An insect observation system in Vietnam

1. An insect observation system in Vietnam Bao Hoai LAM

2. Plan • Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion

3. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Context • Insects cause damage to crops • Brown Planthoppers (BPH) cause a loss of hundred thousands tons of rice production in Vietnam • A light trap network is established to confront with hoppers.

4. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion ContextCurrent light trap network • Compose of light traps • Traps are turned on from 7:00pm-11:00pm every day. • To sampling estimate hopper densities in some places for decision making. • Monitor hopper densities  decision making • Classify and count insects by hand

5. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion ContextDecision making • Escape strategy: propose a synchronous cultivation time in a large area • Identify the peak day of hoppers in light traps. • Sow rice fields after the peak day very soon • Principle: • Hoppers cause big damages in young rice • BPH life circle: ~ 30 days • The next generation of hoppers: ~ 30 days after the peak day • If the rice is sown after the peak day, it is strong enough to confront with the next generation.

6. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Problematic • Light trap network based on Wireless Sensor Network approach. • Insect behaviors and environmental factors are sampling measured by automatic light trap sensor nodes • Insect surveillance network • Question • How sampling environmental factors sensed in sensor nodes affect insect behaviors? • How to know the relation between the damage caused by insect and the data collections in sensor nodes? • How to classify, count/estimate insect densities?  A model for insect behaviors, environmental factors and WSN  A solution to classify, count/estimate massive distributed observations

7. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Method • Model an insect surveillance network as synchronous networks based on cellular automata • Insect surveillance network is composed of 2 systems: • Insect physical system • Network system • Both can be modeled as synchronous networks and they interact each other • Automatic light trap • Provide a mechanism to count/estimate/classify insects.

8. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect surveillance network • An insect surveillance network is a network to monitor insect behaviors due to environmental factors based on WSN approach • 2 systems in an insect surveillance network: • Insect physical system • Network system

9. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Synchronous network • Synchronous network is a network describing synchronized rounds of message exchange and computation • It consists of pieces of processes which may send and receive messages simultaneously • A synchronous network is a graph G where processes are nodes and these processes communicate together via their edges using message sending • Each node consists of : • Statesi: a collection of states at node (process) i • Transition-rulei: rules allowing the node (process) i to send messages to neighbors in order to compose its new state

10. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect surveillance network • Insect physical system: a system that insects behave, a working space or working environment of insects • Network system: sensor nodes compose a network system to monitor the insect physical system

11. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect surveillance network Insect physical system • Insect physical system can be considered as a synchronous network • The space (or environment) of insect behaviors is divided as cells • Each cell has 4 neighbors (Von Neumann) or 8 neighbors (Moore) or 6 neighbors (hexagon)

12. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect surveillance network Insect physical system • Each cell can, at any given time, be in a finite number of states • Rice age • Temperature • BPH velocity • Wind • BPH density • Each state has transition rule to change the new state • At the time t, the state of a cell depends on the state at time t-1 of its neighbors and itself • Updating the rules are identical to all cells • Whenever the rules are applied to the entire system, they could change the entire system synchronously

13. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect surveillance network Insect physical system • Insect physical system is a graph G1=(V1, E1). • Nodes: cells to hold a collection of states • Edges: links between a node and its neighbors • Behaviors at node i are expressed by transition rules of states • Transition rules: function causes insect behaviors

14. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect surveillance network Network system • Automatic light trap sensor nodes of the observation WSN are distributed in the some sampling cells of the insect physical system • Automatic light trap sensor node only works at nighttime with an interval time ~ 0.5 - 1 hour • The WSN can be considered as a synchronous network • Node: sensor node • Edge between 2 nodes is identified by their transmission range • Behaviors: communication behaviors to transmit data via the WSN

15. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect surveillance network Cyber Physical System • A Cyber Physical System (CPS) is a system of collaborating computation and physical processes • The insect surveillance network fits into a CPS framework • Environmental factors become physical entities while the observation network is the computation • Physical loop between physical entities and computation with timed characteristics

16. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect surveillance network INSECTSYN • Insect surveillance network can be called as INSECTSurveillance sYnchronous Network (INSECTSYN) • Synchronous points are needed in operating such INSECTSYN because insect physical system is continuous while network system is discrete

17. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion INSECTSYN implementationWorkflow

18. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Data model • A data model for insect surveillance network • Metadata for NetGen • Cellular system: cell, neighbors • WSN: sensor node, neighbors • Data collected from sensors

19. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Data modelConceptual diagram

20. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Data modelConceptual diagram

21. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Data modelPhysical data in Postgres /*==============================================================*/ /* Table: CELLNEIGHBORS */ /*==============================================================*/ create table CELLNEIGHBORS ( TCE_CELLID INT4 not null, CELLID INT4 not null, ID INT4 not null, constraint PK_CELLNEIGHBORS primary key (TCE_CELLID, CELLID, ID) ); /*==============================================================*/ /* Index: CELLNEIGHBORS_PK */ /*==============================================================*/ create unique index CELLNEIGHBORS_PK on CELLNEIGHBORS ( TCE_CELLID, CELLID, ID ); /*==============================================================*/ /* Table: TCELL */ /*==============================================================*/ create table TCELL ( CELLID INT4 not null, TSE_CELLID INT4 null, NODEID INT4 null, PROJECTID INT4 not null, CELLSYSTEMID INT4 not null, XPOS INT4 null, YPOS INT4 null, CELLLONGITUDE NUMERIC(15,5) null, CELLLATITUDE DECIMAL(15,5) null, CELLELEVATION NUMERIC(15,5) null, NEIGHBORTYPE VARCHAR(20) not null, constraint PK_TCELL primary key (CELLID) ); /*==============================================================*/ /* Index: TCELL_PK */ /*==============================================================*/ create unique index TCELL_PK on TCELL ( CELLID ); /*==============================================================*/ /* Index: HAS_CELLS_FK */ /*==============================================================*/ create index HAS_CELLS_FK on TCELL ( PROJECTID, CELLSYSTEMID ); /*==============================================================*/ /* Index: BELONGS_TO_FK */ /*==============================================================*/ create index BELONGS_TO_FK on TCELL ( TSE_CELLID, NODEID ); /*==============================================================*/ /* Table: TCELLULARSYSTEM */ /*==============================================================*/ create table TCELLULARSYSTEM ( PROJECTID INT4 not null, CELLSYSTEMID INT4 not null, CELLSYSTEMNAME VARCHAR(100) not null, CELLSYSTEMTIMESTAMP DATE null, CELLWIDTH INT4 null, CELLHEIGHT INT4 null, CELLRADIUS INT4 null, constraint PK_TCELLULARSYSTEM primary key (PROJECTID, CELLSYSTEMID) );

22. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Data modelExport to Occam

23. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Data modelEntry point in Smalltalk

24. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect density estimation • Emphasize on sensing aspect • Sensors in the insect surveillance network can estimate the insect density directly • Do not transmit video/image/signal to the data center • A hybrid solution of mechanics and computer science. • Establish a suitable device  automatic light trap sensor node • Implement algorithms for classifying, counting/estimating

25. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect density estimationAutomatic light trap sensor node • Sensors: wind, humidity, temperature, sound • Camera: takes image of the square plane and processes directly.

26. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect density estimationWorkflow for estimating • Signals: image, video, sound or direct from vision sensor. • Feature: Mean red, mean green, mean blue of objects • A learning method is used to classify • Classification/estimation/Counting

27. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect density estimationStrategy • If insects consumed a large area  BPH estimate thanks to its area.

28. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect density estimationStrategy • If non-densed  classification thanks to mean red, mean blue, mean green. Red    Green    Blue66.2128    61.633    44.4947  53.2416    49.0243    32.9213  56.8474    47.8097    25.5046  73.1248    62.486    32.7044  52.3444    48.5074    30.3578  73.4427    66.6412    39.7053  

29. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Insect classification • Segmentation: Watershed segmentation • After segmentation: extract color of blobs • Removing noise based on area size of blobs • K Nearest Neighbors to find the nearest species

30. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion ClassificationDiscussion • Image/Video • Sound • Signal from vision sensors • A bit more • Application in BPH detection in rice fields • Motion

31. Introduction • Insect surveillance network • Synchronous network • Model description • Work flow with NetGen • Data model for insect surveillance network • Insect density estimation • Conclusion Conclusion • Insect surveillance network INSECTSYN is composed of an insect physical network system and a WSN system • Insect physical system is divided as cells • Behaviors of these cells are executed simultaneously • WSN system: sensor nodes are executed in synchronous rounds • Propose a solution for estimating/counting/classifying insects • Automatic light trap • Perspective: • LORA technology to transmit data to a distant destination

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