Advances in Sensing Technologies for Smart Monitoring in Precise
Agriculture
Luca Maiolo
a
and Davide Polese
b
Istituto per la Microelettronica e Microsistemi, Consiglio Nazionale delle Ricerche, Roma, Italy
Keywords: Precise Agriculture, Sensors, Flexible Electronics, Chemical Sensors, Physical Sensors, Volatile Organic
Compounds, Selective Gas Sensors, Non-selective Gas Sensors, WSN.
Abstract: The demand for a continuous increment of crop production reducing at the same time the impact on the used
resources is a challenge that can be solved only exploiting the full potential of sensors technology applied in
precise agriculture. In this review, we present the most recent advances in remote sensing technologies to be
deployed in field and in greenhouses to monitor multiple key parameters such as air temperature, solar
radiation, vegetative index, plant microclimate, soil feature, etc.
1 INTRODUCTION
The improvement of crop production minimizing the
efforts in terms of water, soil, nutrient reservoir
represents one of the most impelling challenge in
modern agriculture (Lytridis, Kadar, & Virk, 2006;
Pimentel et al., 2007; Tsiafouli et al., 2015; Weiss,
Jacob, & Duveiller, 2020). To this purpose, the
interest in adopting innovative technologies in precise
agriculture is continuously increasing.
Indeed, precise agriculture has the aim to use
technology and exploit novel and integrated
approaches to maximize the crop production
preserving at the same time the used resources (Pierce
& Nowak, 1999; Schellberg, Hill, Gerhards,
Rothmund, & Braun, 2008) (see fig.1). In particular,
remote sensing can be the most suitable candidate to
assist this transition, allowing the monitoring of plant
nutrients, the presence of pathogens and the evolution
of the crop during the seasons.
Until now, remote sensing has been performed
mainly by using satellite images or airborne LIDAR,
however in the last years, new approaches based on
Wireless Sensor Networks (WSNs) have gained
interest (Cagnetti, Leccese, & Trinca, 2013; Ojha,
Misra, & Singh, 2015; Polese et al., 2019). WSNs can
be deployed with light infrastructures and they can be
equipped with several kind of sensors in order to
monitor different parameters on the plant and in the
a
https://orcid.org/0000-0003-3220-5353
b
https://orcid.org/0000-0002-6332-5051
soil or in the surroundings such as temperature,
humidity, CO
2
content, etc.
Due to the size of the field or in case of
greenhouses a proper trade off should be taken into
account to consider sensor lifetime, sensor costs and
sensor deployment. Moreover, depending by the case,
passive or active nodes should be conceived as
valuable choice regarding the specific WSNs
architecture combined with the features of the field.
In particular, battery lifetime for each node or energy
harvesting methods need to be considered together
with the cost for device dismantling and replacement.
Figure 1: A scheme representing the new paradigm for
precise agriculture: adopting smart technologies for
maximizing of the yield, taking care for the environment.
Maiolo, L. and Polese, D.
Advances in Sensing Technologies for Smart Monitoring in Precise Agr iculture.
DOI: 10.5220/0010415401510158
In Proceedings of the 10th International Conference on Sensor Networks (SENSORNETS 2021), pages 151-158
ISBN: 978-989-758-489-3
Copyright
c
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
151
In this paper, we explore different types of sensors
that can be integrated into a WSN in order to estimate
the state of health of the crop. The monitoring of a
cultivation can be roughly divided into three main
areas: at ground level, at plant level and at aerial level.
Different technologies are involved in these three
types of analysis, even if they can be combined to
obtain a fully picture of the cultivation state at macro
and microscale. A scheme with these technologies is
depicted in fig.2.
In particular, in this review, in section 2 we
describe the sensor used to estimate physical
characteristics; in section 3, the sensors for volatile
compounds; in section 4, sensors for evaluate the soil
conditions; in section 5, the sensors to estimate the
plant stress level; finally, in section 6, conclusion is
described.
Figure 2: A scheme representing in red the technologies
involved in the specific type of monitoring, in black the
parameters usually detected.
