A Statistical Measuring System for Rainbow Trout

Transcription

1 A Statstcal Measurg Sstem for Rabow Trout Marcelo Romero, José M. Mrada, Héctor A. Motes, Jua C. Acosta Uversdad Autóoma del Estado de Méco {mromeroh, jmmradac, hamotesv, Abstract Tradtoall, a maual method s used to classf the rabow trout small farms, whch mght cause stress ad phscal damage to the fsh. Addtoall, ths maual classfcato s ot alwas accurate, as farmers ol vsuall check whether the trout s fr, fgerlg or table-fsh sze. I ths paper, we troduce a smple statstcal model to measure rabow trout farms. For ths research, we have desged ad mplemeted a ovel prototpe that cludes caalsato, llumato ad vso compoets to take a D dowward-vew mage of the trout. After that, ths mage s pre-processed to get the trout s cotour, whch s used to estmate the fsh s legth b adjustg the best regresso curve to ths cotour. Fall, the trout s sze s defed b the mmum Mahalaobs dstace to trag data. We have evaluated our epermetal results as a bar classfcato problem ad the best precso scores are 9.66% ad % whe classfg fgerlg ad table-fsh trout, respectvel.. Itroducto Geerall, small farms use a maual measurg ad coutg process whe cultvatg rabow trout [-], []. There are ma reasos to perform such classfcato, but the most mportat are ot ol to feed the trout accordg to ts sze, but also to avod cabalsm to the taks []. Problems whe dog a maual classfcato are, dstctvel, the stress ad phscal damage causes to the specme whe mapulated b the farmer. Moreover, t s beleve that ths classfcato approach s ot accurate, where the trout s take from the water usg a et ad vsuall the farmer decde whether or ot the trout should be chaged to aother tak. Meco, as well as ma other coutres the world, has large hdrc areas whch are deal for aquaculture [5-6]. Takg advatage of both, ts alttude ad atural water resources, the State of Meco (Meco) has partcular terest creasg the trout s producto as a sustaablt ad ecoomcs strateg for local small farmers [7]. Hece, ths s a good opportut to tegrate techolog to optmse the trout s producto ths rego. Hece, ths motvated our research terest the feld, where we have accomplshed some results, cludg a research project ad bachelor scece fal dssertatos [-]. I ths paper we report our epermetal results b practcg a small farm located the Valle of Toluca, Meco [4], where we have observed a maual classfcato process as llustrated Fgure. Some related work s observed the lterature. Chg-Lu et al. [4] proposed a techque to measure dead tua fsh usg a colour patter. I ths work the fsh legth s estmated b proportoal relatoshp betwee the fsh bod pel legth ad the mage referece scale. Ner et al. [5] measure four dead fsh classes b costructg a cetral le alog the fsh bod from horzotal ad vertcal vews of the fsh s bod. Fall, a commercal coutg ad measurg sstem s observed Vak [6], however, there s o further formato about ts classfcato procedure. The rest of ths paper s as follows. Frstl, Secto descrbes our ovel prototpe desged for ths research. The, Secto troduces our statstcal measurg approach. After that, Secto 4 detals our epermetal framework. Net, Secto 5 shows our performace evaluato. Fall, Secto 6 cocludes ths paper ad draws some veues for our future work. Fgure. Maual measurg-classfcato process geerall doe small farms Cetral Meco. Note that ths small farms use led earth taks.

