IiWas10 Adit Bima Razi

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The Smart Fishery System based on web remote sensing

Adityo Ashari

Gunadarma UniversityJl. Margonda Raya 100

Depok, Indonesiaadityo.ashari@student.

gunadarma.ac.id

Bima Shakti Ramadhan

Utomo


Depok, Indonesiabima_1990@student.

gunadarma.ac.id

Muhammad Razi


Depok, [email protected]

I Wayan S. Wicaksana


Depok, Indonesiaiwayan@staff.

gunadarma.ac.id

ABSTRACTThe fishery sector is a core part for the Indian economy. butthe issue of climate change make a fishing expedition to beuseless, because sometimes many natural phenomena thatoccur but are not known by the fishermen.Thats why theirfishery productivity are not maximal. In developed coun-tries, maybe this problem can be solved easily. In the de-veloped countries this problem can be solved easily. But fordeveloping countries likes Indonesia, their traditional fish-ermen with limited technology cant solve that problem, Sothats why we build this concept for Indonesian fishermen tohelp them face their limited technology. The analitical dataabout meeting point of the current wave of hot and cold

water,the chlorophyl concentrate ,and the local houl will bemaintained at back end. The system will retrieve the infor-mation based on the interaction with the user, which will bethe fisherman in this case. The first part explains the defi-nition of the smart fishery system and its benefits. Secondlywe talk about another paper that have a related work withthis system. The third part shows that the methodology ofthe smart fishery system. The fourth part shows the expla-nation of how fishermen use the system. Finally, we showthe general description of the smart fishery system.

Student of Sarmag Program, Gunadarma University.Student of Sarmag Program, Gunadarma University.Student of Sarmag Program, Gunadarma University.Center of Information System Studies, Gunadarma Univer-sity.

KeywordsDecision Support System, The Smart Fisher System, webinformation, remote sensing

1. INTRODUCTIONNowadays, the fulfillment of human needs particularly infishery is increasing according to the human population inthe world. With this occurrence, the company which is en-gaged in fish suppliers have to work extra but within a fixedtime.It makes many problem, such as, the large of amountof environment related data, and the slowness of reactionto environment change. Hence, the solution are required to

solve these problems, such as the smart fishery system.

To improve management and decision making in respect offish production and up to national scale. Given the close as-sociation of population and food production, it is possible toestimate the required protein production (fish).This projectis a timely initiative that will bring together two major sci-entific communities with potensial benefit to many millionof people.

In this paper, we want to describe a new generation of fisherysystem that has tremendous potential to improve decisionsupport system. This application will be very useful to thefisherman because it can give the right direction of regionwhere the fish are. So, the user can get a lot of fish in one go.This new and improved application is necessary to developan innovative smart fishery system. This system is especiallydeveloped for fisherman in Indonesia with free websites thatprovides chlorophyl and sea surfeace temperature satelliteimage as source. But for the best result we can use thecommercial websites that provides high quality chlorophyland sea surface temperature satellite image as the source.

This paper is structured as follows. The second sectionpresent our related works. The third section discusses themethodology of smart fishery system. The fourth sectionshows the explanation of how fishermen use the system. The


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final section is closing.

2. RELATED WORKSFor the remote sensing approach, WANG Wen-yu and SHAOQuan-qin [7]from beijing university has use of remote sensingmethods to examine physical oceanography.They discussedthe surface sea conditions, using the indicators of SST andChlorophyll-a that conduced to the production of Ommas-

trephes bartrami which is the second important fish in theNorth Pacific Ocean.Beside that, the paper[6] discussed byHU Solanlkli and the other have a related issues. Both meth-ods can be very useful for the smart fishery system. But thismethod have one weakness. The quality of the image is notgood to be used by fishermen as a reference for fish. Becausethere are no latitude and longitude of the fish region in theimage.

John E. OReilly on his paper [3] stated that large data setcontaining coincident in situ chlorophyl and remote sens-ing reflectance measurement was used to evaluate the ac-curacy,precision, and suitability of a wide variety of oceancolor chlorophyl algorithms for use by SeaWifs. The method

called Maximum band ratio(MBR). This MBR is a new ap-proach in empirical ocean color and has the potensial ad-vantage of maintaning the highest possible satellite sensorsignal : noise ratio over a 3 order-of-magnitude range inchlorophyl concentration. This algorithm will very usefulfor this paper. But it still not maximal for fishermen as areference for fish.

Richard W. Reynold on his paper [4] mention that The newNOAA operational global sea surface temperature (SST))analysis is described. The analyse use 7 days of in situ(ship and buoy) and satellite SST.These analyses are pro-duce weeekly and daily using optimum interpolation (OI) ona 1 degree grid. The OI technique require the specification

of data and analysis error statistic. These statistic are de-rived and show that the SST rms data error from ships arealmost twice as large as the data error from buoys or satel-lite. This method will be very useful for increase accuracyof calculation of the target point (fish). But the weaknessof this method is not able to provide position accuracy offish exist in a region. So that would be very difficult for thefishermen to find fish.

In its development, this paper requires a method for dividesome level of color that will give an option to the user whenuse smart fishery sytem. H.D.Cheng.X.H Jiang use homo-gram thresholding and region merging method to make theimage segmentation[1]. That method will be very useful forthis paper. But for the user it will not give sufficient infor-mation if the system just provide segmentation image.

3. METHODOLOGYIn this paper there are two methodology that will be usedto make this system. First, determine the edge detection incolor region of the image. Second, find the exact geographiclocation of the area.

3.1 Edge Detection In Color RegionFrom figure 1, determine the edge of each color using im-age segmentation process. The figure 1 is the image that is

on paper of reference[7], which assumed Indonesia is in theregion.