2 PHYSICAL SENSORS
Air temperature and solar radiation and in particular
photosynthetically-active radiation (PAR) are ones of
the main factors that regulate the fruit maturation
(Uzun, 2007). The variation of both parameters do not
require fast sensor response, moreover, the daily
mean value is more important of the instantaneous
value, on the other hand, good accuracy and precision
are preferred.
Regarding temperature sensors, among the
multiple options (Childs, Greenwood, & Long, 2000),
thermistors and platinum resistors represent the best
choice even if band gap thermal sensors can be a most
efficient alternative due to cheaper cost and large
variety on the market. Indeed, numerous chips
integrating sensors, analog-to-digital converter and
standard digital communications (making these
devices easily integrate in wireless nodes) are
commercially available and, generally, despite a
lower accuracy, less than 0.5 °C, they can fulfil many
applications.
c)
Figure 3: a) Absorbance spectra of several chlorophylls
(Chl a, b, c1 and d). It is possible to see how the main
absorption occurs in PAR spectrum. b) Absorbance spectra
at plant photosystem level (PSI core and PSII core) and
light harvesting complexes (PSI-LHCI and LHCII) where
the transfer of energy and electrons happen (Reprinted
from: (Kume, Akitsu, & Nasahara, 2018)). c) The effect of
PAR on the different plant process. Reprinted from:
(Ugarelli, Chakrabarti, Laas, & Stingl, 2017).
By definition, PAR is considered the radiation
with a wavelength included in 400-700 nm range, that
plants can use in the process of photosynthesis (see
figure 3), even if recent studies indicate that also
photons with wavelength in the range 701 to 750 nm
may have a role in the plant’s photosynthesis (Zhen
& Bugbee, 2020). This parameter is important to
roughly evaluate the state of health of a plant, since
each plant exhibits a peculiar spectral band depending
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152
by the specific water content, canopy characteristics
and plant form. Considering this spectrum of
radiation that includes visible light and near infrared,
sensors that can estimate, better if separately, both
radiations should be included in every sensor node. A
discrete approach with photodiodes and
transimpedance amplifier guarantee more flexibility,
but integrated solution, with even ADC, guarantees
more compactness. Example of IC integrating both
detectors and digital output are BH1730FVC from
ROHM semiconductors (ROHM, 2016) and
TSL2572 from AMS (AMS, 2019).
3 VOLATILE COMPOUNDS
SENSORS
The changing in the composition of the atmosphere
surrounding the plant represents a simple way to
understand the state of health of the crop since several
volatile compounds participate to plant’s life. For
example, carbon dioxide (CO
2
) is one of the main
components that sustain the plant’s life (Ehlers &
Goss, 2016) and Volatile Organic Compounds
(VOCs) are early markers of the plant’s physiological
dysfunction (F. Martinelli et al., 2015).
Gas sensors have to discriminate different volatile
compounds and at the same time quantify their
presence. If the number of the volatile compounds is
limited and a priori known, a set of selective sensors
can be used, but this approach has the drawback to be
not easily improvable, if new gases need to be
detected and quantified other sensors has to be added.
On the other hand, similar results can be obtained
with a set of unselective gas sensors, following the
electronic nose approach (Gardner & Bartlett, 1994;
Persaud & Dodd, 1982; Röck, Barsan, & Weimar,
2008). In this section, we introduce these two
different classes of sensors focusing on: selective and
non-selective gas sensors.
3.1 Selective Gas Sensors
A selective gas sensor shows a dominant response
respect a compounds rather than others interferes that
can be both physical or chemical (D’Amico & Di
Natale, 2001). The gold standard for selective gas
detection is the measurements of its absorption bands
in ultra-violet, visible and infra-red (IR) regions of the
electromagnetic field (Hodgkinson & Tatam, 2013).
If optical sensors that need of long gas cells cannot be
taken into consideration for in field application, non-
dispersive infrared (NDIR) optical gas sensor that use
a broadband IR source together with two optical
detectors, that are tuned on separate spectrum regions,
to identify common pollutant could be a good
candidate to be used in field applications and some
example of these sensors are already on market
(Alphasense, n.d.-a; Fonollosa et al., 2008;
Hodgkinson & Tatam, 2013; SSTSensing, 2020).
Conversely, these devices show low sensitivity,
interference due to relative humidity and a limited set
of detectable gases, thus limiting their feasibility to
specific application (Dinh, Choi, Son, & Kim, 2016).
