Event Detection over Continuous Data Streamfor the Sustainable Growth in Agriculture

Context

Janagan Sivagnanasundaram1(&), Athula Ginige1,and Jeevani Goonetillake2

1 Western Sydney University, Sydney, Australia{j.sivagnanasundaram,A.Ginige}@westernsydney.edu.au

2 University of Colombo, Colombo, Sri Lankajsg@ucsc.cmb.ac.lk

Abstract. The coordination failure is a concept that explains the failure ofpeople to coordinate and act on a real-world problem properly. The failure ofcoordination among human communities can lead to social problems in manydomains including in transportation, health care, disaster management, agri-culture etc. and ultimately affects the sustainable growth of a country as a whole.The recent advancements in Information and Communication Technologies(ICT) have introduced a new trend called Collaborative Consumption - the peer-to-peer based coordination and sharing of information through online byhuman communities has been expected to solve these social problems identifiedin the above domains. The tremendous adaptation of people towards ICTsultimately resulted in huge, fast-moving and heterogeneous data contributed bypeople in a collaborative manner. In here, most of these data describe real-timeevents associated with the people based on their context. The way of coordi-nating user communities to contribute data, detecting events from it and effec-tive delivery of required information to needful parties would be a possiblesolution to overcome the coordination problem. In this paper, the result of asystematic literature review is performed to understand the current state of theevent detection methods used in information systems. Furthermore, we haveproposed a user centered mobile based information system that assists thedetection of pest outbreak events in agriculture domain for an effective andtimely delivery of actionable information to farmers.

Keywords: Event detection � Coordination failure � Agriculture sustainabilityPest management

1 Introduction

Information sharing is a prerequisite for a country’s development. Without theexchange of information, no innovation would be able to spread and will results ininformation gaps. The unavailability of access for human communities to the needfulinformation based on a problem will lead to many problems and need to be overcomeas soon as possible. According to Maslow’s hierarchy of needs theory as shown in

© Springer International Publishing AG, part of Springer Nature 2018O. Gervasi et al. (Eds.): ICCSA 2018, LNCS 10960, pp. 575–588, 2018.https://doi.org/10.1007/978-3-319-95162-1_39

Fig. 1, a human’s basic needs for own growth can be divided into following levels;physiological, safety, belonging, self-esteem, and self-actualization [1]. Of these, thelowest level “physiological” is the only one that can be fulfilled by as an individual.But for the levels above it, the individuals need to work as a community to achievetheses. Once the communities are formed, there is coordination and exchange ofinformation tends to happen among the individuals in the community. The unavail-ability of coordination among human communities and delivery of actionable infor-mation at the right time to needful parties will lead to many problems such asdifficulties in delivering required information to people when they need it most, guidingpeople to take precautionary actions in the right time, making effective decisions etc.[2]. Thus, a proper coordination among human communities is a necessity for effectivefulfillment of human needs.

Recent advancement in ICTs, Internet of Things (IoT) and the internet has had anenormous effect on many facets of our day to day life. The portable devices such assmartphones have become an increasingly important part of our lives and capable ofsupplementing our daily activities. In addition, the application of smartphones hasprovided a new way of making tentative decisions much easily than before, which leadsto the improved social attachment and social relationships with other people of humancommunities. According to a report by Statista [3], for 2019 the total number mobilephone users are expected to pass 5 billion worldwide. Due to these tremendousadaptation and contribution of human communities towards ICTs ultimately resulted ina new trend called Collaborative Consumption - the peer-to-peer based coordinationand sharing of information through online by human communities and created massive,fast-moving, and heterogeneous data. The tremendous availability of these data isgenerated from various sources including continuous usage of disparate sensors, socialmedia applications, satellite, IoT etc. The smartphone devices have enabled the dis-tribution and sharing of information to be performed nearly at our fingertips. Accordingto Kaplan and Haenlein [4], many people adapted towards using smartphones becauseof the increasing ubiquity, widespread application and the user empowerment providedby social media applications. Thus, the smartphone has the potential to become a toolto solve many societal problems.

