Internet of Trees: A Vision for Advanced Monitoring of Crops

Alessandro Checco

1,∗ a

and Davide Polese

2,∗ b

Information School, The University of Shefﬁeld, Shefﬁeld, U.K.

Istituto per la Microelettronica e Microsistemi, Consiglio Nazionale delle Ricerche, Roma, Italy

Keywords:

Precision Agriculture, WSN, OpenThread, Chemical Gas Sensors.

Abstract:

Ecosystem preservation and production maximisation are competing objectives in agriculture. Reducing the

need of undifferentiated or late interventions on the crops would reduce the number of disease treatments

needed, as well as the consumption of water and fertiliser. This objective is only attainable through crop mon-

itoring systems able to reach a single plant. Precision agriculture employ continuous and pervasive monitoring

of crops, that in turn allows fast and targeted interventions. The aim of this paper is to highlight the problems

that can be found in designing a wireless sensor network (WSN) able to measure environmental parameters

such as relative humidity, irradiance and volatile pollutant concentration and introduces a possible solution

that we named the Internet of Trees.

1 INTRODUCTION

To optimise production and reduce waste of re-

sources, a detailed knowledge of the health state of

the crop, soil, water and nutrient reservoir needs to be

built (Ehlers and Goss, 2016). Up to now, such infor-

mation has been extrapolated by airborne LiDAR or

by processing satellite images. Both techniques have

high costs and suffer from low resolution (Estornell

et al., 2014; Cunha et al., 2015); moreover, even if

these approaches allow to evaluate the state of health

of the crop, they have poorly results on early detec-

tion of plant disease (Zhang et al., 2019). On the other

hand, the Volatile Organic Compounds (VOCs) have

been demonstrated to be early markers of the state of

health of the plant (Martinelli et al., 2015).

To this purpose, several techniques such as gas

chromatography mass spectrometry (GC-MS) or elec-

tronic noses have been used to detect and estimate

volatile compounds in crops (Martinelli et al., 2015).

Nevertheless, these techniques have been used to es-

timate gas concentrations in single points within the

crop that cannot supply any information on the state

of the whole farm. To this purpose, we will inves-

tigate how to design a wireless sensor network en-

dowed with a customised set of sensors able to detect

volatile pollutants and the relevant VOCs, and a tool

https://orcid.org/0000-0002-0981-3409

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

∗

Both authors contributed equally to this manuscript

for data collection, processing and visualisation, able

to extrapolate detailed maps of the crop health.

The parameters measured by each node provide

information on the status of the crop with a detail

that depends only on the number of nodes composing

the network, and that can reach even a single plant.

In addition to local information, trends and dynam-

ics across ﬁelds can be obtained in post-processing

and this derived information can be useful to organ-

ise farming interventions. With the aim of highlight-

ing the different information that can be extrapolated

by the WSN, an appropriate system of data collec-

tion, processing and visualisation needs to be im-

plemented. In the implementation of a such perva-

sive network two main problems arise: (i) Long time

working time of the single node, i.e. low energy con-

sumption and energy harvesting. (ii) Network recon-

ﬁguration in case of node failure, and easy access to

single node information.

In this paper, we will introduce a WSN to monitor

the environmental parameters of the crop and a web

based platform for data collection, processing and vi-

sualisation. In particular, we will clarify which are

the main characteristics that a WSN for precise agri-

culture should have and how can be obtained thanks

to an integrated design of the hardware and software.

Our user target is initially the agronomy commu-

nity, with the goal to expand it to the farmer commu-

nity when the system will be more widespread.

In the following sections, we will describe our

182

Checco, A. and Polese, D.

Internet of Trees: A Vision for Advanced Monitoring of Crops.

DOI: 10.5220/0009368801820186

In Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020), pages 182-186

ISBN: 978-989-758-403-9; ISSN: 2184-4380

idea of the hardware and network architecture, and

the features that they should have to fulﬁll our require-

ments to guarantee adequate monitoring performance.

At the end of every section we will present some im-

plementation examples.

Related Work

In the last years, precision agriculture has been identi-

ﬁed as a promising research ﬁeld to improve the mon-

itoring and managements of crops.

While few studies propose crop automation so-

lutions (e.g. automated ventilation in (Stipanicev

and Marasovic, 2003) or irrigation as in (Sahu and

Mazumdar, 2012)), the majority of them focus on

crop monitoring, using mainly humidity, light and

temperature sensors (Fisher and Kebede, 2010; Dan

et al., 2015; Patil and Kale, 2016; de Lima et al.,

2010; Yoo et al., 2007; Jiber et al., 2011).

