=Paper=
{{Paper
|id=Vol-2761/HAICTA_2020_paper59
|storemode=property
|title=Designing an e-Business Agricultural Decision Support Company: The Case of Olivenia
|pdfUrl=https://ceur-ws.org/Vol-2761/HAICTA_2020_paper59.pdf
|volume=Vol-2761
|authors=Romanos Kalamatianos,Ioannis Karydis,Markos Avlonitis,Panagiotis Gratsanis
|dblpUrl=https://dblp.org/rec/conf/haicta/KalamatianosKAG20
}}
==Designing an e-Business Agricultural Decision Support Company: The Case of Olivenia==
https://ceur-ws.org/Vol-2761/HAICTA_2020_paper59.pdf
Designing an e-Business Agricultural Decision Support
Company: The Case of Olivenia
Romanos Kalamatianos1, Ioannis Karydis2, Markos Avlonitis3, Panagiotis
Gratsanis4
1
Department of Informatics, Ionian University, Greece; e-mail: rkalam@ionio.gr
2
Department of Informatics, Ionian University, Greece; e-mail: karydis@ionio.gr
3
Department of Informatics, Ionian University, Greece; e-mail: avlon@ionio.gr
4
Department of Informatics, Ionian University, Greece; e-mail: gratsp@gmail.com
Abstract. The primary sector has been receiving increasing attention both at
national and global level aiming at the quality of nutrition. 93% of worldwide
olive production comes from Spain, Italy and Greece that treat olive farming
not only from a solely economic point of view but as a way of living, too. Pests
and diseases of the olive tree can cause damage, if not addressed, reaching
levels of up to 80% of the production. Existing methods (biotech, growth
regulators, symbiotic bacteria destruction, pests’ technical sterilization, etc.) in
order to be efficient & effective require information/prediction on the
biological cycle of pests that is not currently available. Olivenia is a Smart
Agri application in order to help olive farmers manage their crops and to
effectively and efficiently tackle the diseases and pests of olive trees by
providing detailed instructions on the least harmful possible required
preventive and therapeutic actions in order to maintain quality and quantity of
crop. ICT methods allow not only for easy pertinent data collection and fast
prediction activities on the threats of the crop but also for knowledge mining
and extraction from collected data.
Keywords: Decision Support System; SaaS; Olive-fruit fly; Electronic smart-
traps.
1 Introduction
Nowadays, the need for support and development of primary sector receives
increasing attention both at national and global level as modern studies attribute the
quality of nutrition. The development of increased quantity and healthier foods by
use of biological methods or the use of cultivation practices with less chemicals and
fertilizers are a requirement in societies and markets. Still, this very requirement of
active reduction of chemicals and fertilizers creates problems in farming.
For Spain, Italy and Greece, one of the key crops of the primary sector is based on
olive trees and their products but it also represents the continuation of a tradition and
a way of living. Accordingly, olive related holdings should be not assessed only from
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a purely economic point of view as in numerous cases are also associated with many
other non-economic factors that make olive tree production sector a way of living.
Olive oil is one of the basic ingredients of the famous Mediterranean diet and has
enormous benefits for health. According to numerous studies the consumption of
olive oil can control cholesterol (Mensink and Katan, 1989; Planell et al, 2014;
Visioli et al, 1995) and protect from heart disease (De la Torre et al., 2017; Estruch et
al., 2013; Keys et al., 1986). It is associated with reduced risk of diabetes (Martínez-
González et al., 2008; Lasa et al., 2014), reducing blood pressure (Galvão Cândido et
al., 2017) and inhibiting the development of some forms of cancers (Escrich et al.,
2011; Storniolo and Moreno, 2016; Smith-Warner et al., 2001; Trichopoulou et al.,
1995; Ayoub et al., 2017). Finally, olive oil can be a great ally for weight loss
(Esposito et al., 2011; McManus et al., 2001).
However, the management of olive-related diseases is still being currently
addressed with terms and practices of the past century, despite the plethora of
modern technologies for monitoring and early warning of possible outbreaks. In
particular, the pest olive-fruit fly (Bactrocera oleae) is the most serious
entomological enemy of olive and is nowadays prevalent in all the olive
cultivation/producing countries. The damage it can cause in the absence of control
measures, can reach up to 80% of production (Neuenschwander and Michelakis,
2009; Neuenschwander et al., 2009).