Fig. 1. Maslow’s hierarchy of needs [1]

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The study in [4] further states that, social media allows it users to create andexchange contents and such information could be used for various operations such as toexplore what’s happening around us, to take precautionary actions, to communicatewith others etc. A new event update triggered by an individual is also considered as afirst alert about something going on. In this case, social media applications could beused as an emergency notification tool. This widespread collaboration among humancommunities enabled the creation of a new paradigm called “crowd sensing”: a col-laboration network which targets mobile users to form a network and allow them toshare local knowledge acquired by their mind and sensor-enhanced devices. In here, auser is considered as a sensor and each tweet generated by the user is considered assensory information. Hence, these sensors are called together as social sensors.

The large set of these data contributed by human communities contains informationabout various events happening around the world. In here an event can be defined as asituation initiated by an individual, followed by others of a human community within aspecific time interval. The detection of such events from human contributed data andsharing it among needful parties will help the human communities to act on a problemprecisely. The topic called event detection has been an active research topic for avariety of domains and serves as fundamental for many real-world applications relatedto predictions, monitoring, diagnosis etc. In addition, event detection can be explainedas a problem of detecting a target event has occurred or not, from a given data streamand can be considered as multi-stream event detection when there are multiple datastreams [5]. It is known that more than 80% of the user-contributed data associatedwith events are primarily complex, streamed, unstructured and without contextualinformation [6]. For an efficient implementation of this, the system should be lesscomplex, simple in design and must adapt to the changing conditions of the sur-rounding environment to capture contextual information [7].

The agriculture is the fundamental source of food for all the countries in the world.Due to the heavy pressure of population growth, the demand for food is continuouslyincreasing at a faster rate. The mixed effect of green revolution and human populationhas made the need for food production to double over the past years. If agriculture failsto satisfy the rising requirement for food products, it is found to affect a country’seconomic growth. The productivity and availability of crops grown for human con-sumption can be affected due to many reasons and one of the important reasons is dueto the pest attacks leads to the reduction of crop performance and low yield production[8]. A thorough study in [9] revealed that due to the unavailability of targeted deliverymethods, farmers and other parties including agro-chemical companies, agricultureauthorities and policy makers of the agriculture domain is not published with therequired information such as pests or disease outbreaks and possible remedies, whenthey need it most. The study further states that the lack of proper framework, formanaging crop losses and pesticide use had ultimately contributed to difficulties inproducing enough food for future dietary needs.

Therefore, in this paper, a thorough review is presented on the state of the eventdetection techniques used in other applications and a multi-stream event detectionmodel is proposed in an attempt to detect real-time pest/disease related events to solvethe identified crop losses problem in the agriculture context. The multi-stream eventdetection model is suitable here because the information we are considering is sourced

Event Detection over Continuous Data Stream for the Sustainable Growth 577

from diverse data sources such as user-contributed data (crowd sensing), weather data,images and spatial-temporal information. The rest of the paper is structured as follows;Sect. 2 of the paper presents the motivation behind this work with possible challenges.Section 3 presents the related works on event detection techniques and similar appli-cations. The proposed solution for the identified problem is discussed in Sect. 4. Theresults and, the conclusion work are presented in Sects. 5 and 6 respectively.

2 Motivation and Challenges

The motivation behind this research was based on a case study from Sri Lanka. A re-search team consists of researchers from different universities initiated a project called“Digital Knowledge Ecosystem for Sustainable Agriculture Production” to explore apossible solution to address a problem related to crop cultivation in Sri Lanka [10]. Theproblem identified in Sri Lanka was, the farmers growing the same crops as otherswithout knowing what others are growing and resulted in overproduction and under-production of vegetables [11, 12]. The research team has explored a mobile solution toaddress this issue where farmers get published static information about vegetables,sowing and harvesting methods, and real-time dynamic information including currentproduction levels of crops and prices. With this real time information, farmers, agri-culture departments, agriculture agencies and agro-chemical companies can takeoptimal decisions to effectively plan their tasks.