2 . Epermetal prototpe I ths Secto we descrbe our epermetal prototpe, whch has bee desged as part of ths research. Ths ovel prototpe s essetal to collect useful trout s D mages; hece we have metculousl desged t. Fgure, shows our epermetal prototpe, whch has evolved from a tradtoal squared glass fsh-cube (prototpe verso ). We have observed relevat ssues from our frst prototpe ad that kowledge has bee epermetall aalsed to obta our secod model. Note that our two prototpes have bee epermetall evaluated to a trout farm; therefore, we have gathered specal kowledge about hadlg the rabow trout. The, as observed Fgure, our epermetal prototpe cossts of three ma compoets: caalzato, llumato ad vso whch are am to collect D trout mages usg a stadard persoal computer. uform wa at the bottom of the caalsato sstem. To do ths, a lght source s located at accordg dstace to dstrbute lght uforml over a acrlc dffuser. The lght source s hgh was defed b usg a bsecto approach ad measurg the lght test projected to the dffuser wth photo-resstors. We tegrated ths dffuse llumato to crease cotrast to the mage ad hghlghtg the trout s bod.. Vso sstem I order to eplore ecoomcal techolog for our classfcato sstem, we have used a basc D WebCam camera ths epermetato. Ths camera s able to capture RGB-mages wth a mamum resoluto of 98 pels. I ths prototpe, ths D camera s located at the top of the caalzato sstem to capture dowward-vew mages of the trout. Its hgh s proportoal to the caalzato base legth to avod etra data to be captured.. Statstcal measurg approach Fgure. Our epermetal scearo to measurg rabow trout. (a) Statstcal approach wth a persoal computer, (b) Vso sstem, (c) Caalsato sstem, (d) Illumato sstem, (e) Specme to be measured, ad (f) Database.. Caalsato sstem We have desg a ovel caalsato sstem based o opaque-glass wth our prototpe. Ths caalzato sstem poses two ma propertes. The frst propert s regarded to ts trapezodal shape, whch has bee cosdered accordg to the dgtal camera s vso feld prcple. As log as such trapezodal shape avods reflecto to be captured whe takg a dgtal mage. I secod term, we ca meto that ths s a two-caal tra, whch prevets occluso b takg ol oe fsh per caal ad t allows capturg two rabow trout per shot. I ths Secto we preset our statstcal approach to measure rabow trout. Cosderg the rabow trout atural swmmg movemet agast the water flow ad observg the trout from a dowward pot of vew, we hpothessed that a thrd order curve could appromate the trout s bod wth the water. Dfferet procedures ca be followed to obta a thrd order curve equato. However, we prefer a smple but effectve soluto that could be eecuted ole after a trout s mage s captured. The, gve sample pots whch depct the trout bod, we appl mmum squares to compute a polomal thrd order equato [8]: (, ) a a a a ( ) Where, a, a, a, a are costats that ga ther values b solvg the [44] equato sstem (): a a a a a a a a 4. Illumato sstem To assst our vso sstem, we have tegrated a llumato sstem, whch dstrbutes lght a

3 a a a 4 a 5 a a 4 a 5 a 6 The equato sstem () ca be easl solved usg the matr otato,, or more specfcall:. After ths computato, we obtaed the best regresso curve that adjusts the trout s bod captured to a D mage. The, we observe that ths regresso curve s related to the trout s legth, whch could be estmated b computg the Eucldea dstace amog the pots X BA AX B wth the regresso curve. Fall, gve a probe-legth ( l ) a classfcato ca be doe b comparg agast trag legths. For ths research, such comparso s performed b computg the Mahalaobs dstace [] from trag fr, fgerlg ad table-trout legths: d l s ( ) Hece, a probe-trout t s classfed through ts legth l b comparg ts Mahalaobs dstace d agast a predefed threshold, whch fact s the umber of stadard devatos that s epected to be l to the trag ma ( ). 4. Epermetal framework Ths secto presets the epermetal framework to llustrate how rabow trout s measured usg our statstcal approach. As show Fgure, after a RGB mage s take b our prototpe, we are followg a fve stages mage processg to get the trout s cotour. As eplaed Secto, we are measurg the trout s legth usg ths cotour. To classf the trout s mage wth a mage, we are performg four ma steps. Frst, a RGB mage of the trout s take usg our prototpe (Secto ). Secod, ths RGB mage s processed to obta the trout s cotour. Thrd, the trout s legth s estmated b applg our statstcal method (Secto ) to the trout s cotour. Fall, usg that estmated legth, the trout s classfed usg a bar classfcato approach. I Subsecto 4., we provde more detal about our mage processg step. Fgure. Processg a comg mage to estmate the trout s legth usg our statstcal approach. 4. Testg procedure As llustrated Fgures, we have mplemeted a ovel fuctoal prototpe whch allows us to gather RGB mages. After that, as show Fgure, RGB mages are processed (usg stadard algorthms the lterature [9], []) utl we obta the trout s cotour. Net, we appl our statstcal approach to that cotour, so we ca estmate the trout s legth. Fall, a bar classfcato approach s take to classf the trout wth the come mage. We detal our epermetal procedure:. As llustrated Table I, for ths epermet, we have collected a trout-mage database usg our prototpe a farm (llustrated Fgure 4). Ths database was created usg fgerlg ad 8 table-fsh specmes, capturg 8 mages per specme. We regret that o ths vst we were uable to epermet wth fr trout, because of the seaso. TABLE I. EXPERIMENTAL DATA IMAGES. Trout Sze # Specmes # Images per Specme Total mages Fgerlg 8 4 Table-fsh Grad total 8 4. From our database, separate trag ad testg sets are defed (see Table II). Thus, 8 mages for trag ad 66 mages for testg are used. Specfcall, we have two trag data, oe for fgerlg sze ( mages) ad aother for tablefsh sze (8 mages). I both cases, we selected the frst captured mage to be part of the trag set. Hece, we have fgerlg mages ad 56 table-fsh mages for testg.

