Irrigation in the age of agriculture 4.0: management, monitoring and precision

Abstract

Technological evolution is essential to make irrigated agriculture more efficient in the use of water. Thus, this review article aims to contextualize irrigation in the age of agriculture 4.0 in order to address how these new technologies are impacting the rational use of water. With regard to the automation of irrigated systems, irrigation efficiency with moisture sensors, applications using smartphone, controllers and fertilizer injectors, as well as how their operation can promote irrigation, was addressed. Regarding irrigation management, the use of remote sensing as an option to determine crop evapotranspiration was contextualized, listing the types of spectral bands and sensors used to collect images (orbital, aerial and terrestrial), in the monitoring of crop water status. The importance of data collection in the delineations of management zones for precision irrigation and what possible advances can still be achieved with regard to obtaining and analyzing data were also discussed. Finally, it is concluded that, despite the high efficiency of automated irrigation systems, information of soil, climate and plant attributes obtained through the range of data provided by sensors will be responsible for mitigating the global impacts caused by irrigated agriculture in the near future, since this information can enhance irrigation, with maximum efficiency, thus reducing water consumption by agriculture.

Full text

Revista Ciência Agronômica, v. 51, n. 5 - Agriculture 4.0, e20207695, 2020
Centro de Ciências Agrárias - Universidade Federal do Ceará, Fortaleza, CE
www.ccarevista.ufc.br ISSN 1806-6690
Artigo Científico
Irrigation in the age of agriculture 4.0: management, monitoring and
precision
A irrigação na era da agricultura 4.0: manejo, monitoramento e precisão
Alexsandro Oliveira da Silva1*, Bruna Aires da Silva1, Claudinei Fonseca Souza2, Benito Moreira de Azevedo1,
Luís Henrique Bassoi3, Denise Vieira Vasconcelos1, Guilherme Vieira do Bonfim1, Juan Manzano Juarez4, Adão
Felipe dos Santos5 and Franciele Morlin Carneiro6
ABSTRACT - Technological evolution is essential to make irrigated agriculture more efficient in the use of water. Thus, this
review article aims to contextualize irrigation in the age of agriculture 4.0 in order to address how these new technologies are
impacting the rational use of water. With regard to the automation of irrigated systems, irrigation efficiency with moisture
sensors, applications using smartphone, controllers and fertilizer injectors, as well as how their operation can promote irrigation,
was addressed. Regarding irrigation management, the use of remote sensing as an option to determine crop evapotranspiration
was contextualized, listing the types of spectral bands and sensors used to collect images (orbital, aerial and terrestrial), in
the monitoring of crop water status. The importance of data collection in the delineations of management zones for precision
irrigation and what possible advances can still be achieved with regard to obtaining and analyzing data were also discussed.
Finally, it is concluded that, despite the high efficiency of automated irrigation systems, information of soil, climate and
plant attributes obtained through the range of data provided by sensors will be responsible for mitigating the global impacts
caused by irrigated agriculture in the near future, since this information can enhance irrigation, with maximum efficiency, thus
reducing water consumption by agriculture.
Key words: Precision agriculture. Internet of things. Remote sensing. Management zones.
RESUMO - A evolução tecnológica é imprescindível para tornar a agricultura irrigada mais eficiente no uso da água. Sendo
assim, esse artigo de revisão visa contextualizar a irrigação na era da agricultura 4.0 de forma a abordar como essas novas
tecnologias estão a impactar no uso racional da água. No que concerne a automação de sistemas irrigados abordou-se a eficiência
da irrigação com auxílio de sensores de umidade, aplicativos com uso de smartphone, controladores e injetores de fertilizantes e
como o funcionamento destes pode promover a irrigação. Com relação ao manejo da irrigação, o uso do sensoriamento remoto
como opção para determinação da evapotranspiração da cultura foi contextualizado, relacionando os tipos de bandas espectrais
e sensores utilizados para a coleta de imagens (orbital, aéreo e terrestre), no acompanhamento do status hídrico da cultura.
Versou-se também sobre a importância da coleta de dados nas delimitações das zonas de manejo para a irrigação de precisão e
quais os possíveis avanços ainda podem ser alcançados no que concerne a obtenção e análise de dados. Por fim, conclui-se que,
apesar da alta eficiência dos sistemas de irrigação automatizados, informações oriundas dos atributos do solo, clima e planta
obtidas através da gama de dados fornecidos por sensores, serão responsáveis pela mitigação dos impactos globais ocasionados
pela agricultura irrigada no futuro próximo, já que estas informações podem potencializar com máxima eficiência, a irrigação
de precisão, reduzindo assim o consumo de água pela agricultura.
Palavras-chaves: Agricultura de precisão. Internet das coisas. Sensoriamento remoto. Zonas de manejo.
DOI: 10.5935/1806-6690.20200090
Editores do artigo: Professor Daniel Albiero - daniel.albiero@gmail.com e Professor Alek Sandro Dutra - alekdutra@ufc.br
*Author for correspondence
1Departamento de Engenharia Agrícola, Universidade Federal do Ceará/UFC, Campus do Pici, Fortaleza-CE, Brasil, alexsandro@ufc.br (ORCID
ID 0000-0001-5528-9874), brunashaires@gmail.com (ORCID ID 0000-0002-5554-2320), benitoazevedo@hotmail.com (ORCID ID 0000-0001-
7391-1719), denisevasconcelos@hotmail.com (ORCID ID 0000-0002-3298-4812), guile2007@gmail.com, (ORCID ID 0000 0002-4603-4092)
2Departamento de Recursos Naturais e Proteção Ambiental, Centro de Ciências Agrárias/CCA, Universidade Federal de São Carlos/UFSCAR, Araras-SP,
Brasil, cfsouza@ufscar.br (ORCID ID 0000-0001-9501-0794)
3Embrapa Instrumentação, São Carlos-SP, Brasil, luis.bassoi@embrapa.br (ORCID ID 0000-0001-9469-8953)
4Departamento de Ingeniería Hidráulica y Medio Ambiente/DIHMA, Universitat Politècnica de València/UPV, Valencia, España, juamanju@agf.
upv.es (ORCID ID 0000-0002-2047-7821)
5Departamento de Engenharia e Ciências Exatas, Universidade Estadual Paulista/UNESP, Campus de Jaboticabal, SP, Brasil, adaofeliped@gmail.com
(ORCID ID 0000-0003-3405-5360)
6Crop, Soil and Environmental Science, Auburn University, Alabama, United States of America, franmorlin1@gmail.com (ORCID ID 0000-0003-
0117-7468)

