Distributed Sensing System Model for Future Internet Agricultural

Application

Sérgio Silva

1,3

, Salviano Soares

1,2

, António Valente

1,3

and António Moreira

3,4

School of Sciences and Technology, Engineering Department, UTAD, Vila Real, Portugal

IEETA - UA, Aveiro, Portugal

INESC TEC -INESC Technology and Science (formerly INESC Porto, UTAD pole), Porto, Portugal

DEEC- FEUP Robotics, Intelligent Systems, Porto Unviversity, Porto, Portugal

1 RESEARCH PROBLEM

To assist the irrigation process is necessary to ensure

the amount of water that we supply to the plants is

on the correct amount. To achieve this we need to

use reliable measuring systems for water amount but

also the amount of chemical agents, like fertilizers,

that contribute to the grow of the plants and also to

the pollution of rivers and water supplies.

Distributed Sensing System Model allows

continuous measurements over time and is based on

unattended wireless sensors capable of measuring

the parameters of interest, and all this information

will then be sent to and stored at a central

monitoring station.

Although there is a great number of sensors that

measure with different techniques, the amount of

water in the soils, the DSS System Model uses the

Multi-functional Heat Pulse Probe (Valente, 2006)

developed by António Valente in order to obtain not

only the soil water content, but also, the electrical

conductivity (EC - as a measure of total nutrients),

soil thermal properties (temperature, thermal

conductivity, heat capacity, and thermal diffusivity –

as a measure of how energy is partitioned in the soil

profile), water flux , and environmental temperature

at the surface.

2 OUTLINE OF OBJECTIVES

The DSS System Model needs to be cost-effective,

as less intrusive as possible and include a reliable

data communications system that allows data

collection from sensors monitoring the crops, land,

and environment. Towards this goal, a non-intrusive

approach is to use low-cost wireless sensors buried

underground, which form a so-called Wireless

Underground Sensor Network (WUSN), together

with a Wireless Aboveground Sensor Network

(WASN) capable of sensing and transporting data

towards the central monitoring station.

The development of Wireless Sensor Technology

(WST) applications in precision agriculture makes

possible to increase efficiency, productivity and

profitability while minimizing unintended impacts

on wildlife and the environment, in many

agricultural production systems.

Real time information from fields provides solid

bases for farmers to adjust strategies and take

decisions.

3 STATE OF THE ART

Agriculture is one of the most ancient activities of

man and unless real and immediate solutions are

found for specific problems or for improving

production and quality is difficult to introduce

innovation and technology. Nevertheless the use of

information from the environment, especially when

we speak about precision agriculture, where the

development of wireless sensor networks and

technology made possible some increases on

productivity and profitability while minimizing

unintended impacts on wildlife and the environment,

in many agricultural production systems.

Therefore the implementation today, of a DDS

system capable of monitoring a wide range of crops

and lands and collecting information, such as soil

water content, electrical conductivity (EC - as a

measure of total nutrients), soil thermal properties

(temperature, thermal conductivity, heat capacity,

and thermal diffusivity – as a measure of how

energy is partitioned in the soil profile), water flux,

and environmental temperature at the surface, is

accept by the framers because they understand the

benefits of this systems based on the understanding

of minimum cost versus increase of efficiency,

productivity and profitability.

Silva S., Soares S., Valente A. and Moreira A..

Distributed Sensing System Model for Future Internet Agricultural Application.

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

In fact the first adopters, in 1994, found precision

agriculture to be unprofitable. Moreover they found

the instances of implementation of precision

agriculture were few and far between. Further, the

high initial investment in the form of electronic

equipment for sensing and communication meant

that only large farms could afford it. But over the

last decade the advancement in sensing and

communication technologies has significantly

brought down the cost of deployment and running of

a feasible precision agriculture framework. The

development of new electronic low cost sensing

probes (Valente,2006), and the emerging wireless

technologies with low power needs and low data rate

capabilities, which perfectly suites precision

agriculture (Wang, 2006), completely transform this

reality making today precision agriculture possible

to any pocket.

Examples like Montepaldi vineyard where since

2005 a test pilot has been deployed and a

commercial system ”VineSense” developed by

(Davide Di Palma, 2010), or the automated fertilizer

applicator for tree crops developed by (Cugati et

al., 2003) where an input module for GPS and real-

time sensor data acquisition, a decision module for

calculating the optimal quantity and spread pattern

for a fertilizer, and an output module to regulate the

fertilizer application rate. Data communications

among the modules were established using a

Bluetooth network.

USDA research group lead by Evans and

Bergman (Evans, 2007) study a precision irrigation

control of self-propelled, linear-move and center-

pivot irrigation systems. Wireless sensors were used

in the system to assist irrigation scheduling using

combined on-site weather data, remotely sensed data

and grower preferences.

