Computers and Electronics in Agriculture 49 (2005) 272–285

FPGA-based real-time remotemonitoring system

Joshua Mendoza-Jasso, Gerardo Ornelas-Vargas,Rodrigo Castaneda-Miranda, Eusebio Ventura-Ramos,

Alfredo Zepeda-Garrido, Gilberto Herrera-Ruiz∗

Biotronics Laboratory, Faculty of Engineering, Universidad Autonoma de Queretaro,Cerro de las Campanas s/n. C.P., 76010 Quer´etaro, Qro., Mexico

Received 12 January 2005; received in revised form 17 May 2005; accepted 16 June 2005

Abstract

Real-time monitoring provides reliable, timely information of crop and soil status, important intaking decisions for crop production improvement. The contribution of this research is the developmentof a real-time remote monitoring system that acquires data from any kind of sensor to be transmittedby radiofrequency to a computer with an interface module, situated within a 900 m radius. Thisallows the sensing of large area fields with a system capable of monitoring crop local environmentalor physiological status; the data transmission and storage in the computer is made in real-time. Todesign this device, the system on a chip approach was followed. Implementation was done in a fieldprogrammable gate array, which ensures a low cost. The performance of this system was testedusing different kinds of sensors and compared with various commercial monitoring systems undergreenhouse conditions. The experimental results showed the system to be reliable. For all experiments,the system obtained anR2 greater than or equal 0.975 in a regression analysis between data acquiredfrom our monitoring system and data obtained from a commercial datalogger with linear fit andsecond-degree polynomial fit.© 2005 Elsevier B.V. All rights reserved.

Keywords:FPGA; Real-time data acquisition; Remote monitoring

∗ Corresponding author.E-mail address:gherrera@uaq.mx (G. Herrera-Ruiz).

0168-1699/$ – see front matter © 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.compag.2005.06.001

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1. Introduction

Evaluation of agricultural production systems is a time consuming and difficult processbecause it means performing visits to selected crop fields to be able to measure and registercertain physical, chemical and biological characteristics of the cultivated areas (Gomideet al., 2001). Monitoring of soil conditions has been traditionally performed through insitu measurements, soil sampling, as well as further analysis in the laboratory (Kaleitaand Tian, 2002). Plant conditions are monitored by taking various measurements, viz.evapotranspiration and photosynthesis, among others. These measurements generally areacquired only at a single point and do not provide data representing the spatial variabilitypresent in soil and plants.

In spite of the use of sensors for in situ measurements, manual collection of data maybecome a task requiring many people. This is a problem when the monitored area of landincreases, and therefore, the number of monitoring sensors is considerably augmented.Furthermore, the acquisition of these measurements can be quite expensive and tedious,especially when the sampling is done at very short time intervals, resulting in a largequantity of measurement data (Kaleita and Tian, 2002). In order to minimize time and useof people to collect data, agricultural technology is moving towards the handling of variablesand their transmission via a remote monitoring, with the consequent saving of resources,as well as an optimization of the technique.

Bangjie (2002)showed the advantage of remote monitoring for complicated landscapes,multi-crop systems, large countries and small family farms in China.Netafim A.C.S. (2004)reported a commercial system called IrriwiseTM, which is a remote monitoring system ofcrop conditions that allows sending data from the sensor to the PC at 15-min intervals.

The objective of this research was to develop a low cost wireless monitoring systemto obtain measurements of current field conditions in real-time. The system can monitorfrom 1 to 16 sensors, send information to a computer via radiofrequency in a half-duplexmode with a 9.6 kbps speed transmission up to a distance of 900 m. Due to the bidirectionalcommunication between PC and the system, the PC works as a remote massive storagedevice. Since the system was developed in a field programmable gate array (FPGA), itoffers low cost, high capacity of expansion and the ability of monitoring several types ofsensors, such as resistance temperature detector (RTD), capacitive humidity sensors, windspeed or direction sensors and soil moisture sensors, such as gypsum blocks, tensiometers,granular matrix, etc.