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A. O. Silva et al.
INTRODUCTION
Global projections indicate that demand and
conflicts for water and energy tend to increase significantly
in the coming years, because of, among other reasons,
exaggerated population growth, environmental degradation
caused by anthropic factors, and climate change that
generate extreme meteorological phenomena, and these
factors also pose risk to global agriculture (FAO, 2011). In
view of this, technological evolution is essential to make
irrigated agriculture more efficient in the use of water and
inputs (CHAUHDARY et al., 2020; SANTAELLA et al.,
2013).
In this context, several techniques have emerged
to assist in the agricultural production process, especially
with regard to irrigation, such as satellite navigation, sensor
network, network computing, ubiquitous computing and
sensitive computing, use of programming, creation of
applications (SOUZA; CONCHESQUI; SILVA, 2019),
among others, thus enabling information to be managed
through the internet of things (IoT), hence helping to
monitor and understand more quickly emerging situations
in agricultural areas, obtaining an informed decision-
making with various types of information (AQEEL-UR-
REHMAN; SHAIKH, 2009), leading the world to the path
of precision agriculture (PA).
PA has arrived offering a multitude of advantages
with high potential related to sustainability, yield, better
product quality, lower environmental impact and greater
profitability. Among other purposes, it seeks to improve the
quality of life aiming at food security and rural economic
development. PA uses an approach with scientific
and modern validation, combined with conventional
knowledge and information technologies for an intelligent
agricultural production (SHIRATSUCHI et al., 2014).
In this path of PA, the main technological advances
in irrigation and fertigation refer to the use of irrigation
systems and modern injection equipment, which promote
the most efficient application of water and fertilizers,
and that allow the entire process to be optimized through
automation in the use of technological information with
data collection informed by IoT for each specific area of
the properties, thus defining differentiated management
zones for higher efficiency in the use of water and
fertilizers (INCROCCI; MASSA; PARDOSSI, 2017).
For irrigation management, remote sensing (RS)
presents itself as an option and can be used to estimate crop
evapotranspiration rate (REYES-GONZÁLEZ et al., 2018;
MOKHTARI et al., 2019), water deficit (VIRNODKAR et
al., 2020), and consequently propose an adequate irrigation
schedule of how much and when to irrigate, or employing
techniques of variable-rate irrigation (VRI) based on the
values of vegetation indices (O’SHAUGHNESSY et al.,
2019; BHATTI et al., 2020) obtained through remote
sensors. Data collection is performed through remote
sensors that capture the reflectance of plants without direct
contact with the target, making RS an alternative of non-
destructive method to indirectly measure the water status
of plants, for example.
Thus, this review article aims to contextualize
irrigation in the age of agriculture 4.0, through a literature
review, in order to address how these new technologies
are impacting the rational use of water in agriculture, also
bringing the main technological trends used in this area.
AUTOMATION OF WATER AND
NUTRIENT MANAGEMENT IN
IRRIGATED AGRICULTURE
Knowledge on the contents of water and nutrients
in the soil and on their dynamics in time and space is
necessary for the proper management of irrigated
agriculture. Soil water content varies with rainfall,
irrigation, drainage and evaporation, as well as with
cultural practices and soil conditions. In this context,
there must be technologies that complement each other
and be able to measure the water content in the soil
instantaneously, accurately and continuously, provide
answer to the basic question “when and how much to
irrigate?” and, if possible, masterly execute the decision-
making process (GAVA; SILVA; BAIO, 2016; SOUZA
et al., 2016a).
Constantly monitoring soil moisture and electrical
conductivity makes it possible to calculate the amount
of water and the appropriate time for its application. In
addition, the automation of systems makes it possible
to manage these data and perform functions through the
internet of things (IoT).
IoT is the communication between equipment
(engines, tractors, agricultural greenhouses, sensors
and others equipped with embedded technology) via
connection with a wireless network capable of gathering,
transmitting and exchanging data. It is considered an
extension of the current computer network, which
enables equipment to receive commands remotely and be
used as service providers, paving the way for numerous
possibilities in agriculture.
IoT is the present and future of the computer
network in which devices relate to other devices and users.
In this context, the devices tend to take control of a series
of common day-to-day actions, without the obligation for
users to be fully dedicated to the command of decisions
(SANTAELLA et al., 2013).