We refer to the extensive review papers in preci-

sion agriculture for more information on the state of

this research ﬁeld (Liaghat et al., 2010; Brisco et al.,

1998; Ge et al., 2011; Jawad et al., 2017).

Novelty

While the use of WSNs for monitoring, management

and prediction purposes is not new, to the best of our

knowledge no previous work addressed the goal of

integrating chemical sensors in the network nodes for

precise agriculture. Such an ambitious goal would al-

low to understand the disease diffusion process, their

correlation with other environmental parameters, and

to build more accurate disease prevention models.

2 HARDWARE ARCHITECTURE

The hardware part of the network, i.e the nodes, have

to fulﬁll several main tasks: (i) Acquire the sensor

data. (ii) Communicate the sensor data. (iii) Harvest

energy. (iv) Minimize the energy consumption. In

Figure 1, a schematic representation of a possible ar-

chitecture of a WSN node satisfying the aforemen-

tioned constraints is shown. The node is based on

a micro-controller (µC) and at least a wireless inter-

face. The µC is assisted by a Battery Management

(BM) and a Maximum Power Point Tracking (MPPT)

circuit, for the power management, ﬁnally electronic

interfaces are required for the physical and chemical

sensors.

With more details, the micro-controller perform

the data reading from the sensors, manage the energy

harvesting and the power consumption and elaborate

Chemical

gas sensor

Physical

Sensor

RF interface

MPPT

Figure 1: Schematic representation of the sensor node ar-

chitecture. In particular three main parts are highlighted:

the battery management (BM) and the Maximum Power

Point Tracking (MPPT) circuits; the Microcontroller (µC),

the Radio Frequency (RF) interface; the physical sensors

(Light intensity and Temperature) and the chemical sensors

(Relative Humidity, VOC sensors).

the data to send to the RF interface. The RF inter-

face should supports different protocol, in order to

allow the communication in long range (LoRa), but

sometimes a single node short range communication

could be useful to download single node information,

perform node debug, or simply for updating the node

GPS position during the installation procedures.

The MPPT and BM circuits have the role of opti-

mizing the power transfer from a photovoltaic panel

towards the battery, preserving the battery function-

ality and energy in order to guarantee a continuous

functioning of the nodes in the long period without

maintenance. The solar energy appears as an obvi-

ous source of energy since these nodes will be placed

onto the tree in a crop, nevertheless the foliage could

sometime darken the panel.

3 SENSORS

Light, water and CO

are the three main components

that sustain the plant life (Ehlers and Goss, 2016), any

lack of these three components have an effect on the

state of health of the plant, at the same time, light and

temperature have effect on the fruit maturation (Uzun,

2007), VOCs are markers of state of health of the

plant (Martinelli et al., 2015), and the levels of plant

bioregulator (Rademacher, 2015) have effect on the

ﬂowering, fruit formation, ripening, fruit drop, defo-

liation, etc.

In order to extract the information about the state

of health of the plants and if they are suffering a lack

of water or nutrients, several sensors have to be inte-

Internet of Trees: A Vision for Advanced Monitoring of Crops

183

grate into the sensor nodes. Fundamentally, three kind

of sensors should be integrated: (i) Physical sensors to

measure temperature and radiance, (ii) Chemical sen-

sors to estimate VOCs, gases and vapors, (iii) Elec-

tro-chemical sensors to evaluate the levels of bio-reg-

ulators.

Physical sensors enable to know the surrounding

condition of the crop environment. Light intensity

and temperature are the main physical quantity that

should be measured since they are correlated with

several plant conditions and fruit maturation. Com-

mercial available physical sensors reach level of per-

formance that satisﬁes the need of the application.

Different consideration have to be done for chemi-

cal gas sensors and electro-chemical sensors. Chemi-

cal gas sensors have to perform two different tasks:

(i) quantify gas, vapors and VOCs, (ii) discrimi-

nate the different VOCs; these tasks require differ-

ent sensor approaches. In volatile quantiﬁcation,

selective sensors are more appropriate, whereas, in

volatile discrimination an approach based on elec-

tronic nose (Gardner and Bartlett, 1994) is more ef-

ﬁcient. In both cases, the research in low power, ad-

justable selectivity, and stability are still going for-

ward. Among the different kind of chemical sensors,

polymeric based (Bai and Shi, 2007) and metal ox-

ides (Polese et al., 2017; Polese et al., 2015; Zhu and

Zeng, 2017; Sun et al., 2012) show interesting char-

acteristics.

On the other hand, electro-chemical sensors need

to be functionalised to detect the appropriate bio-

regulator, this generally needs the interaction with

chemist or biologist to optimize their structures. Ex-

amples of this kind of sensors can be ﬁnd else-

where (Khater et al., 2017; Maiolo et al., 2016).