A number of technologies are available to solve data acquisition, analysis and trans-mission problems. The most common technologies are microprocessors, microcontrollersand digital signal processors (DSPs). Their advantage lies in the fact that programming issimple and many of the instructions are already established. However, these devices requirea high frequency clock and, in the microprocessor case, many extra integrated circuits,which increase the cost and the printed circuit size. The main problem with these devicesis that it is impossible to monitor more than one sensor at a time, due to their sequentialnature (Torres-Huitzil and Arias-Estrada, 2004); thus, the possibility of real-time controldecreases.

Another engineering solution available is using programmable logic. With regard tothis type of technology, instructions or functional blocks have to be designed one by one.

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This technology has the advantage that, all processes can be concurrent (Hanho and Gerald,2003; Torres-Huitzil and Arias-Estrada, 2004), that is, they can acquire data from all sensorssimultaneously, and at the same time transmit the previous data. Another advantage of thesedevices is that, since the blocks are designed for a specific task, they become specific-purpose devices. An FPGA is a form of programmable logic device that makes use ofvery large scale integration technology. It has a high integration capability, coming from8000 to 10,000,000 gates in one integrated circuit. FPGA implementation not only offersthe possibility of optimizing and reconfiguring the designed device, but also the capabilityto perform multiple operations at the same time, yielding an excellent economical benefit.Gschwind et al. (2001)implemented a RISC processor in a FPGA,Reyneri (2004)employedthe FPGA to develop a Simulink-based hybrid codesign to improve signal processing,Ali etal. (2004)used the FPGA to implement a micro universal asynchronous receiver transmitter,while Romero-Troncoso et al. (2004)used the FPGA to develop a tool breakage detectionsystem for CNC milling machines. However, applications in the field of agriculture havenot been reported in the literature.

For monitoring soil, atmospheric and crop conditions, one measurement per second issufficient to achieve real-time control. In greenhouses, this is an advantage because criticalfailures are detected just a second after they occur, so the PC can take control actions almostinstantly. As an example, in a greenhouse with a nutrient film technique irrigation system,if the remote monitoring system (RMS) sends an abnormal water condition measurement(e.g., water flow = 0), the PC can activate the appropriate alarm. RMS may be connected tothe sensors of a weather station to function as a commercial weather station; data on currentweather conditions may be stored in a PC through the RMS interface.

2. The remote monitoring system

Fig. 1 shows the components comprising an FPGA-based remote monitoring system.The radiofrequency transceiver (RFT) enables remote data transmission and reception. Thereal-time clock gives the measuring order to the monitoring system. The sensor interface(SI) provides the RMS with the flexibility to plug in any kind of sensor. The monitoringsystem is implemented in a FPGA, which is in charge of controlling all acquisition functions,

Fig. 1. The system block diagram.

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data processing, answers to PC data requests, programming sampling time, as well as thetransmission of data and error messages.

2.1. Radiofrequency transceiver

The RFT allows wireless connection between the system and the PC. In order to savespace, transmission and reception are carried out using the same antenna. For this reason,communication necessarily has to be half-duplex. With a wireless communication in full-duplex mode, transmission time is reduced by half. Speed and scope transmission dependon transceiver capabilities and antenna characteristics, respectively. A TXM-418-LR (LinxTechnologies, 2004a) transmitter and RXM-418-LR (Linx Technologies, 2004b) receiversharing a 418 MHz whip antenna (Linx Technologies, 1999) are used and all these elementsprovide 9.6 kbps up to a distance of 900 m.

2.2. Real-time clock

The real-time clock works as a clock calendar register and has a special feature, pro-grammable time alarms. These programmable time alarms can be continuous for specificintervals, for instance, every minute, every hour or for a determined time. This saves spacein the FPGA, because it does not work on measuring time. The integrated circuit allows theuser to set sampling time intervals of 1 s or longer. The PC sends sampling and current timeto the monitoring system through the RFT using the 32-bit protocol explained in Section2.4.5. One of the monitoring system activities is to program the real-time clock alarms andset the current time. The alarm interruption programmed on the real-time clock marks thebeginning of the measurements. In addition, a pin-out of the real-time clock provides a 1-Hzsignal used by the monitoring system to measure frequency.