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Irrigation in the age of agriculture 4.0: management, monitoring and precision
Domínguez-Niño et al. (2020) successfully used
capacitance sensors to provide automated feedback to
the programming algorithm for soil water management
(Figure 1). In this study, the authors show the feasibility
of automated sensor-based irrigation programming in
apple orchards. The algorithm, based on the water balance
approach and locally tuned through sensor feedback,
provided accurate doses of irrigation throughout the
cropping season, adapting to climatic conditions and to
the crop growing season.
Kamienski and Visoli (2018) suggest a platform
for precision irrigation based on IoT, for which two
scenarios are presented to test the platform: Matopiba
(Bahia) and Espírito Santo do Pinhal (São Paulo).
For the pilot scenarios, an overview of the platform,
its architecture and computational platform and the
development process based on scenarios adopted in the
projects are presented.
In another study using sensors to automate
irrigation management, Souza; Conchesqui and Silva
(2019) present a proposal for a semi-automatic system
with devices that are available on the market (Figure 2).
The authors used Parrot Flower Power® capacitance
sensors, which can measure not only soil moisture, but
also the intensity of solar radiation, soil temperature, air
temperature and the need for fertilization. In this study,
to effect the semi-automation, programming procedures
were carried out in the irrigation controller GreenIQ®
Figure 1 - Schematization of the automated soil water management system (DOMÍNGUEZ-NIÑO et al., 2020)
(Figure 3A), which performed estimates of soil moisture
through capacitance sensors via Bluetooth.
Soil moisture estimates were analyzed by the
GreenIQ® application via smartphone, informing the user
through the local wireless network about suggestions
of when and how much to irrigate, also offering
meteorological data to assist in the user’s decision making
(Figure 3B). In addition, the GreenIQ®semi-automatic
controller was connected to an Alexa® personal assistant
from Amazon® for the voice interface of the irrigation
management programming commands (Figure 3C).
The GreenIQ® controller can operate automatically,
making all decisions of how much and when to irrigate,
but this function was adjusted to semi-automatic. In this
module, the system waits for confirmation to irrigate, so
the operator can confirm via voice command with the
digital assistant or simply through the application on the
smartphone.
The authors were able to conclude that the semi-
automatic irrigation system showed agility and accurate,
which promotes a reduction in labor as there is no need for
manual readings of sensors in the field and an increase in
the amount of information provided to decide how much
and when to irrigate without even the need to be present in
the field. Also, the tests proved the system is efficient. It
was possible to perform irrigation programming through
voice commands. In addition, voice commands ended up
being less attractive, because it is necessary to go to the

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A. O. Silva et al.
Figure 2 - Organization chart for the operation of the
semi-automatic irrigation management system (SOUZA;
CONCHESQUI; SILVA, 2019)
agricultural greenhouse to program the irrigation, while
when using only the GreenIQ® semi-automatic controller,
the irrigation programming can be done from any location
with wireless network signal via smartphone.
Sensors in the management of soil water and nutrients
With regard to the monitoring of water and
nutrient contents in the soil, direct or indirect methods
can be employed to provide an accurate response,
which helps to keep the soil always at a sufficient level
of water and nutrients for plant growth (RAMOS et al.,
2014). The difference between the methods is the way
they are applied, the equipment used and the response
time.
To overcome the obstacles of the direct method,
indirect methods that work with physical properties
of the soil to estimate its moisture and electrical
conductivity have emerged. In this case, Ramos et al.
(2014) explain that the use of more sophisticated devices
Fi gur e 3 - A) Equipment used: GreenIQ® controller; B) Graphical interface of the soil irrigation management application and C) Alexa®
personal assistant from Amazon® (SOUZA; CONCHESQUI; SILVA, 2019)
to apply these indirect techniques, despite enabling
several types of measurement and with a shorter response
time, also require a higher cost due to the acquisition of
equipment.
Among the sensors currently used, according
to Souza et al. (2016b), Time Domain Reflectometry
(TDR) and Capacitance emit electromagnetic waves
at a specific frequency in conductive rods inserted into
the soil to evaluate the interference of water on the
propagation of electromagnetic pulses.
There are two possibilities for the use of
electromagnetic techniques to measure soil moisture and
electrical conductivity with respect to the frequency of
operation of the device. In the first one, the frequency
of operation fluctuates among values below 100 MHz,
called capacitance sensors, also called frequency domain
reflectometry (FDR), while in the second one the device
operates at a frequency of approximately 1.2 GHz, called
TDR (SOUZA et al., 2016a).
These frequencies polarize the dipoles present
in the soil and the phenomenon is responsible for the
effect of water (dipole) on the propagation velocity of the
electromagnetic wave. However, the capacitance, through
a reduction in the operating frequency, enables the use
of frequencies below 100 MHz, which interferes in the
polarization of ions (Figure 4).
This reduced frequency leads to lower cost,
which provides better conditions for the acquisition of
capacitance sensor when compared to TDR sensor. It
is also believed that the use is not consolidated among
farmers and res earchers, due to the abs ence of information
describing its limitations, especially regarding the need
for a calibration for each type of soil.
The devices used in the TDR are a high-precision
oscilloscope (ZHANG et al., 2017), a battery and probes
composed of coaxial cables, stainless steel rods and epoxy
resin head, Figure 5A.