4 NETWORK ARCHITECTURE

WSNs for precise agriculture need to cover large cul-

tivated area, work for long time without maintenance,

and to be re-conﬁgurable under node failure or node

integration. We found that the Thread network pro-

tocol (https://www.threadgroup.org), and in particu-

lar OpenThread, the Google open-source implemen-

tation of it (https://openthread.io/) satisfy these re-

quirements ant it has be chosen in order to have a

re-conﬁgurable network that can easily prevent node

failures and work under stringent energy constraints.

A traditional Wireless multi-hop structure will be

employed, with a small number of border routers con-

nected to the Internet, and a dense network of nodes

equipped as described in the previous section. This

solution is adequate for crops that need to accommo-

Figure 2: Example of WSN structure in olive orchard. In

the ﬁgure, the different roles carried out from the nodes are

highlighted.

date hundreds of nodes and a limited (less than 32)

number of routers. For bigger crops, a solution based

on Contiki and RPL ( IPv6 Routing Protocol for Low-

Power and Lossy Networks) might be more appropri-

ate (Ellmer, 2017).

We will develop adaptive algorithms to dynami-

cally adapt the sensor report frequency depending on

the energy requirements and the measurement vari-

ability in that space and time frame.

Location Mapping

After the installation phase, a GPS equipped de-

vice can map the position of the sensors, using a

combination of anchoring during the pairing pro-

cess and RSSIs-based multilateration for error correc-

tion (Hightower and Borriello, 2001).

5 DATA VISUALIZATION

DASHBOARD

To build a dashboard, we will investigate the use of

Google Data Studio (Snipes, 2018), as well as open-

source solutions like chartist.js (Kunz, 2014) or can-

dela, using the Resonant data and analytics platform.

We aim at following the state-of-the-art of dashboard

design best practices (Elias and Bezerianos, 2011).

The main interface of the dashboard will show a

map of the crop, with an estimation of the health of

the crop. By clicking on a region in the map, an his-

toric trace of the health of that area will be shown.

The health parameter of an area is not the raw

sensor data, but rather a time and spatial interpolated

summary of the neighbouring nodes status, consider-

ing each sensor thresholds in an adequate time win-

WSN4PA 2020 - Special Session on Wireless Sensor Networks for Precise Agriculture

184

dow. This is obtained using health rules, that are a set

of conditions an area is supposed to satisfy. Health

rules can be time-dependent (e.g. different during the

night), and be as simple as independent thresholds for

each sensor, but also encompass complex joint rules

(e.g. humidity thresholds can depend on luminance

values). Health parameters (low and high threshold,

time and spatial tolerance) for each sensor can be

manually set in a conﬁguration section of the dash-

board, or downloaded from an online database.

The user will be also be able to visualise the raw

measurements from the sensors in this section of the

dashboard. Advances health rules warning can be set

up using modeling and predictive tools, as explained

in the next section.

An example of a dashboard for an olive orchard is

shown in Figure 3.

Figure 3: Example of data visualization dashboard for an

olive orchard, (design elements from freepik.com).

6 MODELING AND PREDICTIVE

TOOLS

We aim to build a database of health parameters and

health warning rules, that depend on the geographic

position of the crop and the type of cultivar. This will

be obtained with an extensive analysis of the litera-

ture in the agriculture ﬁeld. Moreover, farmers will

be able to contribute to this database by inputting their

conﬁgurations and their rules: the challenge here is to

convert the farmer experience to algorithmic rules, to

be able of learning from the agronomy community.

A challenging goal of this project is to be able

to assist the planning of crop irrigation, fertilization

and treatment: we aim to build health models able to

early pinpoint the need of an intervention in an area,

reducing the size of the area treated and potentially

reducing the effect of the issue because of the model

predictive abilities (e.g. for olive orchard (Testi et al.,

2006; Moriana et al., 2003)).

7 CONCLUSIONS

In this paper, we introduced the main challenges that

arise when designing a Wireless Sensor Networks for

monitoring the health of a crop. In particular, we have

identiﬁed the most important physicals and chemicals

features that should be monitored to assess the health

of a crop and potentially predict future issues. We dis-

cussed which characteristics a network node should

have and the network protocols that should support.

Moreover, the use of an appropriate dashboard for

the data visualization can help the user to understand

the evolution of the state of health of the crop, and

provide them with a tool to investigate how to opti-

mize irrigation, fertilisation and the phytosanitary in-

terventions in order to maximize the production and

minimize the environmental side-effects. In the fu-

ture, these approaches could also be directly used by

farmers to improve agriculture worldwide.

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