In worldwide, approximately 35% of crop production is lost due to the pests anddisease attacks. In the meantime, as stated previously, the agriculture has to satisfy theglobal level requirement for food, and other bio-based commodities as well. Accordingto FAO [13], food security is the initial step towards building a greater economy of acountry and it exists when all the human communities have the physical and eco-nomical access to foods at all the times to satisfy their dietary needs for a healthy life.In order to satisfy the increasing food demands, these crops must be protected from pestor disease attacks [14]. Thus, helping farmers to safeguard their crops will be a vitalfactor in promoting food security. The recent advancement in ICTs has alleviated manysocietal problems and undoubtedly holds many of the keys to ensure food securityglobally. The best combination of these available technologies has to be used to makeagriculture more productive and profitable to satisfy the standards of human health.A thorough analysis of information systems developed for agriculture domain revealedthat [15], most of the applications were widely used as farm activity journals, farmmanagement tools and communication platforms. None of them were specificallydesigned and developed for managing crop losses.

In this research work, pest management is used as an exemplar scenario formanaging it through a multi-stream event detection technique with the use of aninformation system, as crop losses are something which has got a significant negativeimpact on the food security. The development of a mobile-based information systemwould be a viable solution for this problem because of the wide-spread applicability ofsmartphones among human communities and even farmers in rural areas, withoutworrying about their low-level education, literacy, and language, used to carry theirmobile phone to the farm, and use it in their daily activities.

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The advancement in ICTs effectively exploded diverse types of data and theamount of available data. These diverse types of data include text, images, audio,video, sensory information, spatial-temporal information etc. Thus, the heterogeneousway of monitoring real-world events is one of the challenges to capture diverseproperties at a spatial-temporal point. In here, to detect disease or pest outbreak eventswe are considered using crowd sourced data including images contributed by farmers,atmosphere weather information from weather stations, and spatial-temporal informa-tion via the access to farmers location using context aware computing. Another chal-lenge associated with the proposed system for pest or disease event detection is Design.Due to the lack of knowledge and literacy rate among rural farming communities, theproposed system has to be simple in design with necessary features that are desirableand effective without a need for extra efforts.

3 Background

3.1 Definitions

The unavailability of the proper definition for event detection initiates several problemssince this topic is complex and many of the aspects of this technique are not clear. Overthe years there have been number efforts by scholars to give a concrete definition forthis concept but, most of the definitions are lack of satisfying the properties of an eventsuch as context, an action which triggered it etc. In here, we will present such defi-nitions proposed by other scholars in the literature for event detection and propose anew definition that unifies the common traits observed in the literature. According to awork in [16], an event is defined as “something that happens at specific place within atime slot with some consequences”. The consequences in this regard are people getmotivated about a particular event and action made by them via sharing and messagingto others promptly with the use of wide range of social media applications.

Weng and Lee [17] state that, an event is “a stream of user contents having similartopic and words posted within a time interval”. McMin et al. [18] propose an event as“a significant occurrence that happens within a specific time and place”. Aggarwal andSubbian [19] state an event as “something that happens at a specific place and time andinterest to the mass media”. In here, an event takes place at a particular location andtime will automatically stimulate people around that place to post contents about it. Asa conclusion, all of these definitions have some properties in common and definedbased on micro-blog data generated in social media applications.

Proposed DefinitionBased on the observations of common traits in the above definitions and a focus onuser-contributed data we define event detection as: Given a stream of actions An byusers Um of the collaboration network N, detection of real-world event e of a contextC along with the spatial information Le and temporal information Te.

Let consider each of the words used in the above definition:

a. action (a): In a crowdsourcing network N, an action a is a post of a new content bya user u.

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b. collaboration network (N): A network of users Um formed together to perform aseries of actions An to contribute some needful knowledge.

c. user (u): An individual who can perform an action a in a crowdsourcing network N.d. event (e): Describing a situation initiated by a user u as an action a, followed by

others in a human community within a specified time interval Te (tstart to tend) andlocation Le.

e. context (C): The description of an environment where the event e happened.f. spatial-temporal information (Le & Te): Having spatial-temporal qualities or

space-time properties of an event e.