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Irrigation in the age of agriculture 4.0: management, monitoring and precision
According to Souza et al. (2016b), TDR and
capacitance sensor can measure the soil water content
through the propagation velocity of electromagnetic
pulses, correlating this velocity with the soil dielectric
constant (Ka1). In the air this constant is equal to 1, in
solid media it is between 3 and 5, and in the water it
is equal to 81 (PAVÃO; SIMIONE; SOUZA, 2017).
Considering the soil-water-atmosphere system, the
apparent dielectric constant (Ka2) of the soil is measured,
which, in addition to Ka, also takes into account the losses
for the surrounding environment (SOUZA et al., 2016a).
The effect of the sampled medium on the propagation of
the electromagnetic wave is transformed into Ka through
the equation presented by Hook; Livingston (1995):
(1)
Figure 4 - Comparison of the real and imaginary dielectric
constants for the composition of the apparent dielectric constant
for different techniques (TDR and capacitance) in a saturated
soil (SOUZA et al., 2016a)
where,
DX - Distance traveled by the electromagnetic
wave, m;
Vp - Propagation velocity, 0.99 (99% c);
c - Velocity of light, 3 x 108 m s-1;
L - Rod length, m.
Figure 5B shows the correlation between the
reflection coefficient and the distance traveled, which
makes it possible to identify the beginning and end of the
propagation of the electromagnetic wave on the probe
rod and, consequently, to calculate Ka. The generic TDR
probe must comply with questions in its construction for
the perfect identification of the distance traveled, Figure
5C (SOUZA et al., 2006b).
The possibility of adopting the TDR technique
not only in the soil with obtaining of water and nutrient
contents, but also in media such as the plant itself for
monitoring the xylem solution is another attraction
(NADLER et al., 2006; PAVÃO et al., 2014). Calibration
arises from the fact that the equation developed by Topp;
Davis and Annan (1980) is used as the standard, and it has
limitations when applied in soils with high organic matter
content, high salt concentration and presence of iron
oxides, as most soils in the Brazilian territory (SOUZA
et al., 2016a).
However, the use of TDR is limited by its price
of acquisition, for both the probes and the device,
which requires accurate electronics to generate the
electromagnetic pulses (Table 1).
Capacitance is called as such, because its probes
function as electric capacitors, which store energy,
consisting of two charged coaxial plates, one positive and
the other negative. These plates are placed in the soil, which
Figure 5 - A) Tektronix 1502 C reflectometer and WinTDR graphical interface; B) Monitoring of the electromagnetic wave
propagation, where: X1 is the beginning and X2 is the end of the probe rod; and C) Detailing of TDR probe (SOUZA et al., 2006a;
SOUZA et al., 2006b)

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A. O. Silva et al.
functions as a dielectric material, separating the plates and
preventing the passage of electric current between them.
When one of the plates is electrified, there is an expansion
of the electromagnetic field through the oscillation of the
capacitors, which polarizes the water molecules present in
the soil; the higher the water content in the soil, the lower
the amount of energy accumulated on the surface of the
probe (SOUZAet al., 2016a).
Some capacitance devices do not use Ka for their
moisture estimates. These devices calculate the relative
frequency (RF) (SOUZA et al., 2016a), which is converted
into soil moisture (m3 m-3) for each type of soil using a
calibration equation, suggested by the manufacturer or
constructed by the user himself.
The RF aims to offer an option of correlation with
water in the soil without the effect of the imaginary part
(Ka2) on the composition of Ka in the soil (Figure 1). Ka2
is the result of the alteration of the dielectric medium by
the presence of free ions and variations in temperature. Ka
is composed of the sum of Ka1 (real part) plus Ka2, which
is very effective at operating frequencies below 100 MHz
(SOUZA et al., 2016a). RF is defined by the following
equation:
(2)
where,
Fa - frequency count of the probe in the air;
Fs - frequency count of the probe in the soil;
Fw - frequency count of the probe immersed in
water.
Electromagnetic techniques for agriculture
management
Many experiences are described in the literature
for the management of fertigated agriculture using
electromagnetic techniques. Mendonça et al. (2020)
present results that bring an interesting discussion about
the possibility of using 25% of the available water capacity
(AWC) for grape tomato under subsurface drip irrigation.
In this study, 3 limit values of moisture were tested for
irrigation management, which were monitored daily by
TDR Tektronix
1502 Easy Test Soil
moisture
mini Trase
ESI MP -
917 TDR 100 Soil
moisture
Trase Trime FM2
Cost (US$) 116,95.00 4,707.00 6,895.00 5,350.00 3,650.00 9,550.00 4,370.00
Table 1- Comparison of minimum cost for operation between different TDR devices (adapted from ROBINSON et al., 2003)
the TDR, being 0.33, 0.29 and 0.25 m3 m-3, respectively
equivalent to 100, 75 and 50% of soil AWC. Deficit
irrigation of 75% AWC was the most indicated, resulting
in 471 mm of applied water depth, which corresponds to
36% of the 100% AWC depth, promoting the same quality
and quantity of fruits.
The main disadvantage is the need for calibration
for the different tropical soils, as the presence of high
levels of clay, iron oxide and organic matter can directly
interfere in the measurements. As already mentioned,
the techniques require water polarization for reliable
measurements of the behavior of the propagation of the
electromagnetic wave in the soil, which may move in a
way that suggests that there is no storage because water is
not free in the soil. This phenomenon is common in soils
with adsorption accentuated by the increase in the specific
surface of the particles, but does not compromise the
management of soil water and nutrients, provided that the
storage range that will be explored by the plant is defined
and a specific calibration is performed under laboratory or
field conditions.
Calibrations under laboratory conditions are
more limited as they use disturbed soil samples, whereas
calibrations under field conditions are more laborious
because they involve the need to open soil pits. The
literature reports a wide variety of studies on calibration
for the different techniques of capacitance and TDR
probes (CHEN et al., 2019). An equation representing a
specific calibration between the Ka or RF ratio and soil
moisture can hardly be used accurately for another type of
soil (SOUZA et al., 2013).
Berça; Mendonça and Souza (2019) used TDR
probes to monitor the effect of sugarcane straw as mulch
on the distribution and storage of fertigated water and
nutrients in cabbage. These authors concluded that the use
of organic mulch did not interfere in cabbage yield and
promoted savings of up to 28.1 mm (14.5%) in the water
depth used.
About FDR, Bello; Tfwala and Rensburg (2019)
suggest the use of automated sensors for irrigation
management through continuous monitoring of water
absorption by plants. In addition to the use of the
electromagnetic technique for the management of
fertigation, it can also be used in the mapping of the