A soil moisture sensor network for monitoring

soil water content changes at high spatial and

temporal scale has develop by the Institute Of

Chemistry And Dynamics Of The Geosphere

(ICDG, 2014).

A wireless infrared thermometer system for in-

field data collection develop by Mahan and Wanjura

(Mahan, 2004) with infrared sensors, programmable

logic controllers and low power radio transceivers to

collect data in the field and transmit it to a remote

receiver outside the field.

4 METHODOLOGY

This PhD work will consider the design and

implementation of a Distributed Sensing System

(DSS) capable of monitoring a wide range of crops

and lands and collecting information such as soil

water content, electrical conductivity (EC - as a

measure of total nutrients), soil thermal properties

(temperature, thermal conductivity, heat capacity,

and thermal diffusivity – as a measure of how

energy is partitioned in the soil profile), water flux,

and environmental temperature at the surface. The

DSS system will use the Multi-functional Heat Pulse

Probe (Valente, 2006), developed by António

Valente. This information will then be sent to and

stored at a central monitoring station. The DSS will

be based on unattended wireless sensors capable of

measuring the parameters of interest, including

aboveground and underground. The DSS needs to be

cost-effective, as less intrusive as possible, and

include a reliable data communications system that

allows data collection from sensors monitoring the

crops, land, and environment. Towards this goal, a

non-intrusive approach is to use low-cost wireless

sensors buried underground, which form a so-called

Wireless Underground Sensor Network (WUSN),

together with a Wireless Aboveground Sensor

Network (WASN) capable of sensing and

transporting data towards the central monitoring

station. In order to fulfill these requirements three

major design options will be considered:

1) the use of commercial off-the-shelf and low-cost

devices;

2) the use of open communications technologies

supporting adaptive modulation-coding

techniques and energy-efficiency mechanisms

and multi-hop routing;

3) a three-tier integrated and open IP-based

communications architecture.

The first-tier will be defined by the Wireless

Underground Sensor Network (WUSN) formed by a

set of nodes (sensor nodes with Multi-Functional

Heat Pulse Sensors), buried underground, which are

connected to one or more aboveground nodes.

Underground sensors may communicate using

technologies such as IEEE 802.11g/n/ah and IEEE

802.15.4/g/m. The use of the 433 MHz and 868

MHz bands will be considered to provide

underground-to-aboveground long range

communications. The underground sensors will send

data to the central monitoring station through the

second-tier.

DistributedSensingSystemModelforFutureInternetAgriculturalApplication

The second-tier will be defined by the Wireless

Aboveground Sensor Network (WASN) supported

by standard wireless technologies, e.g., IEEE

802.11n/ac/af, together with existing mesh routing

protocols. Each WASN node will be in charge of

relaying the data generated by the WUSN nodes

connected to it, as well as the data generated by the

local sensors, towards the ground central station.

The use of the 433 MHz and 868 MHz bands will

also be considered, which together with wireless

multi-hop communications, will enable long range

communications and ensure the coverage of wide

distances. The third-tier will be defined by a reliable

sensing infrastructure deployed on top of the less

reliable node to node communication layers defined

by the first and second tiers, by taking advantage of

in-network distributed aggregation protocols. This

approach will allow operating over the less reliable

WUSN and WASN with a reduction on the amount

of data transmitted via information aggregation,

processing, and compression in each intermediate

node. Several of these protocols can also deliver

reliable communication and continuous monitoring,

by building a reliable layer and transforming the

data to make it resilient to retransmission, data loss,

and data duplication.

5 CHALENGES OF THE

IMPLEMENTATION SITE

The layout of hillside vineyards in the Douro Region

is strongly conditioned by the original slope and

relief of the parcels of vines. Also, the soil is mainly

based on complex schist which imposes some

constrains in the assessment of its hydrological

aspects. The unique characteristics of these

vineyards, as well as the topographic aspects,

erosion control, vertical planting, the intrinsic

limited water availability, and wide temperature

span across all day and year. Distributed Sensor

System monitoring and information processing can

help in understanding vineyard variability and

therefore how it might be managed, thus improving

the quantity and quality of the wines.

A feasibly ZigBee based remote sensing

network, intended for precision viticulture has

shown by Morais et al. The network nodes were

harvested from the environment (Morais, 2010).

Figure 1 shows the implementation of an in-field

data acquisition network.

Figure 1: Implementation of an in-field data acquisition

network.

The implementation site is a precision viticulture

environment of “Quinta do Castro”.

To achieve maximum flexibility, the system

should recharge its batteries using energy harvested

from the surrounding environment, from up to three

sources (photonic energy, kinetic energy from

moving water in irrigation pipes and from wind),

avoiding maintenance and human interference.