2.3. Sensor interface

The SI module is divided into four kinds of sensors, one with a 4–20 mA output, one withfrequency output, one with digital output, and a granular matrix sensor (GMS). Almost allclimatic and industrial sensors have 4–20 mA protocol transmitters. Following this standardonly a RCV420JP (Burr Brown Corporation, 1997), which is a current to voltage converter,and a serial 12-bit analog to digital converter (ADC) per sensor are necessary. Measurementswith the GMS are usually taken by using an AC current to prevent electrode polarization(McCann et al., 1992). In the GMS case, an oscillator was designed and equipped with avoltage level adapter. The SI block can be reconfigured, in order to adjust output signalsfrom sensors to the system. This means that if only 4–20 mA transmitters are plugged tothe RMS, then only the RCV420 and 12-bit ADCs for the selected number of sensors willbe required. Frequency and digital output signal sensors can be plugged in as a digital inputneeding only a voltage level adapter.

2.4. Monitoring system

The monitoring system module (Fig. 2) is composed of different blocks. The synchroniza-tion block adjusts external asynchronous signals coming from the sensors to the monitoring

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Fig. 2. The monitoring system functional blocks.

system internal timing. The measure state machine block, which is described in the nextsection, is composed of the ADC controller and the period-time meter. This allows theRMS to read practically all kinds of sensors. The data preprocessor unit is a specific appli-cation DSP for adjusting data to their final values by reducing to 8 bits the size of everydatum without losing significant precision. The memory block saves measurement results.Once they are sent to the PC, they are deleted from the memory block. In addition, itsaves the incoming instructions from the PC in a first in–first out memory. The 32-bitasynchronous receiver transmitter (ART32b) sends data and error messages from the RMSto the PC and receives instructions from the PC for the RMS. The control unit controlsand coordinates all units, in addition to executing all instruction requests coming from thecomputer.

2.4.1. Measure state machineThe measure state machine acquires the data given by the ADC, measures the GMS

period-time output signal, as well as measures sensors with frequency output. A finite statemachine controls all ADC sampling times and habilitation signals, but each ADC outputis stored in a different 12-bit register. To measure GMS, which responds to frequency, asub-module called period-time meter was designed.Fig. 3 shows the period-time meterstructure. For measuring period-time, first, the cycle selector unit receives an habilitationsignal to initiate a cycle delimitation process. Secondly, the cycle selection unit enablesthe time counter. At the same time, the time-based generator generates a 1-MHz clocksignal. Finally, the time counter increases the 1-MHz pulse count while it is enabled. Theperiod-time meter output signal is the cycle period-time in�s. Frequency measurementonly needs the time counter; in this case, the time counter is enabled by the 1-Hz signalfrom the real-time clock, and takes the frequency input signal to increase the pulse countusing an accumulator (Accum) to increase this value, so the time counter output is the signalfrequency in Hertz. In the case of sensors with digital output, the measure state machinetransfers the output value directly to the memory block without further processing. Everysensor plugged to the RMS has its own ADC, period-time meter, time counter or digitalinput, depending on the case.

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Fig. 3. The period-time meter. The output signal of the period-time meter is measured in microseconds.

2.4.2. Data preprocessorThis is the key for optimizing data transmission and its storage. A diagram of the system

is shown inFig. 4. This module carries out the conversion of data acquired by the measurestate machine for each sensor unit. For this conversion, a second-degree adjusting equationneeds to be solved for each sensor. For this reason, only parameter registers are updatedfor each sensor type plugged to the terminal. Parametersa, b, c andPadj are updated withthe instruction received from the PC; the instructions are shown inTable 1, Section2.4.5.IR is the feedback value of the Booth Radix multiplier; the other input signal is equal to1. The blocks are described as follows: the word MUX represents a multiplexer; the wordDEMUX represents a demultiplexer and the word Accum an Accumulator. To solve theseequations, the data preprocessor was designed as a specific purpose DSP. It is based on

Fig. 4. The data preprocessor DSP. The signal inputs are described as follows: Inputsa, b andc are parameters ofthe data preprocessor.P is the read value of the sensor.Padj isPminus an adjustment value.