For image segmentation processing, we refer to the methodused in the paper of reference [1] that is Homogram Thre-holding and Region Merging. Because by using the formulawhich he gave, The result of segmentation process can bedone and the segmentation between each color has become

very clear.

Figure 1: Chlorophyll Image based on WANG Wen-yu and SHAO Quan-qin paper[7]

Smart fishery system will give five options based on color.The color is red, purple, dark green, light green and yel-low. Red color is an area that has the most concentrationof chlorophyll. But the region is a coastal area which hasa depth so low that it is not suitable for fishermen to findfish. Purple represents areas that have a lot of chlorophyllconcentration. However, in this purple area is the borderbetween the beach with sloping sea. It is estimated that inthis area there are many corals, so it will be very danger-ous for the fishermen when fishing. Dark green representsareas that still have quite a lot of chlorophyll. This area isquite feasible to be the choice of fishermen in determiningthe catchment area. Light green is an area that has quitea lot of chlorophyll chlorophyll. This area became an areathat is highly recommended because it also a meeting placebetween hot and cold currents. Yellow represents areas withlittle concentration of chlorophyll. But because the factorsof heat and cold currents meeting, the area became a prettygood area to be selected as the place to find fish for fisher-men. Blue color is not included in the option because of itslocation too far from shore.

3.2 Find Exact Geographic Location of The

AreaTo help fishermen known the fish area, we need to give the

exact latitude and longitude position. Use matching foundregion with known map or three point methods to get theexact latitude and longitude of the fish area.

3.2.1 Matching Found Region with Known MapThe purpose of this process is to find the right position of fishregion with matching the segmentation result image withknown map (i.e google earth). Adopt scale-space interestpoint detection method from paper[2]. The method is usingcorrelation approach with formula :

pw

[I (hR ( p)) I( p)]2


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Or using the method from paper[5] to match the segmenta-tion image with known map. Figure 2 is simple matchingfigure 1 with google earth using overlay feature.

Figure 2: Overlay Result

3.2.2 Three PointAssumed that the x-axis is longitude and Y-axis is latitude.Suppose that two points known and have coordinates. We

will find a C point (in green/chlorophyl area) that do nothave coordinates. Suppose the first point is at point A (X0,Y0) and the second point is at point B (X1.Y1). The coordi-nates of the C point can be known. So, it can be concludedthat this method can be used as a way to determine thelocation.

Figure 3: Three Point System

From the figure 5 that we can see: Longitude distance A toB is 75 = 2,5 cm or 1cm=30 longitudeLatitude distance A to B is 50=2cm or 1cm=25 latitude

so point C has a distance from point B 2cm in longitude and0,5cm latitude so the position of point C (125-60,700+12,5)= (65,82,5) from the above we can calculate the X position

between 2 point known

4. HOW TO USE ITThe Smart Fishery System can be used by the fishermen onland. The fishermen input their location and the applica-tion will give five option of the fish areas. The system willtell the the exact latitude and longitude position and thedescription of each area. Fishermen choose the fish area byanalyzing the description of each area and record the lati-tude and longitude. Fisherman sailed using the GPS untilreach the latitude and longitude location which they hadnoted.

5. CLOSINGIn this paper, the progress that has existed until now is thesmart fishery system concept design stage that using satel-lite imagery as data source and use the edge detection andfind the exact position of fish areas, also provide the ex-planation of how fishermen use the system. Smart fisherysystem provides an effective contribution to the world withthe existence of fisheries where fishermen can get maximal

productivity of fish and can control the fish empowerment.For future work this concept is expected to be used for otherresearch materials. In connection with the idea of this con-cept may be made a applications such as determining theroute of a fisherman by providing more detailed informationsuch as location of the rocks, wind direction, speed, etc.waves.

6. REFERENCES[1] H. Cheng, X. Jiang, and J. Wang. Color image

segmentation based on homogram thresholding andregion merging. Dept. of Computer ScienceOrganization, Utah State University, 2002.

[2] Y. Dufournaud, C. Schmid, and R. Horaud. Matching

Images with Different Resolutions. pages 34, 2000.[3] J. E. OReilly, S. Maritorena, B. G. Mitchel, D. A.

Siegel, K. L. Carder, S. A. Garver, M. Kahru, andC. McClain. Ocean color chlorophyll algorithms forSeaWiFS. In Journal of Geophysical Research, volume103, pages 24,93724,953, 15 Oct. 1998.

[4] R. W. Reynolds and S. Thomas M. Improved global seasurface temperature analyses using optimuminterpolation. In Proceding of SITIS 2005. NationalMeteorological Center, NWS, NOAA, Organization, 29Aug. 1993.

[5] D. Scharstein. Matching Images by Comparing theirGradient Fields. Department of Computer Science,1994.

[6] H. U. Solanlki, R. M. Dwivedi, S. R. Nayak, J. V.Jadeja, D. B. Thakar, H. B. Dave, and M. I. Patel.Application of Ocean Colour Monitor chlorophyll andAVHRR SST for fishery forecast: Preliminaryvalidation results off Gujarat coast, northwest coast ofIndia. Marine and Water Resources Group, SpaceApplications Centre (ISRO), 27 Apr. 2001.

[7] Q.-q. S. Wen-yu, Wang. Remote sensing of sea surfacefeatures: Implications of fisheries in north pacific ocean.volume XXXVII. Beijing University Of CivilEngineering and Architecture, Institute of GeographicSciences and Natural Resources Research Organization,2008. The International Archives of thePhotogrammetry, Remote Sensing and Spatial

Information Sciences.Beijing 2008.

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