Advances in Sensing Technologies for Smart Monitoring in Precise

Agriculture

Luca Maiolo

and Davide Polese

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

https://orcid.org/0000-0003-3220-5353

https://orcid.org/0000-0002-6332-5051

soil or in the surroundings such as temperature,

humidity, CO

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

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.

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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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

) 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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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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