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Irrigation in the age of agriculture 4.0: management, monitoring and precision
solution in the analyzed profile by correlation with
geostatistics. This is what happens, for example, in the
use of SURFER software, from the company Golden
Software.
According to Golden Software (2017), SURFER
is a program based on data interpolation for the
construction of two-dimensional and three-dimensional
grids that can be used by geologists, archaeologists,
engineers, oceanographers, biologists, climatologists and
other professionals. Examples of the varied applications
of SURFER are the studies of Grecco; Bizari and Souza
(2016) and Souza and Matsura (2004) in the spatial and
temporal characterization of patterns of water and solute
distribution in the soil, Figure 6.
Efficiency and technological advances in fertigation
Fertigation is a technique of fertilizer application
via irrigation water, whose main advantages are the
increase in yield and quality of food and the reduction of
costs with labor and fertilizers. Despite these and other
advantages, investment and skilled labor are required to
carry out management properly (INCROCCI; MASSA;
PARDOSSI, 2017).
The main technological advances in irrigation
and fertigation refer to the use of irrigation systems and
more modern injection devices, which provide the most
efficient application of water and fertilizers, besides
enabling the whole process to be optimized through
automation.
One of the major technological advances in
fertigation is the possibility of using sensors and remote
control through the IoT technology. In high-tech automatic
systems, the equipment can be used in harmony with
agriculture. This means that sensors in the soil to collect
EC and pH data, for example, can be used to adjust
injection systems in real time.
Although current technology enables a high
efficiency of fertigation in any method of pressurized
irrigation, the localized method is the one that has the
greatest benefits, especially for regions with scarcity of
water resources. Localized irrigation, besides reducing
nutrient losses for applying them close to the roots, is
the one that best adjusts to the water saving strategies.
In addition, it allows safer use of wastewater, an activity
that is being used in almost all the world and tends to
expand more and more (NARAIN-FORD et al., 2020).
The current technological advance has facilitated
access to more sophisticated equipment, which allows
controlling the EC and pH of the solution automatically
and/or applying the nutrient solution at a constant
concentration and in several sectors at the same time.
Dosing pumps and automatic fertigation systems are some
examples (Figure 7).
This device offers a complete possibility for
precise fertigation, as it makes it possible to inject and
dose nutrient solutions proportionally (regardless of
pressure variation), keeping them within the ideal ranges
of EC and pH (CARRIJO et al., 2001). In addition, it
can be easily automated and used simultaneously in
various sectors. Despite the advantages, this device
is expensive, needs power to operate, lacks skilled
manpower to operate and requires continuous inspection
and maintenance. The operating principle may vary
according to the manufacturer. In general, the device
is able to inject into the main irrigation pipe various
fertilizers and acids, from a single homogeneous solution
contained in mixing chambers.
The suction of fertilizers and acid in the dosing
channels can be done through Venturi injectors, centrifugal
pumps or dosing pumps. Especially when the injection
is done through Venturis, an auxiliary booster pump is
required to create the pressure difference for suction.
This auxiliary pump enables the use of Venturis with high
suction capacity and low consumption of motive flow. The
form of installation can be inline (Figure 8A) or in two
bypass configurations (Figure 8B and 8C).
Head losses are lower in bypass installation,
especially when the device is connected before and after
the irrigation pump. In this scheme, the irrigation pump
can serve as a dosing booster pump. The operation of this
type of device is based on the programming of a control
panel (Figure 9).
The options available in the panel, which vary
according to the manufacturer, makes it possible for
example to register and select formulations (nutrient
solutions) specific to a given crop, to adjust the EC or pH of
the solution to a desired value (via algorithms), to choose
independent settings for each sector to be fertigated, to
enable alarms for maximum and minimum values of EC
and pH, among other options. Fertigation can be initiated
from an irrigation controller and, depending on the model,
it is also possible to control irrigation.
The maintenance of this system is usually
performed at short intervals (daily, weekly, others) and
involves several procedures such as cleaning filters
(fertilizer and supply water), inspection for water and
fertilizer leaks, calibration of the sensors of EC, pH etc.
Automation has enabled higher efficiency in the
use of resources, due to greater control of activities. The
application of water and fertilizers in the right quantity
and at the ideal time, the reduction in pump actuation
and the lower human intervention have promoted greater

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A. O. Silva et al.
Figure 6 - Soil moisture profiles (m3 m-3) for drip irrigation with flow rates of 2 and 4 L h-1 (SOUZA; MATSURA, 2004)
Figure 7 - Hydraulic dosing pump (A) and automatic fertigation system (B)
Source: gavish.intercallchat.info and cannapro.com
yield and saving of water, energy, fertilizers and labor,
among other advantages. Some studies show that the use
of modern irrigation and fertigation systems can increase
yield and/or reduce environmental costs and impacts
(CHAUHDARY et al., 2020; KASSING; SCHUTTER;
ABRAHAM, 2020).