Depending on the region, olive-fruit fly displays two to five generations per year
(Donia et al., 1971; Economopoulos et al., 1982; Kapatos and Fletcher, 1984). It can
even be found during the winter in the olive groves as a fully developed pest in case
the climatic conditions are not prohibitive for its survival. Accordingly, in areas with
mild winters and when the olives-fruits are suitable for feeding the larvae of the pest,
it may be possible to have it present in all its biological stages.
1.1 Motivation and contribution
It is therefore clear that tackling the olive-fruit fly is a very complex problem. The
common method producers use, chemical applications, with total or partial coverage
may be completely ineffective if the application (in most cases spraying) is not in the
correct and optimal time and place. To tackle the pest, chemical control may be
applied preventively and/or therapeutically. Unlike the therapeutic approach, the
preventive, in order to be effective, is applied over large areas to address re-infection
scenarios so it is organized by organizations such as ministries of agriculture and
local farming directors. A prerequisite for the success of the bait method (ground or
air) is timely and meticulous implementation of the first spray that is decisive for the
further course of the pest control since it coincides with foundation generation of the
pest. It is therefore obvious that the urgent need for reliable monitoring & prediction
of the biological cycle of pests and diseases affecting the olive trees are of great
impact on their control. Advanced Information Communication Technologies (ICTs)
provide for management of crop-land, IoT sensory hardware, cooperating with
manually collected data from the field, as well as organisation and prediction of
pests’ and diseases’ activities.
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In order to address these requirements, this work proposes the design of an e-
Business Agricultural Decision Support (DS) company based on the experiences of
the “Olivenia” venture. Accordingly, the key contributions of this work can be
summarised as follows:
• Overview of current applications of ICT in agriculture and specifically on the
olive-tree sector,
• Proposal of a novel way to address the existing shortcomings,
• Identification of market opportunity, and
• Definition of revenue model.
2 The case of “Olivenia”
Olivenia, a name coined for a venture presented in the 2017 KATANA contest , 1
an EU Horizon2020 project that aimed at promoting ICT in the agrifood sector and at
funding new ideas. The proposal of Olivenia got the fourth position at the initial
2
sorting of ideas from a pool of approx. 600 ideas from all over Europe.
Olivenia is a Smart Agri application in order to help olive farmers to effectively
and efficiently tackle the diseases and pests of olive trees by providing detailed
instructions on the least harmful possible required preventive and therapeutic actions
in order to maintain quality and quantity of crop. The ICT methods proposed did not
only allow for easy pertinent data collection by the farmers and fast prediction
activities on the threats of the crop but also for knowledge mining and extraction
from collected data that would otherwise be very hard to do manually and are
necessary for better crop management.
2.1 Cropland management
Initially, users/farmers determine the geo-characteristics of their farmland to
allow the system to use the geo-informational graphical features for pests’ and
diseases’ predictions. This is necessary in order to define the main entity of land
describing the geographic area of a farmer’s/user’s olive grove as well as the
mounting points of pests’ traps (Fig. 1).
1 http://katanaproject.eu/
2 https://www.youtube.com/watch?v=wWY-RVm91q8
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Fig. 1. Cropland management interface during design and development.
2.2 Information collection from traps
Olivenia includes a mobile application (app) for both android & iOS operating
systems that is designed to allow users / farmers perform a semi-automated
information collection from traps. The app uses the GPS position sensor of the
smartphone to determine the trap being tested and subsequently raises predefined
questions, to the user/farmer, regarding the status of the trap. The questions are
related to the collected olive-fruit flies. Moreover, the app supports taking pictures of
collected pests for further training of automatic inference of pests’ characteristics
(Kalamatianos et al., 2018). The collected data are automatically submitted to
Olivenia’s servers using internet access, when available.
Olivenia uses fully automated smart-traps as a way of collecting, digitising and
transmitting the necessary data at a processing center based on the Electronic Olive
Fruit (E.O.F.) fly trap, i.e. an electronic version of the classical McPhail trap
(Kalamatianos et al., 2018). This trap is self-sustainable as far as electric power is
concerned by use of batteries and charging solar panels and features a multitude of
meteorological sensors, a camera and networking capabilities. An array of such traps
/ sensors, placed in an olive grove, forms an ad-hoc network that makes propagation
of information easier, almost not requiring physical presence, given internet access at
any point of the ad-hoc network and minimal required maintenance for the cleaning
and liquids’ refresh of the McPhail part of the E.O.F. fly trap.