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Table 1Remote monitoring system instruction set

Name Code Parameter 1 Parameter 2 Parameter 3a

ID establishment 0000000 000000 0000000 00 & ID1-DTR 0000001 #s 0000000 00000002-DTR 0000010 #s #s 00000003-DTR 0000011 #s #s #sAP ‘a’ 100**** a20−15 a14−8 a7−1

AP ‘b’ 101**** b20−15 b14−8 b7−1

AP ‘c’ 110**** c20−15 c14−8 c7−1

AP “adj” 111**** adj 20−15 adj14−8 adj7−1

a Sub-indices indicates bit number; ‘#s’ indicates the sensor number written in 6 or 7 bits, according to theparameter length and ‘&’ means concatenation.

a multiplier accumulator of 20 bits× 20 bits using the radix-4 sequential Booth algorithm(Rubinfield, 1975).

2.4.3. Memory blockThe memory block unit keeps hold of the data until a new sampling is done or a data

request from the PC arrives. It also keeps the instructions sent by the PC until they areexecuted. The memory block is a 32-bit random access memory (RAM) divided in twoparts: transmission and reception. Transmission works as a normal RAM, while receptionworks as first in–first out memory, so the incoming instructions can be read in order. Fourinstructions can be stored in the reception part before the first instruction is executed.

2.4.4. The 32-bit asynchronous receiver transmitter (ART32b)The ART32b is the functional block that keeps the RMS in communication with the PC.

Its function is to set up the data to be transmitted serially and to receive instructions fromthe PC. This serial communication unit works as a commercial universal asynchronousreceiver transmitter. The transmission and reception format goes from the least significantbit to the most significant one, 1–32–1. That is, it sends an initial bit, then a 32-bit wordto end with a stop bit, with no parity bit. Transmission speed is programmable and mayvary from 1.2 to 9.6 kbps. The ART32b is composed of four units, as shown inFig. 5. TheBaud-rate generator is a frequency divider that adapts the clock frequency to programmedtransmission speed. The transmitter passes the incoming data from the control unit and thememory block from a parallel to a serial form for its transmission. The receiver reads the34-bit datum, directs it to the asynchronous receiver transmitter control and then returns toa stand-by state to wait for the next datum. The asynchronous receiver transmitter controlcoordinates the other three units and checks the proper data reception.

2.4.5. The 32-bit serial protocolThe proposed protocol is compossed of a 32-bit long instruction. This protocol can be

explained as follows. The remote monitoring system ID goes from bit 32 to bit 28, withthis ID, up to 32 RMS can be identified. Instruction code is 7 bits long specified from bit27 to bit 21; within this length, 64 instructions could be programmed. Parameters 1, 2 and3 are instruction arguments located from bit 20 to bit 1; parameter 1 is only 6 bits long.

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Fig. 5. The 32-bit asynchronous receiver transmitter (ART32b) protocol.

Established instructions are: data transmission request (DTR), adjust parameter (AP) andID establishment.Table 1shows the instruction format. In AP code, four asterisks (****)represent the sensor number.

3. System development

The FPGA is a device that can be programmed only once. Therefore, the system to besynthesized in the FPGA must have the support of many simulations in order to avoid errors,because once the chip is programmed the internal configuration is fixed and does not allowany subsequent changes.

