Our living environment poses all kinds of obvious and perhaps less obvious hazards. As an example, Ragwort (Senecio jacobaea) and related species of the genus Senecio are the main source of toxic pyrollizidine alkaloids. The plants commonly grow in road verges, meadows and production fields and are equally toxic for humans and animals. Therefore, proper identification of such a hazardous object is vital. This paper describes Determinator DSS, a Decision Support System, that aims to cover problems of proper identification of a hazardous object, either in the field or in a lab, and in diverse domains. Determinator DSS is used in several practical applications for ensuring food and feed safety and supporting environmental issues. The paper highlights several software aspects and design features of the DSS that enable it use in diverse circumstances and for different users as a generic solutio

Janssen, S.J.C.

Raamsdonk, L.W.D., van

Uiterwijk, M.

Name not available

Determinator - a generic DSS for hazard identification of species or other physical subjects

Wageningen Yield 

International Environmental Modelling and Software Society (iEMSs) 
 2012 International Congress on Environmental Modelling and Software 
Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany 
R. Seppelt, A.A. Voinov, S. Lange,  D. Bankamp  (Eds.) 
http://www.iemss.org/society/index.php/iemss-2012-proceedings 
 
 
Determinator – A Generic DSS For Hazard 
Identification Of Species Or Other Physical 
Subjects 
 
Michel Uiterwijk1, Leo W.D. van Raamsdonk2, Sander J.C. Janssen1 
1Alterra, Institute for Environmental Science, Wageningen, The Netherlands 
2RIKILT, Institute for Food and Feed Safety, Wageningen, The Netherlands 
Michel.Uiterwijk@wur.nl 
  
 
Abstract: Our living environment poses all kinds of obvious and perhaps less 
obvious hazards. As an example, Ragwort (Senecio jacobaea) and related species 
of the genus Senecio are the main source of toxic pyrollizidine alkaloids. The plants 
commonly grow in road verges, meadows and production fields and are equally 
toxic for humans and animals. Therefore, proper identification of such a hazardous 
object is vital. This paper describes Determinator DSS, a Decision Support System, 
that aims to cover problems of proper identification of a hazardous object, either in 
the field or in a lab, and in diverse domains. Determinator DSS is used in several 
practical applications for ensuring food and feed safety and supporting 
environmental issues. The paper highlights several software aspects and design 
features of the DSS that enable it use in diverse circumstances and for different 
users as a generic solution. 
 
Keywords: Determinator, DSS, identification, crowd-sourcing, environment.   
 
 
1 INTRODUCTION 
 
Our living environment provides all sorts of hazards, which form putative risks for 
human beings or animals.  These hazards can be based on the presence or 
overpresence of either substances or animal or plant species. Tracking and tracing 
are intensively used to tackle these hazards, ultimately aimed at ensuring food 
safety and safety of the environment. In order to collect information on the 
occurrence and frequency of these hazards, Early Warning Systems, or Emerging 
Risk Management Systems are implemented (Marvin, et al., 2009). Most of these 
systems have a human centred approach, and are based on data provided by 
expert networks.  
 
A specific task that occurs in hazard analysis is the identification of the hazardous 
substance or species vis-à-vis other substances or species. In the situation of 
expert networks, the network itself approves the reliability of the identifications. 
However, in environmental projects this identification often has to be carried out by 
a user in the field, which is often not a highly trained expert without sufficient expert 
knowledge to distinguish between all the different substances or species into 
hazardous and non-hazardous. This imperfect knowledge might lead to miss, or 
ignore valuable observations. 
  
For example, Ragwort is an exotic plant in the Netherlands, leading to hepatic 
veno-occlusive disease (HVOD), necrosis of the liver and ultimately death. At lower 
doses they may lead to liver cirrhosis (EFSA 2011). Plant inspectors or other 
professionals are often out in the field, which should allow them to identify 
suspicious plants or animals by using a mobile version of a DSS.  Correct 
identification of Ragwort requires some expert knowledge as it resembles other 
native plants found in Europe. Also, insect plagues are becoming more and more 
frequent in the Netherlands, also in trees, with the ‘Oak Processionary’, a caterpillar 
M. Uiterwijk et al. / Determinator – A Generic DSS For Hazard Identification… 
(Thaumetopoea processionea), whose poisonous setae (hairs) cause skin irritation 
and asthma. For these trees it is important to timely identify the infected oaks to 
inform hikers and others of the danger of the caterpillars. 
  