2.3 Exacerbation & treatment recommendations predictions
The user/farmer has the opportunity to receive outbreak predictions and treatment
recommendations for the olive-fruit fly. The predictions are customized to the
characteristics of the specific farmland. Outbreak predictions and treatment
proposals, aside from being available at request, are also sent to the mobile device of
the user/farmer for timely information dissemination. Moreover, if a prediction for
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actions to farmers, whose results are likely to affect other farmers (e.g. due to
physical proximity of croplands) is made, the service also notifies all interested
parties.
It is thus evident that Olivenia tackles the knowledge creation & information
dissemination as a continuous process not relying or necessary requiring the
interested parties’ action to provide timely results. All in all, Olivenia provides olive
farmers with: (a) virtual cropland management using graphical interface, (b)
smartphone applications allowing hassle-free collection of information from traps set
on olive trees, (c) smart-traps minimizing the required manual activity performed at
the traps using arrays of sensors and wireless connectivity, (d) information on the
actions required in order to tackle the pests, and (e) pest outbreak predictions and
necessary treatment recommendations.
2.4 Monetization scheme
The business model followed by Olivenia is based on the subscription for the
olive-grove cataloging and prediction services. Initial plans are to include only the
temporal dimension of the subscription (yearly & perpetual) while future plans
include the olive-grove’s size dimension as well. Given a subscription’s expiration,
the user’s account will retain all information, but no changes will be possible nor any
prediction services will be rendered.
The minimum duration of a package (5 years) was selected based on the
requirement of the algorithms for training to be able to provide customized results in
outbreak prediction and on the cultivation cycle’s special characteristics. The
“perpetual” package’s duration was based on the requirement for increased sales-
driven services especially when for these the key expenditure on ICT infrastructure
resources is almost constant. Accordingly, Olivenia’s perpetual levels of “temporal”
subscription is valid for as long as Olivenia exists as a company, and Olivenia is run
by the same company, and Olivenia offers to its customers the same SaaS product the
“forever”/“perpetual” subscription refers to.
The “perpetual” package is also divided into two differentiated categories based
on the inclusion of smart-traps. Accordingly, the “perpetual” package does not
include leased smart-traps, while the “perpetual+” does indeed. The extraneous cost
of including the appropriate volume of smart-traps in the “perpetual+” package is to
be leveraged by increased cost of the package and minimization of the manual labour
required by the user in providing for the prediction algorithms. The simple
“perpetual” package is expected to lead to minimized use of the prediction services
in some cases by the users given the lack of their input and thus minimization of the
increase of the yield of the grove.
2.5 Effects of using Olivenia
Given the damage the olive-fruit fly can cause, in the complete absence of control
measures, reaching of up to 80% of production (Neuenschwander and Michelakis,
2009; Neuenschwander et al., 2009), it’s safe to assume that use of Olivenia is
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expected to increase the effective production in the range 10% to 20% (Fig. 2), by
solely performing the usual suppression of the olive-fruit fly but in a more effective
and efficient way. Using the “perpetual” subscription, farmers with olive groves as
small as 250 olive trees break-even the cost of the subscription in 5 years and
subsequently have only net profit from using Olivenia (Fig. 3).
Fig. 2. Farmers’ olive production increase with the measures proposed by Olivenia for varying
sizes of olive groves.
Fig. 3. Farmers’ net gain with the “perpetual” package for varying size of olive groves and
years of subscription.
2.6 Market opportunity
Over 750 million olive trees are cultivated worldwide, 95% of which are in the
Mediterranean region. Most of global production comes from Southern Europe,
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North Africa & the Near East. Of the European production, 93% comes from Spain,
Italy & Greece. Spain has the highest number of olive trees and is nowadays the
world’s leading olive-fruit & olive-oil producer and exporter with 2.1 million
hectares of olive groves.
Italy is the second EU producer with two-thirds of its production leading to extra-
virgin oil with 37 Protected Origin Appellations widespread on its territory. In Italy
there are about 6180 olive oil mills & the overall amount of processed olives in
2006/2007 was about 3.500.000 tons with a production of about 600.000 tons of oil.
Greece devotes 60% of its cultivated land to olive growing. It is the world’s top
producer of black olives and has more varieties of olives than any other country.
Greece holds third place in world’s olive production with more than 132 million
trees, which produce approximately 350,000 tons of olive oil annually, of which 82%
is extra-virgin.
Acknowledgments. The financial support of the European Union and national funds
of Greece and Albania under the Interreg IPA II Cross-border Cooperation
Programme “Greece – Albania 2014 – 2020”, for the project “Olive Culture" is
gratefully acknowledged.
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