Several simulations were carried out to test all internal signals of each unit. Once thesimulation testing was finished, interconnection at the simulation level among each unitwas carried out to obtain the system’s performance. Before the synthesis of the RMSwas laid on the FPGA, units were independently implemented to perform physical tests.For this intermediate implementation, a complex programmable logic device (CPLD)CY37512P208-125NC (Cypress Corp., 2001) was used on a prototype card. Each unitwas tested in the CPLD because it is reprogrammable, allowing to debug all the errors inthe design. Once simulations and tests ruled out any error, the implementation of the RMSon the FPGA was carried out.

For the physical implementation of the RMS an ACTEL 54SX32A-TQ144 FPGA (ActelCorporation, 2001) was used. FPGA specifications gave 32,000 logic gates available, beingthe outcome an equivalent of 23,065 gates and 853 flip-flop logic complexity. Expected timedelays at functional blocks were 58.8 ns, which is below the 250 ns operational clock period.The synthesis report for the FPGA gave 1221 combinational blocks from 1800 available,and 853 sequential blocks from 1080, totaling 72% usage. A picture of the RMS is shownin Fig. 6.

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Fig. 6. Remote monitoring system (RMS) prototype picture.

Several experimental tests were performed on the RMS, both during acquisition andwhen encoding the parameter for the regression equation.

4. Experiments

The experiments were carried out in a 5600 m2 multi span greenhouse with a north-south orientation having top and lateral ventilation and a 6 m ridge height. All data weretransmitted from the RMS to a PC in an office 150 m away to test remote capabili-ties.

Experiments were set up to test three different sensor output signals, in three differentfield experiments for the RMS. The first experiment consisted of placing two temperaturesensors RTD PT-102 (Iomega Corporation, 2004a), each one with a TX92A (Iomega Corpo-ration, 2004b) transmitter, and two relative humidity sensors HIH 3602 (Honeywell, 2004)with their corresponding TUNATM RH-01 (Castaneda-Miranda and Garcıa-Escalante,2000) transmitter in a radiation shield in the middle of the greenhouse. Both pairs oftransmitters were calibrated with their corresponding reference. One temperature sensorand one relative humidity sensor were connected to the greenhouse climatic control unitTUNATM SCCII developed in Queretaro State University; the arrangement is shown inFig. 7; the others were plugged in to the RMS. Data were stored by each system for 2 weeks.

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Fig. 7. The TUNATM SCCII picture.

Signal adjustment parameters were calculated and updated in the RMS before starting theexperiment.

The second experiment was made to test RMS performance for output signal frequencysensors. A wind speed sensor (Davis Instruments, 2003) was adapted to a servomotor shaft;the servo speed was controlled. As a first step, the sensor output signal was sensed withoutany further analysis. Secondly, an adjustment with the data preprocessor was made in orderto transform the output signal from Hertz to meters per second.

Concerning the third experiment, ten cylindrical containers, each 27 cm in diameterand 30 cm in height, were filled with 10 kg of fine texture soil. An homogeneous con-tainer filled with soil was placed at the middle height of the containers, followed by theinstallation of Watermark (Irrometer Inc., 2004) sensors, all having the same orientation,to simulate the same environment for all of them. Watermark sensors are of the GMStype. The containers were identical, filled with soil 5 cm below the maximum height. Thesoil samples were set to field capacity according toIsraelsen and West (1922). Drying ofsoil samples was done under greenhouse controlled conditions. Before starting the thirdexperiment, accurate parameters were set based on a second-degree polynomial fit betweenthe RMS and a Watchdog Model 200 Data Logger (Spectrum Technologies Incorporated,2004).

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Fig. 8. One-day record of greenhouse temperature. These values were obtained with the remote monitoring system(RMS) and the TUNATM SCCII controller.

5. Results

The results obtained from the first experiment showed that the measurement error insensors with 4–20 mA transmitters depends mostly on transmitter calibration. Adjustmentparameters are the second source of errors.Figs. 8 and 9show 1-day data measurements oftemperature and relative air humidity with transmitters equally calibrated, respectively.Regression analysis of the TUNATM SCCII and the remote monitoring system gaveR2 > 0.998 for both linear and second-degree polynomial fit. A maximum difference inmeasurements between the TUNATM SCCII and the RMS were 0.25◦C for temperatureand 0.25% for relative humidity. Relative humidity drops during the hottest hours of theday in this type of semiarid environments, as is observed in the graph.