3.2 Event Detection Techniques – State of the Art

In recent years, many researchers tried to detect events such as trending news stories,life threating situations, traffic reports, epidemic disease outbreaks etc. by exploring thedata contributed by human communities. According to [20], the most of the detectedevents can be categorized into three distinct groups as stated in Table 1.

The main challenge associated with event detection is to select appropriate toolsand techniques. A survey of event detection methodologies used in the above identifiedgroups is discussed in Table 2.

3.3 Problems Identified

According to the table above, the majority of the research works have been conductedrelated to event detection, organization, and prediction in other domains such astransportation, disaster management etc. and none of them were specifically designedfor the agricultural domain. In addition, all the identified information systems werebased on social media applications and micro-blogs as the source of information. Thesocial media applications like Twitter, Facebook etc. generally have low signal to noiseratio [28, 29]. The people all over the world generate tweets more than several milliontimes per day, resulting in the noisy and informal presentation of 140-characters. Theinformation in a tweet is generally subjective and confined to text format. To make adecision precisely and effectively, human communities must understand the data theyget hold is a fact or an opinion. The appropriate way to differentiate facts and opinionsis a problem in micro-blogs based application such as Facebook, Twitter etc. Basically,

Table 1. Event categories

Eventgroup

Event type Examples

A Emerging events Live & Trending news etc.B An event due to natural or human

interventionEarthquake, Flood, Fire etc.

C Public feedback events Electoral polls, Traffic control, Diseasemanagement etc.

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the accuracy of detected events relies upon the quality of data. Thus, micro-blogscreated in social media applications can’t be used as sources to detect the events, as ithas some negative concerns about the quality of data.

Over the years, multimedia data is the largest data sources contributed by humancommunities. Every second, thousands of images and videos are uploaded by peoplevia computers, smartphones etc. Some of these multimedia data have geo-taggedinformation. One of the important features of this geo-tagged information is itsspatial-temporal information. When an interesting event happens, people capture thoseevents as images, which allow the discovery of events promptly, by visualizing thephoto. In [30], Flickr data with geo-tagged information has been used to understand theusers’ interest. Organizing these multimedia data for further analysis is a difficult taskdue to its varied levels of meanings about its concepts. Over the years, there is a huge

Table 2. Taxonomy of event detection applications

Eventidentified

Detection technique Type of datastream

Features used Applicationcategory

References

Breakingnews

Naïve BasedClassifier & OnlineClustering

Microblogs Terms in thetext, Hashtags

A [21]

Trendingnews

Discrete WaveletAnalysis & GraphPartitioning

Microblogs Individualwords

A [17]

Earthquake SVM Microblogs No of words,No ofkeywords

B [22]

Disaster Named EntityRecognition, Filtering

Microblogs No of similarwords

B [23]

Traffic Keyword analysis,Abbreviation analysis

Microblogs Keywordexistence

C [24]

Flu trend Keyword analysis,Group averageclustering

Microblogs Keywordexistence

C [25]

Epidemicoutbreak

Eventshop,Classification andsegmentation ofImages

Microblogs Average oftweets/dayImage pixel(intensityvalue)

C [26]

Flood Eventshop,Classification andsegmentation ofImages

Microblogs Image pixel(intensityvalue)

C [26]

Asthmarisk

Eventshop,segmentation ofImages, interpolationof fixed sensor values

Microblogs,sensorvalues,photos

Image pixel(intensityvalue)

C [27]

Event Detection over Continuous Data Stream for the Sustainable Growth 581

effort made to carry out an analysis using automatic semantic classification on multi-media to classify it into a set of predefined concepts [31]. Even though, having mul-timedia data over text data is beneficial, utilizing concepts for situation detection basedon it will be a challenging task.