In these examples, identification of the frequency and occurrence of a hazard can 
be implemented by means of crowd-sourcing, thus getting as many people as 
possible for doing (correct) observations. There are a lot of observation networks 
with experts, such as for the Insect Pests Monitoring program in the Netherlands 
[Moraal et al, 2011]. To make this observation network larger and include non-
experts as well, expert knowledge should be disclosed to guarantee the quality of 
observations, which can be supported by the application of (mobile) DSSs.  
 
This paper describes an expert system for identification of hazards, the 
Determinator Decision Support System. Determinator DSS is argued to be a 
generic tool for hazard identification, and this is demonstrated in this paper by 
explaining IT features of the Determinator DSS, that aid its generic nature. 
Furthermore, the framework for hazard identification as conceptualized for 
Determinator DSS and the methods used in developing Determinator DSS are 
discussed and the value of a DSS in the framework of crowd-sourcing is presented. 
 
 
2 DEVELOPMENT OF THE DETERMINATOR DSS PLATFORM 
 
2.1 Datamodel 
 
The approach of Determinator is that all its specific applications share a generic 
datamodel structure. This datamodel structure contains the following elements: 
Targets, Features, FeatureGroups, TargetMatches, Labels and TreeItems. 
 
Targets are the concrete entities (objects), such as plants or animals. Targets can 
also be categorized by means of adding Labels to it, such as a family name or 
region(s) of occurrence.  
 
Features are the visible entity characteristics (like plant height or leaf shape) of a 
target, which can be organised in Feature Groups. Determinator also allows two 
different types of features: Continuous and Discrete. Discrete features are 
described in three states (like none/few/many), whereas continuous features have a 
numerical value for minimum and maximum. Every value between these minimum 
and maximum values can be used to describe the target. 
 
Decision rules are described in TargetMatches and contain the mapping of Targets 
and Features, by specifying valid feature states for each target. The decision rules 
in TargetMatches allow to use the Booleans AND and OR, e.g. IF a AND b THEN p 
OR q. This rule exemplifies the situations that feature states “a” and “b” both apply 
to either target p or target q. 
 
Finally, TreeItems contain the representation of a determinate decision tree, where 
each node can link to either a Target or another node. Basically, every lemma in a 
tree is based on the simple rule IF m THEN p ELSE q. The (combined) feature 
state “m” can combine more than one simple state, e.g. “a” and “b”.  
 
2.2 Tools 
 
Tools that were developed for the Determinator DSS platform are a Player tool for 
identification by the end user and a Developer tool for the modelling expert.  
 
The Player tool is intended for visual on-the-spot identification by the end user and 
its functionality can be divided into two categories: explorative and systematic 
identification.  
 
M. Uiterwijk et al. / Determinator – A Generic DSS For Hazard Identification… 
Explorative identification is possible through the following options in the menu 
(Figure 1, left screen): Browse, Filter, Select, Search, Overview and Compare. 
 
Browse (Figure 1, middle screen) lists all the targets in the datamodel and shows 
description, labels and one or more images of each target. Keyboard shortcuts are 
available for quick browsing. The Filter option is used to list all targets that have a 
specific feature value, like showing plants that are flowering in a specific month. 
Select does the same as Filter but filtering is now done with labels instead of 
feature values. The Search option provides text-based filtering in name and 
description fields and Overview shows all images of all targets in one window. 
 
Compare functionality can be used to compare two targets by feature, by showing 
all feature values and providing them with a match colour: green means a full 
match, red means no match and yellow means a partial match on that feature. 
A comparison table of Ragwort and Oxford Ragwort is shown (Figure 1, right 
screen), where can be seen that the feature of incisions has no match (red), so we 
can distinguish these two targets by this feature alone (feature value is double vs. 
simple). Partial matches (yellow) can be seen for flowering month, plant height and 
leaf location. The rest of the displayed features indicate a full match (green). 
 
            
Figure 1 - Player screens for Menu, Browse and Compare 
 
Systematic identification is done through options Match or Tree in the menu. 
 
Match functionality first asks the user to select the feature groups of interest. All 
features in those selected groups are given as questions for the user to answer, by 
selecting one of the options at the bottom of the window (Figure 2, left screen). In 
case no definitive choice can be made by the user, there is a N/A option. Answering 
questions gives a matching percentage for each target. During Match identification, 
an indicator is shown in the screen header how many targets are still full matches 
(100% Targets). The user can click the Results button to get a complete list of 
targets and their matching percentage (Figure 2, middle screen). 
 