In the second experiment, the first part of the results revealed that the inaccuracy offrequency measurements was±1 Hz independently of the measured frequency, but theminimal frequency that could be measured was 1 Hz, since our intention was not aimedat measuring fractions of Hertz. Regarding the second part, the calculated wind speed andregistered wind speed differences changed proportionally with the scale used. However, a

Fig. 9. One-day record of relative humidity. These values were obtained with the remote monitoring system (RMS)and the TUNATM SCCII controller.

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Fig. 10. Drying soil curve. The graph shows the recorded values between the remote monitoring system (RMS)and a commercial data logger.

regression analysis gaveR2 > 0.991 in the mph scale transformation andR2 > 0.994 whenm/s scale transformation was used, bothR2s were adjusted to linear fit.

Regression analysis concerning the third experiment with a linear fit gave a determinationcoefficient,R2 > 0.97 andR2 > 0.99 for a second-degree polynomial fit. These outcomeswere obtained through the comparison between RMS and data logger data. To show thisrelationship graphically, values of soil water tension in centibars are plotted during a 38-hperiod (Fig. 10). It can be seen from the graph that a close relation between the valuesexists. The drop in soil moisture tension with time is the result of not compensating for soiltemperature in the reading data.

Concerning time-measurement capabilities, for sensors with 4–20 mA protocol trans-mitter, measurements took only 3.75�s. For GMS-type sensors, measurements took only100�s. In regard to frequency output signal sensors, measurements were made in 1 s. Withregard to digital output signal sensors, measurements took only 250 ns.

Due to the binary nature of number representation in digital systems there is an inher-ent possibility of approximation error. For example, 2.13 has a binary representation of10001000012 in a 2.8 format, with an error of 0.00109375, when using 10 bits; whereaswhen 8 bits are used with a 2.6 format the representation is 100010002 with an error of0.005. To evaluate this situation, an equation to calculate the maximum output error wasnecessary. The resulting equation is

E = ±2(log2(range)−8) (1)

where range is the region between the limits within which the sensor measures;�x� is thefloor operand, which represents the greatest integer less or equal tox, andE is the maximumoutput error. This equation is useful for users to estimate whether precision is good enoughfor their specific needs.

6. Conclusions

The purpose of the current research was to develop a real-time remote monitoring systemcapable of reading any kind of sensor. Low cost and reliability were the two main goals ofthis design. Each unit comprising the monitoring system was tested in a CPLD before its

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implementation to the FPGA. The three experiments were designed to test the overall RMSperformance.

According to the experimental analysis, the system has an excellent performance andgood precision level. Concerning real-time capabilities, RMS takes 56�s for pre-processingall data and 42.5 ms to transmit all data from the RMS to the PC, with a transfer speed of9.6 kbps. This ensures that RMS can measure weather, soil and crop variables in real-time.At 9.6 kbps it is possible to monitor 368 sensors in 1 s; this means 23 remote monitoringsystems.

This system is easy to use and connect; its installation requires a minimal electronicknowledge, being similar to a data logger for a weather station. It is important to remarkthat the use of a FPGA allows the development of a system in a short time period, withsmall investment and a guarantee in the integrity of the design. The developed prototypecosts US$ 217. This prototype can read two sensors with 4–20 mA protocol transmitters,two GMS-type, 10 frequency output sensors and 2 digital output sensors.

For this version of the remote monitoring system, an interruption in the communicationcauses the loss of all generated data. In a further work, this disadvantage can be avoidedwith the addition of a memory chip; this memory could be an EEPROM or flash memory.Therefore, if generated data cannot be transmitted, for example due to weather conditions,they can be stored while waiting to be downloaded. A 2 Mbyte memory is enough to store16 sensor measurements every second for 34 h.

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