The previous works establish a basis for improving the quality of data generated byhuman communities and ultimately contributes to detection of events. Incorporation ofmultimedia data instead of textual data can improve the quality of the data stream up tosome certain level but, the accuracy wise there is a negative impact due to the variedlevel of understandings. According to [32], most of the agricultural projects failbecause when systems were designed for farmers, characteristics such as context,culture and socio-economic were not considered which leads to not being able todevelop and recommend appropriate technologies that are compatible with the targetgroup. With the advancements in context aware computing, crowd sensing, andweather models, it is possible to allow farmers to share the observations on their fieldwith the descriptors along with spatial-temporal, weather information. The use of suchdiverse streams of data and matching it with the knowledge base of diseases/pests,personalized information or alerts could be provided to farmers and other stakeholders.This will help all the related parties of the agriculture domain to act on a problemprecisely without further thinking.

4 Proposed Solution

In here, the solution has been given for the problem of managing pests or diseasesthrough a multi-stream event detection technique with the use of a mobile-basedinformation system. Nowadays, the smartphones are equipped with diverse types ofbuilt-in sensors such as positioning sensors, motion sensors, cameras and microphoneswhich make the smartphone a promising tool for assisting farmers. Our solution forprotecting crops from pests and diseases achieves early and real-time detection of pestand disease outbreaks through capturing farm-field observations as events through datacontributed by farmers via mobile phones. Then, by verifying observed events throughaggregating similar events generated by other farmers and correlating with weatherdata, prompt communication back to the farming community is achieved to confirm anoutbreak, including disease type and remedies. The major reason for the spread of pestsor disease is extreme weather changes. Thus, it is important to incorporate the weatherdata stream to validate the data contributed by farmers.

The proposed model lends itself to learn from initial observations and over time tochange the model to predict the most probable types of pest and disease outbreakevents to a predictive model. By collecting data verifying pests and disease outbreakson a farm, and weather data leading to those outbreaks, it is possible to establish abetter correlation between different types of disease/pest outbreaks and weather datapatterns leading up to such outbreaks. With such insights, we will be able to predict theonset of outbreaks based on weather data rather than waiting for it to happen and verifyit using aggregated farmer collected data. Being able to proactively predict rather thanreactively verify will give farmers more time to take necessary precautionary actions tofurther minimize crop losses due to pest and disease outbreaks.

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Converting the above-proposed solution to a practical system has many challengesthat we plan to address in this research. We have formulated the following set ofresearch questions to be investigated; (a) what is the optimal mobile user interface forfarmers to report an observed pest or disease event with as much information aspossible, taking into account their level of mobile literacy, native language and con-straints due to farm environment; (b) what is the best model to predict the probability ofan outbreak event based on weather data; (c) how best to identify the nature (pest ordisease or pathogen) and specific details of the outbreak to work out possible remedies;(d) what is an effective way of providing information about identified outbreak eventsand remedies to the farming community for them to take action to mitigate the croplosses.

In our solution, the farmers can register their farm and take a photo of their affectedcrops with the descriptors from a menu as shown in Figs. 2 and 3. We have structuredthe field observations as menu into 3 levels; type of attack, type of organism andseverity. Then these geo-tagged and time-stamped farmer observations can be aggre-gated based on location and time to find the intensity of observations at a given locationwithin a specified time window. If the intensity in a specific geographical area reaches athreshold value we can conclude that the area has a pest or disease outbreak. There isscientific knowledge relating to onset of different diseases and pest outbreaks duringdifferent weather conditions. We can thus get weather data from existing weatherstations and dynamically adjust the threshold values of aggregated farmer observationsto decide whether there is a valid outbreak in that geographical area.