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Irrigation in the age of agriculture 4.0: management, monitoring and precision
Figure 8 - Installation schemes for a NetaJet automatic fertigation system: inline (A), and in bypass with the system connected before
and after the irrigation pump (B) and after the irrigation pump (C)
Source: netafim.com
Considerations on fertigation management - case of
irrigating communities with pressurized distribution
networks
If the fertilizer distribution scale is expanded
and the injection is performed in community systems
or systems that supply different plots / users, through
a pressurized network with a high number of hydrants,
the problem of obtaining a good uniformity becomes
more complex. This is the case in regions such as the
Mediterranean, where there are problems with water
supply both spatially and temporally. A common solution
in the search for improved water management is the
construction and management of collective irrigation
infrastructures. For this, on many occasions they are
constructed in irrigating communities (association of
water users). Through these community structures, the
goal is to improve the abstraction and distribution of
water, from the points of view of both water and energy
efficiency.
In Spain, the irrigated area in 2019 is estimated at
3.83 million ha, of which more than 76% are drip systems
(53%) and sprinkler systems (23%) (ESYRCE, 2019).
Such distribution is due to a modernization process,
promoted by public administration, combining public and
private resources (TARJUELO et al., 2015). On the other
hand, at least 80% of this entire irrigated area is maintained
under community irrigation structures. Therefore, most of
Figure 9 - Fertigation controller panel (A) and detail of the program interface (B)
Source: hidrosense.com.br
them are pressure irrigation networks that have some kind
of automation system and control of irrigation, and many
of them have community fertigation systems (GARCIA,
2018).
Collective fertigation is even more justified
when there are major monocultures with homogeneous
fertilizer needs and the average dimension of
the exploitation of each producer is small, with
paradigmatic cases such as the Valencian community,
where traditionally the average dimension of the plot
is very small (less than 1 ha) and citrus monoculture is
dominant (ORTEGA-REIG et al., 2017).
The way to operate the necessary infrastructure
and manage fertigation is varied and will depend greatly
on the degree of automation and management of the
network. First, the way water is distributed will be
decisive; for example, if the network operates on demand
(organization with total flexibility), where each user can
freely open the water output of its plot, the fertilizer
flow injected must be continuous and proportional to the
flow transported by the main network. In this situation,
the injector device must consist of electric injectors,
preferably centrifugal type, where the solution flow will
be adjusted by modifying the speed of rotation of the
pump. For this, it will be necessary to use a frequency
variator for the pump motor, operated under the pulse
transmitter of the meter or the electronic flow meter,

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A. O. Silva et al.
located in the main pipe of the distribution network. If
this is the operating scheme, the solenoid valves are not
required at the different outlets in the field, since it is
not the end user who performs the opening and closing
operation.
If the network distributes water in fixed sectors
or shifts (rigid organization) and each plot has the same
irrigation time determined by the network managers, the
collective injection can be programmed in a more rational
and fractionated manner, distributing the times for cleaning
and application of each fertilizer. However, in this case, a
much higher degree of automation is required, and it is
necessary to equip all multi-user hydrants with solenoid
valves. In this case, the injection devices that can be used
are enlarged, although electric injectors are always more
interesting for community networks, with piston pumps
giving good results in this condition.
However, the introduction of new crops in the
irrigation sectors, which have different water or fertilizer
needs compared to monoculture, implies not applying the
same treatment for all plots. Another important change
arises when many of the plots are used for organic
cultivation, which implies washing of the network of
substances that are not compatible (GENERALITAT
VALENCIANA, 2020).
Under these new conditions, the options of on-
demand irrigation systems are no longer viable for
a correct distribution of fertilizers, and it is almost
mandatory that the control of irrigation and fertigation
times be fully organized by network managers and
not by end users. Jiménez-Bello et al. (2011) studied
the behavior of the water distribution network and
the behavior of fertilizer evolution for each hydrant,
analyzing how irrigation programming conditioned the
time of fertilizer application in each plot.
It is possible to establish a methodology, by
means of an EPANET model (ROSSMAN, 2000), that
homogenizes irrigation times if there are users who do not
want fertigation. Likewise, if the network is dependent
on energy for pressurization, it is possible to establish
strategies to ensure adequate fertigation times. An
interesting conclusion of this model is that, if there are
hydrants/plots that do not want community fertigation, it
is difficult to ensure that no amount of chemical reaches
them, while maintaining the correct time of fertilization.
Other complementary measures are defined for
this management, thus requiring the sectorization and
hydraulic regulation of the network, which includes
monitoring by means of EC and pH sensors in the head
control, before and after injection and in critical hydrants
of the network, to monitor and verify the appropriate
dosage of fertilizer. In addition, for the injection of
products with high added value and at low concentrations,
it is necessary to design, besides the main heads, product
injection hydrants in strategic points of the network, to
reduce travel times and optimize the application of the
products (GENERALITAT VALENCIANA, 2020).
IRRIGATION MANAGEMENT USING
REMOTE SENSING
In order to provide spectral and spatial information
during the crop season, remote sensing (RS) has been
widely applied in the monitoring of the temporal and
spatial variability of agricultural crops in recent years.
Among the activities in agriculture, RS is being widely
applied in the field of irrigation for a more efficient
water management. In RS, data are obtained by means
of sensors that capture the reflectance of plants without
direct contact with the target, making RS an alternative of
a non-destructive method to indirectly measure the water
status of the plants, for example.
However, it is worth pointing out that the
effective monitoring of crops using RS depends on the
type of platform on which the sensor will be embedded
as well as on the level of collection (orbital, aerial
and terrestrial), and spatial, spectral, radiometric and
temporal resolutions may interfere with monitoring
(BERNI et al., 2009). Thus, we will briefly discuss
the use of RS for irrigation management, which can
be employed to estimate the evapotranspiration rate
(REYES-GONZÁLEZ et al., 2018; MOKHTARI et al.,
2019), water deficit (VIRNODKAR et al., 2020), and
consequently propose an adequate irrigation schedule
of how much and when to irrigate, or using variable-
rate irrigation (VRI) techniques based on the values of
vegetation indices (O’SHAUGHNESSY et al., 2019;
BHATTI et al., 2020).
Estimation of water status
Among the spectral bands used in the monitoring
of irrigation management, the thermal band, in the
infrared region, is the one that has a direct correlation
with the estimate of plant water status, as water status is
based on leaf temperature, which is inversely proportional
to stomatal opening and transpiration (FUCHS, 1990),
being commonly used to calculate the crop water stress
index (CWSI). Thermal sensors capture leaf temperature
changes more easily, indicating variations in water status
and stomatal conductance, which consequently may
result in yield gains, which can reach up to 30% (LOPES;
REYNOLDS, 2010) when irrigation is adjusted based on
these parameters.

