arising, including recently localization and sensing to monitor earth surface processes. Indeed, passive tags can cost 10 to 100 times less than wireless sensors networks and require little maintenance, facilitating years-long monitoring with ten's to thousands of tags. This study reviews the existing and potential applications of RFID in geosciences. The most mature application today is the study of coarse sediment transport in rivers or coastal environments, using tags placed into pebbles. More recently, tag localization was used to monitor landslide displacement, with a centimetric accuracy. Sensing tags were used to detect a displacement threshold on unstable rocks, to monitor the soil moisture or temperature, and to monitor the snowpack temperature and snow water equivalent. RFID sensors, available today, could monitor other parameters, such as the vibration of structures, the tilt of unstable boulders, the strain of a material, or the salinity of water. Key challenges for using RFID monitoring more broadly in geosciences include the use of ground and aerial vehicles to collect data or localize tags, the increase in reading range and duration, the ability to use tags placed under ground, snow, water or vegetation, and the optimization of economical and environmental cost. As a pattern, passive RFID could fill a gap between wireless sensor networks and manual measurements, to collect data efficiently over large areas, during several years, at high spatial density and moderate cost.Comment: Invited paper for Earth Science Reviews. 50 pages without references. 31 figures. 8 table