Fig. 2. Registering farm Fig. 3. Event reportingscreen with 3 levels of menu

Fig. 4. Knowledgebase ofdisease information [11]

Event Detection over Continuous Data Stream for the Sustainable Growth 583

During the process of event reporting, the diverse data streams such as crowdsourced data stream, image data stream, and weather data stream will be captured. Inhere, the crowd sourced data stream will contain information such as the type of cropgrown, soil information, type of attack (pests/pathogens), severity of attack (using colorcodes) and spatial-temporal information; image data stream will contain the image ofthe affected crops, weather data stream will contain information related to temperature,humidity, soil moisture, soil temperature, soil temperature at 10 cm depth, rainfall etc.The validation of the reported incident will be performed through aggregating similarincidents reported from the same geographical area and through weather patterns. Oncevalidated, the reported event information is used to search the knowledge base of cropdisease information and communicated back to farmers via the same mobile applicationand deliver an alert notification to others to take precautionary actions. For this work,we will be using the same knowledge base of crop disease information developed forthe DKES project [10] as depicted in Fig. 4. Finally, the detected events will be pointedin a map scaled from country wise to suburb wise as shown in Figs. 5, 6 and 7 to giveas a summary of detected events.

To capture the weather information of the reported event, we have used the APIsprovided by openweathermap.org [33]. These APIs provided the access to the atmo-sphere weather information and soil information at global scale. In order to verify theaccuracy of the generated weather data from weather station, we deployed weathersensors to monitor weather changes remotely as shown in Fig. 8. Following parameterssuch as temperature, rainfall, pressure, wind speed and direction, humidity, soil tem-perature and soil humidity were observed in real time. After some period of continuousobservation, we have compared the data collected from weather station and remote

Fig. 5. Events belongs tocountry

Fig. 6. Events belongs tostates

Fig. 7. Events belongs tosuburbs

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weather sensors. Based on the comparison, we considered using the weather stationdata to capture weather patterns instead of deploying remote weather sensors. From ourresults, the weather station data shows high correlation with the result of remoteweather sensors, is available at global scale, and cost effective. The result of thecomparisons is discussed in Sect. 5.

5 Results

The comparison performed to verify the accuracy of weather station data (fromopenweathermap.org) and data from weather sensors reveals that there is a high cor-relation between these two. The comparison of various weather parameters betweenweather station data (red) and remote weather sensor data (blue) are shown in Figs. 9and 10.

Fig. 8. Deployed weather sensors

Fig. 9. Atmosphere temperature (Color figure online)

Event Detection over Continuous Data Stream for the Sustainable Growth 585

Based on the results, we have decided to incorporate the weather station data withthe proposed mobile based application. So, when a farmer reports an event, all theinformation related to weather and soil of the corresponding farm will be extracted viathe access to farmer’s location.

6 Conclusion

The coordination failure is a concept that explains the failure of people to coordinateand act on a real-world problem properly. The failure of coordination among humancommunities can lead to social problems in many domains including in transportation,health care, disaster management, agriculture etc. and ultimately affects the sustainablegrowth of a country as a whole. Without the exchange or sharing of information, noinnovation would be able to spread and will result in information gaps. The informationgaps are direct barriers to a country’s development and must be overcome as soon aspossible to limit further problems. The rapid uptake of mobile phones has enabled thesharing of information to be performed quickly and effectively. Such large digitallyconnected communities create a new way to investigate and perceive varied aspects ofhuman behavior, starting from personal events to high risk environments. A combina-tion of such events can be detected from user-contributed data translated into signifi-cant information and modeled to explore the world around us to control adversesituation promptly.

A thorough analysis of information systems developed for agriculture domainrevealed that most of it were widely used as farm management tools. The analysisfurther emphasize that none of the applications were specifically developed forreporting and managing crop losses. In this paper, pest management is used as anexemplar scenario for managing crop losses through a multi-stream event detectiontechnique with the use of a mobile based information system. Early management of

Fig. 10. Soil moisture (sensors) vs atmosphere temperature (weather station) (Color figureonline)

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crop losses is important as it something which has got significant negative impact onthe food security. The following diverse data streams such as farmer observation data(crowd sourced data), weather data, images and spatial-temporal information were usedto detect pest or disease outbreak events. With the use of different streams of data andmatching it with the knowledge base of diseases/pests, personalized information oralerts will be generated to farmers with possible remedies. This will empower thefarmers to take precautionary actions promptly to safeguard their crops before anylosses.

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