The DSS platform incorporates information from

remote sensing, from in situ weather conditions,

from water source levels, from soil history, and from

farmer knowledge about the relative productivity of

selected “Management Zones” of the vineyard, can

be applied, for instance, to predict yield and

diseases, and to disseminate advice throughout the

growing season about the optimum usage of water

and the chemical treatments needed.

6 SENSORS HARDWARE

The sensor nodes hardware is based on the multi-

functional probe (MFP) schematically represented in

Figure 2.

Figure 2: Multi-functional probe (Valente, 2006).

The MFP consists of one central heater needle

and four surrounding thermistors, as reported by

Mori et al., (2003). The needles are made from

stainless steel tubing, 0.912m min diameter,

protruding 40mm beyond the edge of the PVC

PECCS2015-DoctoralConsortium

mounting. Spacing between the heater and

temperature sensors is about 8.5 mm. The heater was

made from enameled Stablohm 800A wire

(0.062mm in diameter and 440.8 Ω m−1 of

resistivity), which was inserted in the heater needle.

The heater resistance was 100 (heater resistivity

of 2010 Ω m−1). The needle is then filled with

high thermal-conductivity epoxy glue to obtain

water resistant and electrically insulated heater.

The Wenner array is formed by four aluminium

ring electrodes equally spaced with 3mm separation.

The outer electrodes (denoted as A and B in Figure

2) provide for an alternated current source (1mA

peak-to-peak and 100 Hz frequency), whereas the

potential difference is measured across the two inner

electrodes (M and N).

The overall system, including battery, fits in a

cylinder with 21.4mm in diameter and

approximately 50mm long.

Figure 3 shows the MFP probe before

installation.

Figure 3: Multi-functional probe (Valente, 2006).

For temperature measurements, four thermistors

are used where an excitation current of 200 µA is

used. This excitation current flows directly through

the thermistor generating a voltage across the

thermistor proportional to its resistance.

For the heat pulse, a voltage (VHP = 24 V) is

applied to the heater (RHEATER = 100Ω) 3. The

control is made by turning on the transistor Q1

switch for 8 s and then off for 900 s. The accuracy of

q’ in Eq. (1) is very important for the ∆T

determination. The power dissipated per unit length

of heater needle, q’ is then calculated as,

′  







(1)

where Ih (A) is the current through the heater and

Rm (Ωm−1) is the resistance per unit length of the

heater wire. If the values of Ih and Rm are accurate,

this calculation gives an accurate value for q’. This

requires a voltage measurement of the voltage drop

across a current-sensing resistor, VRSENS.

Electrical conductivity measurements are

performed using a Wenner array where a alternated

current (1mA peak-to-peak and 100 Hz frequency) is

applied to the outer electrodes, whereas the voltage

across the inner electrodes is measured. To

efficiently measure the electrical conductivity, an

integrated sinusoidal current source is needed due to

interactions between the capacitive (dielectric) and

conductive behaviors of soils (Zhang, 2004). Most

sine current generators are based on a direct digital

synthesizer (DDS), which requires a read-only

memory (ROM) sine look-up table and a current

mode digital-to-analog converter (DAC). This

solution requires a large integration area since

waveform generation is closely related to the ROM

size and the DAC resolution. Instead of using such

approach, a set of precisely scaled current mirrors

can be used to produce sine steps (Donfack, 2000).

Sizes of individual current mirror transistors have

been calculated according to sine wave values. The

current increments, ∆ sin(αi), after sin(αi−1) are

given by:

(

The voltage drop at the inner electrodes of the

Wenner array is measured using two peak detectors

for amplitude measurement. These are used to detect

and hold the minimum and the maximum values of

the signal. Due to the switching action of the

excitation circuit, a first-order gm-C filter is used

before the peak detectors.

7 EXPECTED OUTCOME

The use of the proposed DDS System Model is

expected to improve productivity and profitability of

precision agriculture; with the present work we

expect in the near future show that this model can

provide the technology means to farms in order to

achieve the minimum costs versus increases in

efficiency.

The system must provide, with minimum

intrusion and assistance, reliable communications

and data collection from sensors monitoring the

crops, land, and environment.

Provide real time information, from fields, for

farmers to adjust strategies and take decisions.

8 STAGE OF THE RESEARCH

The research is on the initial stage with the

implementation of the wireless sensors and testing of

the communications.

DistributedSensingSystemModelforFutureInternetAgriculturalApplication

ACKNOWLEDGEMENTS

This work is financed by the ERDF − European

Regional Development Fund through the

COMPETE Programme (operational programme for

competitiveness) and by National Funds through the

FCT − Fundação para a Ciência e a Tecnologia

(Portuguese Foundation for Science and

Technology) within project ref. FCOMP − 01− 0124

− FEDER − 022701.

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PECCS2015-DoctoralConsortium