Other candidates as selective gas sensor are the
potentiometric sensors: these devices measure the
Nerst’s potential created between a sensing electrode
and a reference electrode separated by a solid
electrolyte as consequence of the adsorption of the
gas. An auxiliary electrode is often introduced to
enlarge the number of detected compounds. (Pasierb
& Rekas, 2009). Potentiometric gas sensors allow
detecting a larger number of volatile compounds than
NDIR, but the number remains limited. Example of
commercial available potentiometric sensors are
NO2-B43F (Alphasense, n.d.-b) or GS+4CO
(Ddscientific, n.d.).
3.2 Non-selective Gas Sensors
With the electronic nose approach, it is possible to
maximize the number of detectable VOCs, reducing
the costs for the implementation of the sensing
platform or the sensing node. Indeed, the usage of
non-selective gas sensors permits to integrate similar
active materials (e.g. polymers, metal oxides) with
different cross sensitivity and demanding the
discrimination of the VOCs mixture to a post-process
computational method (Gutierrez-Osuna, 2002). This
approach has been tested in different scenarios,
showing good results in term of discrimination of
multiple gases and odours in a real environment
(Laothawornkitkul et al., 2008; F Leccese et al., 2018;
Fabio Leccese et al., 2016; F. L. Marco et al., 2017;
E. Martinelli et al., 2015; Pecora et al., 2009; Röck et
al., 2008). One of the main advantage of this
technique is related to the possibility to print the
active layer with polymers or metal oxide inks
directly on the substrate, reducing the manufacturing
time and consequently the fabrication costs.
Moreover, regarding metal oxide gas sensors, it is
worth to mention the possibility of using the same
sensors as multiple “virtual” sensors by temperature
modulation (Hierlemann & Gutierrez-Osuna, 2008).
This approach allows to discriminate different
compounds using a single sensors (Herrero-Carrón,
Yáñez, Rodríguez, & Varona, 2015; E. Martinelli,
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Polese, Catini, D’Amico, & Di Natale, 2012; Polese,
Martinelli, Catini, D’Amico, & Di Natale, 2010).
Nevertheless, the use of temperature modulation
requires energy that is a big drawback for application
in system with energy limitations. To this purpose,
great efforts have been done to limit the power
consumption or introduce materials that maintain
sensing characteristics even at room temperature
(Elmi, Zampolli, Cozzani, Mancarella, & Cardinali,
2008; Polese et al., 2015, 2017).
Finally, it is important to note that the
discrimination algorithms can be implemented
remotely, without providing a specific hardware on
the sensing platform to run them and gas
discrimination and classification can be compared
and sensor nodes can be calibrated among them using
appropriate algorithms (S. Marco & Gutiérrez-
gálvez, 2012; Polese et al., 2013; Yan & Zhang,
2015).
3.3 Ultra-flexible Gas Sensors
The possibility to implement flexible and
conformable sensors directly on the plants (e.g. on the
leaves), represents a smart approach to exploit the
features of flexible electronics in this specific
application (Nassar, Khan, & Villalva, 2018). Indeed,
flexible polymeric sensors can be light, transparent
and they can be tailored in form of net to avoid any
possible damage on the plant. In this way, the normal
physiology of the plant is not affected and the
parameters to be detected can be collected in a
significant space around the plant to monitoring its
microclimate (Zhao, Y, 2019). These sensors can be
integrated into ultra-thin polymeric foils together
with readout electronics and data pre-processing
units, thus allowing the fabrication of complete
sensing node.
In the recent years a lot of examples have been
reported in literature to monitor a plethora of gases,
pollutants and valuable parameters like pH, relative
humidity and temperature (L Maiolo et al., 2014;
Zampetti et al., 2009, 2011). In particular, resistive
and capacitive sensors as well as potentiometric
devices have been proposed (Luca Maiolo et al.,
2013). Moreover, to increase analyte sensitivity and
maintaining low device cost, active layer composed
of polymeric or metal oxide nanostructures have been
presented (Ahn et al., 2010; Chinnappan, Baskar,
Baskar, Ratheesh, & Ramakrishna, 2017; Fiaschi et
al., 2018; Li, Li, Wu, Wang, & Luo, 2019). Indeed,
especially disordered nanostructures like metal oxide
nanorods, nanowires or nanofoams together with
polymeric nanofibers exhibit high sensitivity and
easy fabrication process, with scalable manufacturing
methods such as electrospinning technique, printed
electronics or chemical bath deposition technique
(Ding, Wang, Yu, & Sun, 2009; Strano et al., 2014)
(see fig.4).