4 . From these ad 8 trag mages, trag data s gathered, whch fact cosst of trag legths, the arthmetc mea ad the stadard devato for each sze. 4. For each testg trout mage, estmated legth are gathered as llustrated Fgures ad 5. To do ths, we gather a RGB mage usg our prototpe. The, we eecute a fve stages mage processg: gra-scalg, flterg, thresholdg, closg, ad cotourg. Net, we estmate the trout s legth b appl our statstcal approach to the cotour obtaed above. Fall, usg ths estmated legth we classf the trout as fgerlg or table-fsh. 5. To speed up our mage processg step, our vso sstem gathers 646 pels RGB-mages. 6. Captured RGB values are coverted to a grascale b formg weghted sums of the R, G, ad B compoets:.989 * R.587 * G.4 * B ( 4 ) 7. Nose reducto s performed ever grascale mage b usg a [] Gaussa lowpass flter ad.5 G TABLE II. TRAINING AND TESTING SETS. Trout Sze Trag Testg Fgerlg Table-fsh 8 56 Total 8 4 (, ) e ( 5 ) 8. A bar mage s obtaed b usg a.45 threshold, whch was calculated epermetall from trag rabow trout mages. 9. The trout s bod s emphaszed b usg a closg operato, frst eroso ad the dlato wth a [58] mask. Ths operato s the ke to elmate small clusters of pels aroud the trout s bod cluster.. The trout s cotour s obtaed b removg teror pels. I ths case, a pel s set to f all ts 4-coected eghbours are, thus leavg ol the boudar pels o: If The (6). Usg the trout s cotour, we appl our statstcal measurg approach detaled Secto.. B defto, the trout s sze s estmated b computg the Mahalaobs dstace from ths estmated legth to trag data (Eq. ).. For the classfcato ths epermet, mage that the complete testg-trout set (66 total) s passed through out a grd oe b oe two steps. Frstl, the grp s szed to flter ol fgerlg. The, ever trout able to pass ths grd s a fgerlg. Secodl, for the rest of the testg set, the grd s ow szed to flter table-fsh trout. Remember that we are computg the Mahalaobs dstace ad ths allow us to easl mplemet the approach above b usg frstl fgerlg trag data ad secodl table-fsh trag data. Referrg as trag data the arthmetc mea ad the stadard devato from each sze. Aother advatage usg Mahalaobs dstace, s that we ca make our classfcato process as rgd as we decde, b defg a threshold umber of stadard devatos. The, ths paper we are reportg classfcato fgures from oe to s stadard devatos. 4. We are cosderg ths epermet as a bar classfcato problem, as llustrated Table III. B dog ths, we are collectg true postve (TP), false postve (FP), false egatve (FN) ad true egatve (TN) frequeces []. 5. Usg values Table III, performace fgures are geerated b computg accurac, repeatablt, ad specfct whe classfg as fgerlg ad table-fsh trout. Furthermore, we are presetg a recall-precso curve for our classfcato procedure. I ever case, we are evaluatg usg as threshold from oe to s stadard devato. Accurac, a degree of veract, s a measuremet of how well the bar classfcato test correctl detfes a rabow trout s sze. Accurac TABLE III. BINARY CLASSIFICATION. Actual postve Actual egatve Predcted postve TP FP Predcted egatve FN TN TP TN TP TN FP FN ( 7 ) Repeatablt, a degree of reproducblt, s a dcator about how robustl a rabow trout sze ca be detfed.

