Camera-controlled fish-feeding

Transcription

1 Ref: C0478 Camera-controlled fish-feeding Eiko Thiessen and Eberhard Hartung, Institute of Agricultural Engineering, University Kiel, Max-Eyth-Str. 6, Kiel, Germany Abstract The feeding of fish in aquaculture accounts for over half of the production costs (Wilson 2002). Thus it is very important to minimize the amount of uneaten food which will also improve the water quality. The aim of this project was to detect the alternation of fish activity due to feeding and feed uptake by image analysis and to use this information to control an automated feeding system. In the current experiment a camera-controlled automatic feeding was compared with automatic feeding with a fixed amount. A digital camera was installed submerged in a round basin with turbots (Scophthalmus maximus) of a land based research station. In long-term studies fish activity and behavior was detected with juvenile, small-size and mid-size turbots and analyzed concerning the feeding and non-feeding times by image analysis and expert assessment. Based on this data a control algorithm was developed and integrated in a commercial automatic fish feeding system. The result of the current study showed for turbots that resting with no activity accounts for over 99 % of the day. During feeding the whole fish group was active and the measured activity rose from the start of feeding, reached a maximum after approx. 5 s and stayed almost constant during the time of feeding until it dropped down when feeding was stopped or fish were not hungry anymore. By means of activity analysis it was possible to estimate the times when feed was applied but fish were not active and therefore not eating and high feed losses occurred. The observed potential of feed saving is in the range of 30 %. A first trial with camera-controlled feeding system compared to a commercial fixed automatic feeding system showed a benefit of growth increase due to better feed conversion (2.3 kg feed applied / kg growth compared with 2.8 respectively). Keywords: aquaculture, recirculation-systems, automatic-feeding, image-analysis 1. Introduction In contrast to pulmonary livestock, branchiate animals like fish metabolize their ambient water which should not be polluted additionally with food waste. Normally fish are fed by hand or automatic feeder. Feeding by hand is labor consuming and automated feeding is restricted to fixed amount of feed independent on whether there is still feed uptake by the fish or not. If automatic feeding would have a feedback from the fish itself, if feed is still eaten, this kind of feeding could meet the demands of fish and feed losses could be minimized. There are already feeding systems which are so-called demand feeding like pendulum dispenser. But in these system only a few fish are responsible for triggering the feeding and therefore the feed leavings, feed losses respectively, are high (Alanärä 1992, Alanärä & Brännäs 1993, Coves et al. 2006). In off-shore aquaculture it was possible to determine online the feed leavings under the cages with hydro-acoustic methods and to control the Proceedings International Conference of Agricultural Engineering, Zurich, /8

2 feeding therewith. This led to an increased growth rate from 0.7 %/d to 1 %/d for salmon (Juell et al. 1993) but it is not suitable for small basins on-shore. Fábregas et al measured the activity of trouts with electromechanical sensors and described the significant rise of activity during the feeding period. The swimming activity decreases for many fish species when the feed uptake is dropping. But instead of electromechanical sensors the fish activity could also be detected with an image analyzing system which is supposed to stop feeding events automatically if there is no feed uptake anymore. The aim of this project was to detect the alternation of fish activity due to feeding and feed uptake by image analysis and to use this information to control an automated feeding system. In the current experiment turbots were used as model fish and a camera-controlled automatic feeding was compared with automatic feeding with a fixed amount. 2. Materials and methods The general approach of the current study was to detect and describe the feeding behavior of turbots in different production periods by image analysis and to develop an algorithm for the camera controlled automatic feeding system and to validate it in comparison to normal automatic feeding system with a fixed amount of feed per feeding. Therefore activity data was collected with juvenile, small-size and mid-size turbots in a time period of two months for each size and analyzed concerning the feeding and non-feeding times by image analysis. Based on this data a control algorithm was developed and integrated in a commercial automatic fish feeding system. Two fish groups were fed with the two feeding systems that are to be compared for about four months. System set-up The experiments were performed in an on-shore research station at Büsum, located at the north sea. Ten black-plastic tanks (each 1.8 m diameter and 1 m water height) formed one system with the same water and one tank was used for camera observation and two tanks for the feed trial. Juvenile turbot (P. maxima) were obtained from Maximus A/S (Bedsted Thy, Denmark) and were fed with commercial pellets (size adapted to fish size). The fish size varied during the observed time periods between 50 g and 1 kg and the fish-density between 10 and 100 kg/m². The water is reused as common in a recirculation aquacultural system. The mechanical-biological filtering consists out of micro mesh filter, sedimentation, skimmer, fluidized bed reactor, trickling filter and moving bed filter. Water quality parameters like temperature, ph, oxygen, redox and conductance were measured continuously and were within the normal range during all experiments. For the activity measurement a digital camera (monochrome, GigE-ueye UI-6210-SE with 640x480 pixel and a ½ CCD chip) was housed watertight in a plastic tube with an acrylic glass window. It was adjusted under the water surface with an oblique angle to the ground. Due to the use of a fisheye lens (Fujinon FE185C057HA-1 with 185 field angle in air correspondent to 100 in water) almost the whole basin was in the field of view (figure 1). The window of the camera-housing was flushed continuously with an extra water flow to avoid air bubbles in front of the camera. There was no extra illumination for the camera; the mean light intensity due to ambient and artificial light (fluorescent lamps) was about 100 lux. The image acquisition and analysis was conducted with a commercial PC and the software Halcon 9.0. Three different activity parameters from succeeding images were tested preliminary for performance in changing light conditions: a) the standard deviation in all greyvalues of a difference image, b) the rate of areas with a greyvalue difference above a threshold and c) the Proceedings International Conference of Agricultural Engineering, Zurich, /8