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Irrigation in the age of agriculture 4.0: management, monitoring and precision
Despite not compromising the use in agriculture,
the application of thermal images has some limitations,
such as the high cost of cameras (KHANAL et al., 2017)
and low resolution of images (satellites or unmanned
aerial vehicle - UAV), which can cause the extraction of
mixed pixel (JONES; SIRAULT, 2014). However, despite
these limitations, several applications of thermal images
and UAV, including their use in water status estimation,
are presented by Messina and Modica (2020).
Due to the low spectral resolution of thermal
images, several vegetation indices (VI) that use the visible
and near infrared (NIR) bands have been proposed as an
alternative in the monitoring of crop water stress, mainly in
the estimation of leaf water potential (LWP) and stomatal
conductance. VIs that use visible and NIR bands in their
composition are able to detect physiological changes in the
photosynthetic apparatus, such as increased degradation
of chlorophylls, carotenoids and xanthophylls, which
consequently reduces leaf reflectance, besides reducing
stomatal opening under water stress conditions (ZARCO-
TEJADA et al., 2013).
Estimating LWP by means of RS increases the
capacity of analysis in a commercial area (COHEN et al.,
2015), which consequently makes it possible to generate
spatial variability maps of the plantation based on the
indices that estimate water stress. In addition, it makes
it possible to monitor variations in water use by the
crop, increasing the accuracy in irrigation management
(COHEN et al., 2015; QUEBRAJO et al., 2018).
The normalized difference vegetation index
(NDVI), developed by Rouse et al. (1974), is the most
usual among the VIs that have a structural correlation
with plant water stress, that is, VIs with good correction
with variations in stomatal conductance and LWP. Other
VIs, such as renormalized difference vegetation index -
RDVI (ROUJEAN; BREON, 1995), and optimized soil
adjusted vegetation index - OSAVI (HABOUDANE et
al., 2002), have also been pointed out as an alternative for
monitoring water stress, especially when the vegetation
cover is low and, consequently, soil reflectance interferes
in the values of the vegetation pixels. This is one of the
main advantages in the use of these VIs, especially when
using satellite images.
In addition to these VIs that use only the visible
spectrum, the normalized difference water index - NDWI
(GAO et al., 1996) can be used as an alternative to
estimate LWP, as it is generated from the combination of
wavelengths in the NIR and shortwave infrared (SWIF)
ranges. So, this VI can capture changes in reflectance due
to changes in the internal structure of the leaf through
the NIR, as well as changes in leaf water content through
the SWIF (IHUOMA and MADRAMOOTOO, 2017).
Consequently, these factors make it a potential option to
manage the LWP and irrigation of crops.
Estimation of Evapotranspiration (ET) and Crop
Coefficient (kc)
Crop irrigation schedule around the world is
based on ET estimation, which considers the kc of crops
at each stage of development. Estimating ET is essential
to manage irrigation and make efficient use of water.
There are different ways to estimate ET, either directly
(weighing lysimeter and soil water balance) or indirect
(pan evaporation, atmometer, Bowen ratio energy balance
system (BREBS), eddy covariance (EC), scintillometer,
sap flow and remote sensing (ALLEN et al., 2011;
REYES-GONZÁLEZ et al., 2017).
Due to the rapid growth and adoption of remote
sensing and its ability to cover extensive areas, several
researchers around the world have used satellite images
to estimate ET, through mathematical models and VIs,
proposing irrigation schedule based on spatial variation of
ET (FRENCH et al. , 2015; CHEN et al., 2018; OLIVEIRA-
GUERRA et al., 2020).
Mapping evapotranspiration at high resolution
using internalized calibration (MATRIC - ALLEN et al.,
2007), and the surface energy balance algorithm for land
(SEBAL - BASTIAANSSEN et al., 1998), from which the
former is derived, are the most used models to estimate ET
in various crops. These models combine visible spectral,
NIR and thermal bands together with meteorological
data to empirically estimate spatial variations of
hydrometeorological parameters useful for water
management and use, especially irrigation management
(SANTOS et al., 2008; REYES-GONZÁLEZ et al., 2017;
FRENCH et al., 2015; ANDERSON et al., 2012).
Among the main advantages of using RS to access
ET is that, through the construction of spatial variability
maps of ET using VIs, it is possible to visualize and
identify spatial variations within the same area, whereas
the use of only local weather stations does not have
such a dimension of variability. This advantage allows
making better decisions and understanding the dynamics
of water consumption by crops over time. Estimating ET
by means of RS can reduce by up to 18% the amount of
water destined for irrigation, taking NDVI as an irrigation
management parameter (REYES-GONZÁLEZ et al.,
2018). In addition, through spatial and temporal analysis
it is possible to verify the water requirement in each stage
crop development (Figure 10).
Using satellite images (Landsat) to extract the
reflectance values and calculate the NDVI, linear models
showed the relationship of Kc estimated by NDVI with
the reference values stipulated by FAO-56 for grass

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A. O. Silva et al.
(kc = 1.5507*NDVI - 0.0229) and for alfalfa (kc =
1.1981*NDVI - 0.1002) in the Lagunera region, Mexico
(REYS-GONZÁLEZ et al., 2018). Similarly, but using
MODIS images, Kamble et al. (2013) developed a linear
model (kc = 1.457*NDVI – 0.1725) for corn crop in which
NDVI variations are related to Kc variations of the crop
during its growth cycle, mainly in irrigated areas.
However, it is important to emphasize that the
estimation of kc using VI may vary in space and time,
especially due to variations in the cropping system
and agroclimatic characteristics of each region, so the
values estimated for a crop in a given region may not be
applicable for other growing regions (VANINO et al.,
2015; VILLAGRA et al., 2014), which makes it necessary
to analyze and develop local methods that are functional
for each region. Despite this geospatial limitation, the
use of RS techniques to assist in irrigation programming
may be more accurate than fixed values in the literature
(VANINO et al., 2015), such as those pre-established by
Figure 10 - Temporal and spatial ETc calculated using NDVI from Landsat 7 and Landsat 8 images for silage corn crop. Lighter
values indicate lower water requirement and darker values indicate higher water demand. (Adapted from REYES-GONZÁLEZ
et al., 2018)
FAO-56. This is because the methods that use RS consider
the spatial variability of the crops, which allows different
managements and consequently better water use.
PRECISION AGRICULTURE:
MANAGEMENT ZONES FOR
DIFFERENTIATED OR VARIABLE-RATE
IRRIGATION
In conventional agriculture, the heterogeneity
present in an agricultural production area is not considered,
and average values of soil and plant attributes, representative
of this same area considered as uniform, are taken into
account for decision-making regarding the performance of
an agricultural practice, usually related to the application of
an input (fertilizer, soil amendments, seed, water, pesticide
etc.), soil sampling, plant sampling and harvest.