5 Repeatabl t TP TP FP ( 8 ) Specfct, a degree of specalt, rates how egatve rabow trout s sze s correctl detfed. Specfct TN TN FP ( 9 ) Recall measures the fracto of postve eamples that are correctl labelled: Recall TP TP FN ( ) Fall, we are computg recall ad precso metrcs. Tables VII ad VIII summarses those results ad Fgure 9 plots the respectve recall-precso curve. Observg our epermetal results whe classfg our testg set as fgerlg, we score the best precso, 9.66% at oe stadard devato. Whereas, our best recall score, 94.8%, s obtaed at s stadard devatos. Furthermore, whe classfg those testg legths that do ot fell to a fgerlg class, we scored a % precso ad recall, oe ad three stadard devatos, respectvel. These epermetal results are ot ol motvatg, but also a valuable evdece that dcates effectveess our classfcato sstem. Precso measures that fracto of eamples classfed as postve that are trul postve: Precso TP TP FP ( ) 5. Performace evaluato We ow preset performace fgures whe usg our statstcal model to measure rabow trout farms. As observed Fgure 5, our statstcal approach s performace to measure a rabow trout depeds o our mage processg stage. However, accordg to our epermetal results, we beleve that we have addressed ma ssues about capturg ad processg a RGB mage wth our sstem. As we have metoed before, we cosder ths as a bar classfcato problem. To proceed wth ths, we are followg step our epermetal procedure (Secto 4.). Thus, the complete testg legths (66 mages) are compared agast fgerlg trag data, usg Mahalaobs dstace. The, f a testg legth falls to a predefed threshold (oe to s stadard devatos) the respectve testg trout s marked as fgerlg. Net, all remag testg legths are compared agast table-fsh trag data usg Mahalaobs dstace as well. Aga, f the testg legth falls to a predefed threshold (oe to s stadard devatos) we label the respectve testg trout as table-fsh trout. The, as prescrbed Table III we cout TP, FP, TN ad FN frequeces, whch are summarsed Tables IV ad V. Hece, b usg these values we are able to compute accurac, repeatablt, ad specfct metrcs, whch s preseted Table VI ad llustrated Fgures 6 to 8. Fgure 4. Fuctoal prototpe used wth our measurg sstem. Ths prototpe cludes a llumato source, a pramdal caalsato compartmet ad a D camera. (a) (c) (e) (g) Fgure 5. Image processg performed to measure a rabow trout usg our statstcal approach. (a) RGB comg mage sesed b the vso sstem; (b) Gra-scalg; (c) Flterg; (d) Thresholdg; (e) Closg; (f) Cotourg; (g) trout s legth estmated b a thrd order regresso curve (plotted red). (b) (d) (f)

6 TABLE IV. FREQUENCY WHEN CLASSIFYING A PROBE SET AS FINGERLING. # Stadard devatos +/- +/- +/- +/-4 +/-5 +/-6 TP FP TN FN 44 5 TABLE V. FREQUENCY WHEN CLASSIFYING A PROBE SET AS TABLE- FISH. % Repeatablt Fgerlg Table-fsh Stadar devatos Fgure 7. Repeatablt performace whe classfg oug ad adult rabow trout usg our statstcal model. # Stadard devatos +/- +/- +/- +/-4 +/-5 +/-6 TP FP TN 44 5 FN 7 % Specfct Fgerlg Table-fsh. Classfcato Stage Evaluatg as fgerlg Evaluatg as table-fsh TABLE VI. CLASSIFICATION SUMMARY. Testg mages Accurac Repeatablt Specfct 66 74% 9% 8% % % % Estadar devatos Fgure 8. Specfct performace whe classfg oug ad adult rabow trout usg our statstcal model...9 TABLE VII. RECALL-PRECISION WHEN CLASSIFYING A PROBE SET AS FINGERLING. # Stadard devatos +/- +/- +/- +/-4 +/-5 +/-6 Recall 5.8% 79.4% 84.76% 9.47% 9.86% 94.8% Precso 9.66% 84.6% 77.9% 77.% 77.69% 77.95% % Precso Fgerlg Table-fsh TABLE VIII. RECALL-PRECISION WHEN CLASSIFYING A PROBE SET AS TABLE-FISH. # Stadard devatos +/- +/- +/- +/-4 +/-5 +/-6 Recall 9.56% 96.