       
Figure 2 - Player screens for Match Questions, List and Result 
M. Uiterwijk et al. / Determinator – A Generic DSS For Hazard Identification… 
    
      
Finally, Tree is walking along a determinate decision tree (of TreeItems) by 
choosing one out of two possible options, resulting eventually in a target (Figure 2, 
right screen). The user can also navigate back one step or directly to the root of the 
Tree to start over (Figure 3). 
 
       
         Figure 3 - Player screens for Tree (Nodes 1, 2 and 7) 
 
The Developer tool can be used to create new or edit existing datamodels and all 
functionality is presented in different tabs (Figure 4). 
  
 
Figure 3 - Developer screen showing general information of a datamodel 
 
The strength of the Developer tool is not only the ease of creating a new 
datamodel, but also the analysis and validation functionality. Target Analysis 
(Figure 5) provides a table displaying overlap between targets. A cell with a green 
colour indicates that the two targets have at least 1 differentiating feature and can 
be distinguished by this feature. 
 
M. Uiterwijk et al. / Determinator – A Generic DSS For Hazard Identification… 
 
Figure 4 - Developer screen showing Target Analysis 
 
It is vital that a datamodel is adequate and effective. For more information about 
these issues, see [Raamsdonk et al (in press)]. 
  
Both tools are built in Microsoft C#.NET and work in all versions of Windows, but 
the Player also works on a Windows Mobile device. All knowledge for a datamodel 
is stored in an encrypted xml and images are stored in a separate folder. 
 
Currently, a mobile web application is being built in HTML5/jQuery to support 
multiple mobile platforms (like iOS, Android and Windows Phone) by running in a 
HTML5 compatible web browser. Also there are plans to link identification with 
observation networks by sending identification results over the internet. To 
guarantee the quality of the observation it can be documented with photographs 
and GPS coordinates, because today’s smartphones have those capabilities. 
 
3 DISCUSSION 
 
The successful application of Determinator, or in more general sense of decision 
support systems, depends on the domain of application. As mentioned in the 
Introduction, the domains of environmental studies and of feed and food safety will 
be discussed. 
 
Projects for environmental monitoring studies, such as the Insect Pests Monitoring 
Network [Moraal et al., 2011], and monitoring programs for the effect of climate 
change on biodiversity (Battisti et al., 2010) are partly based on a reliable 
identification of the target species. Also in the framework of the relationship 
between environmental changes and health issues, a proper identification of the 
hazards is necessary. An example is the recent legally based monitoring of seeds 
of Ambrosia species in bird feed, which are able to cause severe allergy problems 
(Frick et al., 2011). These and other projects (see e.g. Key to Nature: Nimis and 
Lebbe, 2010) can benefit from support systems for validation of identification of the 
target, i.e. the species. At the same time, several of these projects use crowd-
sourcing as a process for gathering data on occurrence and frequency of putative 
hazards. In order to facilitate the identification, the following strategy could be 
implemented in a DSS for improving the quality of the uploaded data: Experts can 
report observations directly (without systematic identification) and non-experts have 
to follow systematic identification, either through a simple (decision tree) or detailed 
identification (match matrix) process. Observations by these non-experts, 
accompanied with proper documentation (photo and automatically generated GPS 
coordinates and description), can later be confirmed by an expert based on the 
uploaded data.  
 
In the domain of feed and food safety a range of monitoring, detection or early 
warning systems is implemented (Marvin et al., 2009). As an example, the 
M. Uiterwijk et al. / Determinator – A Generic DSS For Hazard Identification… 
European Union has started the Rapid Alert System for Food and Feed safety 
(RASFF), which is used in Europe for fast communication on deviations from EU 
legislation and to further improve food and feed safety (Kleter et al., 2009). In this 
system, hazards are identified by national competent authorities of EU member 
states, which are classified as experts. In cases of e.g. botanic impurities, 
notifications depend on both the level of expertise of the competent authority and 
on the presence of a monitoring system for botanic impurities (van Raamsdonk et 
al., 2009). The discrimination between rarely occurring hazardous botanic 
contaminations and legally applied food and feed ingredients is sometimes difficult 
to make. A DSS platform such as Determinator can aid to this identification 
process. The case of Ragwort is equally important for the domains of food and feed 
safety and for environmental monitoring. 
 
In general, entities that are defined as (physical) objects can be described in 
models for DSSs which are focusing on pictorial information. These descriptions 
can support the data which is generated by the process of crowd sourcing. The 
combination of a recognised hazard and data on frequency and occurrence (from 
crowd-sourcing) can provide useful information for the assessment of the 
accompanying risk. 
 
 
ACKNOWLEDGMENTS 
 
This work was partly funded by the Dutch Ministry of Economic Affairs, Agriculture 
and Innovation. 
 
 
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