Rev. Ciênc. Agron., v. 51, n. 5 - Agriculture 4.0, e20207695, 2020 13
Irrigation in the age of agriculture 4.0: management, monitoring and precision
However, from the end of the 1980s, the
performance of agricultural practices in a specific
way with local conditions of cultivation became an
object of interest (ARSLAN; COLVIN, 2002), that
is, precision agriculture (PA). Thus, the variability
between the various parts of an agricultural area began
to be considered in the performance of agricultural
management, which led to the need to perform an
agricultural practice uniformly in one sub-area, but
in a differentiated way in relation to another sub-area.
Each sub-area that receives a specific management is
defined as a management zone (MZ).
The heterogeneity present in an agricultural field
is caused by the spatial and temporal variation of several
factors such as climate, topography and biological
activity (CÓRDOBAet al., 2013), or even, MZ is a sub-
region that is relatively homogeneous in terms of soil
and topography attributes (ROUDIER et al., 2008;
HAGHVERDI et al., 2015), and for which a specific
application rate should be used (ROUDIER et al.,
2008). Thus, there may be a reduction of resources used
and optimization of yield (SCHEPERS et al., 2004).
Therefore, delineating the MZ is a critical problem due
to the varied situations of heterogeneity encountered in
agriculture and the purpose for its application.
When the temporal-spatial variation of the area
to be irrigated is significant, the application of water at
variable rate or in a differentiated way by an irrigation
system can improve water use efficiency and crop
yield. Conventional irrigation management, without
considering such variability, helps to define the moment
and how much to irrigate, while variable-rate irrigation
can help improve the definition of how much and where
to irrigate. The methods for delineating MZ may vary
according to the data used and the techniques used for
this. Embedded sensors, proximal remote sensing, and
orbital sensors can aid in field data collection. Spatial
and temporal resolution and accuracy vary depending
on available data and influence the MZ delineation.
Yield data only may not have a good potential for MZ
delineation due to temporal variability. In practice, the
knowledge that the farmers have about the conditions of
their cropping area can also be very useful for choosing
the most appropriate data for defining MZ for irrigation
(HAGHVERDI et al., 2015).
Knowledge on the spatial variability of water
storage (Figure 11) facilitates the performance of the
differentiated management of irrigation in the field,
while the temporal stability of this attribute can identify,
in the field, points that best represented the spatial
average for the area. Furthermore, from this knowledge,
it is possible to reduce the number of samples needed to
estimate a representative average with high precision and
to determine representative points of the area that can be
used for soil water monitoring (LEMOS FILHO et al.,
2015; LEMOS FILHO et al., 2016).
MZs can be defined based on one soil attribute,
such as the available water at the effective depth
of the root system of the crop, using geostatistical
analysis (NASCIMENTO et al., 2014), based on the
particle-size composition of the soil as percentages of
sand, silt and clay (OLDONI et al., 2018), or based
on various soil attributes, such as apparent electrical
conductivity (ECa), particle-size composition, bulk
density and available water (OLDONI; BASSOI,
2016), when the multivariate analysis fuzzy c-means
clustering can be used to define the number of MZ.
Jiang et al. (2011) used four soil attributes (saturation
moisture, field capacity, permanent wilting point and
bulk density) to characterize soil spatial variability
and, through principal component analysis, obtained
the delineation of two MZs, besides providing a means
of verification as to the optimal number of soil samples
to be obtained.
Despite the development already carried out
and the knowledge already acquired regarding the use
of MZ for the differentiated management of irrigation
or water application at variable rate according to the
variability found in the field, Smith and Baillie (2009)
state that the commonly used meaning for the term
precision irrigation addresses the application of water
volume at the depth of the soil profile explored by
the root system of plants and at the desired time, but
uniformly, without taking into account the variability
of the area, besides being also associated with the use
of equipment and sensors for the practice of irrigation.
Thus, the term precision irrigation is used incorrectly.
Figure 11 - Example of spatial variability of soil water storage
in agricultural area

Rev. Ciênc. Agron., v. 51, n. 5 - Agriculture 4.0, e20207695, 202014
A. O. Silva et al.
2017/ 25610-7) for the scholarships granted. To Revista
Ciência Agronômica for the invitation to publish this
article.
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CONCLUSIONS
1 - To ensure the sustainability of irrigated agriculture,
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availability can be reduced due to anthropic actions
in nature, the use of automated and high-precision
irrigation systems is expected to increase significantly
in the coming years, expanding with the advancement
of technology;
2 - However, despite the high efficiency of these systems,
information of soil, climate and plant attributes obtained
through the range of data provided by sensors will be
responsible for mitigating the global impacts caused
by irrigated agriculture in the near future, since this
information can enhance precision irrigation with
maximum efficiency, thus reducing water consumption
by agriculture.
ACKNOWLEDGEMENTS
To the Coordination for the Improvement of Higher
Education Personnel (CAPES), the National Council for
Scientific and Technological Development (CNPq) and the
São Paulo State Research Support Foundation (FAPESP -

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