% % % % % Precso % % % % % % % Accurac Fgerlg Tablefsh Stadard devato Fgure 6. Accurac performace whe classfg oug ad adult rabow trout usg our statstcal model % Recall Fgure 9. Recall-precso curves whe classfg fgerlg ad table-fsh trout. 6. Coclusos I ths paper we have troduced our statstcal sstem to measure rabow trout farm usg computer vso. Ths ovel techque s a smple but effectve statstcal method whch has bee evaluated a small farm Cetral Meco. For ths research, we have desged ad mplemeted a fuctoal prototpe to collect D trout mages. Ths prototpe cludes caalzato, llumato ad vso compoets whch have bee metculousl assembled. Also, we beleve that ths prototpe could be easl tegrated to a mechacal sstem to tercoect led earth taks farms. Our prelmar results ecourage our research as the have show that our classfcato sstem s

7 effectve, where a 9.66% ad % precso are observed whe classfg fgerlg ad table-fsh trout, respectvel. It s mportat to observe that, although our statstcal approach has bee spred to measure rabow trout, ths approach ca be appled to other fshes grow farms. As part of our future work we are tegratg a water flow to our prototpe as well as a stereo vso sstem, the, we are vestgatg two ma ssues: the lght reflecto to the water ad the presece of turbulece. Our fal am s to mplemet a ecoomcal classfcato sstem whch would be stalled small farms Cetral Meco. [4] Chg-Lu Hseh, Hsag-Yu Chag, Fe-Hug Che, Jhao- Hue Lou, Shu-Ka Chag, Ta-Te L (). A smple ad effectve dgtal magg approach for tua fsh legth measuremet compatble wth fshg operatos. Computers ad Electrocs Agrculture, Volume 75. [5] Ibrahm, M.Y., Wag, J (9). Mechatrocs Applcatos to Fsh Sortg Part : Fsh sze detfcato. Idustral Electrocs (ISIE 9). IEEE Iteratoal Smposum. [6] Vak (). Boscaer Fsh Couter. Refereces [] Romero, Vlchs ad Portllo (). Itellget sstem to cout, measure ad classf fshes usg computer vso. Research project SIEA 74M. Autoomous Uverst of the State of Meco. [] Serea Mejía-Pchardo (). Coteo clasfcacó de la trucha arcoírs utlzado vsó artfcal: revsó lterara aálss. BSc. Fal Dssertato. Egeerg Departmet. Autoomous Uverst of the State of Meco. [] José Mauel Mrada-Cotreras (4). Prototpo de u sstema clasfcador de la trucha arcoírs utlzado u modelo estadístco de su logtud obtedo de mágees D. BSc thess, Egeerg Departmet, Autoomous Uverst of the State of Meco. [4] Rco del sol (4). Small Trout Farm. La Marqueza, State of Meco, Meco. [5] Comsó Nacoal del Agua (6). Cocesó de Aprovechameto de Aguas Superfcales. Water Natoal Coucl. [6] Daro Ofcal de la Federacó (4). Le de aguas acoales. DCVII(4): Federal Ofcal News. [7] Gallego A. I., R. Carrllo, D. García, L. Sasso, J. Guerrero, R. Carrllo, D. García, C. Díaz, C. Fall, C. Burrola, L. Whte, J. Majarrez, C. Zepeda, X. Agular, G. Legorreta A. Sáchez. (7). Programa maestro, sstema producto trucha del estado de Méco. Govermet Pla for Rabow Trout Producto. Autoomous Uverst of the State of Meco, Méco. [8] Murra Spegel (). Probabldad Estadístca. Mc Graw- Hll. [9] Rafael Gozalez ad Rchard Woods (8). Dgtal Image Processg. Pretce Hall. [] Rchard Duda, Peter Hart, Davd Stork (). Patter Classfcato. Wle Iterscece. [] Adrás Woarovch, Görg Hots ad Thomas Moth- Poulse (). Small-scale rabow trout farmg. FAO Fsheres ad aquaculture techcal paper (56), Food ad Agrculture Orgazato of the Uted Natos. [] Rafael Gozalez, Rchard Woods ad Steve Edds (4). Dgtal mage processg usg Matlab. Pretce-Hall. [] Jesee Davs ad Mark Goadrch (6). The relatoshp betwee precso-recall ad ROC curves. I proceedgs of the rd Iteratoal Coferece o Mache Learg.