Figure 4: A scheme representing the low cost
manufacturing processes used for sensors fabrication.
4 SOIL SENSORS
The soil is one of the main elements for the plant life,
and its characteristics have effect on the quality and
the productivity of the agriculture production. To this
purpose, it is of main importance estimate the soil
physical, mechanical and chemical characteristics.
Physical properties of soil takes into account
colour, texture, structure, porosity, density,
consistence, temperature, and air (Osman, 2013),
whereas mechanical properties mainly takes into
account the mechanical strength mainly due to the
soil compaction that reduces the growth rates of crop
roots (Adamchuk, Hummel, Morgan, & Upadhyaya,
2004). Finally, the chemical characteristics takes into
account the pH and the soil nutrient mineral content.
Generally, physical properties are evaluated by
electrical spectroscopy, optical or radiometric sensors
(Corwin & Lesch, 2005; Robinet et al., 2018;
Romero-Ruiz, Linde, Keller, & Or, 2018), whereas
mechanical properties are estimated by the use of
cone penetrometer (Cho, Sudduth, & Chung, 2016).
Finally, the soil nutrient content is estimated by ion
exchange membranes (Gu & Grogan, 2020; Qian,
Schoenau, & Huang, 1992).
Up to now, the use of the soil sensors in WSN is
very limited if not absent, but, nevertheless, the
possibility of integrating these measurements would
be very interesting in improving the crop health.
However, the integration of standard sensors as
temperature, humidity, pH, and ion selective could be
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154
very interesting to investigate and feasible in the few
next years.
5 PLANT STRESS SENSORS
The challenge of a reliable monitoring of plant
growth and development relies on the possibility to
individuate early stage markers of drought, pathogens
and plant physiological dysfunction long before these
signs become visible. Many physiological functions
of the plant can be related to the abiotic or biotic stress
conditions that induce the formation of reactive
oxygen species (ROS) outside the cells (Qi et al.,
2018). The detection of these markers should be safe
and not harmful for the cultivation, preferring non-
destructive probing methods like optical and remote
sensing techniques.
Among others, portable Raman spectroscopy has
been proposed as valuable tool to obtain a rapid
quantification of the stress phenotype associated with
nutrient deficiency (Gupta, Huang, Singh, & Park,
2020). This technique can be applied directly to the
leaf of the plant without wasting it.
A very elegant strategy has been recently reported
in literature, combining smart electrochemical
sensing with biotechnology (Desagani, Jog, Avni, &
Shacham-Diamand, 2020; Pandey, Teig-Sussholz,
Schuster, Avni, & Shacham-Diamand, 2018). In these
works, the plant itself is a living sensor. In particular,
transgenic plants can be instructed to produce specific
analyte and markers enabling a direct monitoring of
the state of health of the plant.
This approach is part of a larger vision in which
the sensors network itself is a living cultivation. In
this case, the conventional of Internet of Things
(IoTs) is translated into Internet of plants (Bais, Park,
Weir, Callaway, & Vivanco, 2004; Checco & Polese,
2020).
6 CONCLUSIONS
In this review, we report all the innovative sensing
technologies available for the optimization of the crop
production in precise agriculture. The new paradigm
of yield maximization combined with the
preservation of the natural resources can be pursued
by building innovative sensing infrastructures
capable to continuously monitoring the plant
microclimate, the presence of nutrients and the thread
of pathogens. These technologies have nowadays the
potential to offer cheaper devices with high
sensitivity since they can be fabricated with modular
and scalable manufacturing processes. This in turn
provides WSN that can control multiple parameters
and merge information from the ground, at plant level
and from the sky (aerial vehicles or satellite). We
believe that flexible electronics and portable spectral
analysis could represent a unique toolbox capable to
guarantee the foreseen results in precise agriculture
taking into account a responsible use of resources.
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