3 averaged vector-length of optical flow. Pre-tests indicated, that the three parameters were not affected by different light conditions until the camera chip is not over- or underexposed and if light conditions are not changing quickly. The calculated standard deviation a) showed to be the most robust parameter in respect to exposure and was used for the following results. Typical values are in the range of 0 to 10, where 0 to 1 reflects no fish motion and 5 to 10 high swimming activity. To avoid over- or underexposure the camera exposure time was automatically adapted every hour to the ambient light. The frame rate was set to two images per second and the activity was calculated with a moving average over 4 s. Recording of activity parameter and images was normally every 5 min, but at feeding or in periods with swimming activity is was increased to every second. A commercial automated feeder (Linn Profi 5 kg) was used in a timer configuration (fixed duration of feed and start times programmable) or in a PC configuration (electronic signal switches the feeder on and off). Figure 1: A set of images indicating the three regions and the related activity during feeding 2.1 Estimation of feeding behavior The monitoring of the fish activity was performed during different production phases in a time period of altogether one year. The fishes were separated every month to form a homogenous group with a weight variation of less than 20 %. For the collection of activity data the turbots were fed ten times a day with the automatic feeder in timer configuration. The amount was set to 2 % of the total biomass daily. The camera image was separated in three regions bottom, middle and top (figure 1). Abnormal conditions like turbid water or non-eating fish were recorded by manual screening of the images. The activity data was analyzed during and between the feedings. The time slice for swimming at each period was calculated by defining swimming as an activity higher than 1. The timeline of activity during the feeding was averaged by resampling the activity in each region in respect to the time after feed start. The software MatLab R2010 was used with the Signal Processing Toolbox. By checking the activity during the feeding the actual appetite level of the fish was estimated. No activity during feeding indicates no swimming and no feed uptake; four levels of appetite were defined: no appetite (0 < activity < 1), low appetite (1 < activity < 2), normal appetite (2 < activity < 5) and high appetite (5 < activity). Proceedings International Conference of Agricultural Engineering, Zurich, /8

4 2.2 Feeding algorithm Manual feeding and the subsequent interpretation of fish behavior were conducted from experts simultaneously with data acquisition. The feeding control algorithm was developed based on the expert interpretation and the signal-analysis of the detected fish activity respectively. The main focus was to specify a threshold of activity at which feeding still meets the fish demand. The feeding algorithm does not use the activity at the surface as dropping pellets could wrongly be assessed as fish activity there. It follows chronologically four steps: 1. feeding minimal duration, estimation of an actual motion threshold by averaging the actual fish activity and weighting with a factor < 1 2. interval-feeding with pauses: feeding only if actual activity is higher than motion threshold or if activity during last pause was permanently higher than motion threshold 3. extra feeding every baiting period for interval length to check if fish still react on feed even if activity was too low before 4. stop feeding at maximal duration It features adjustable parameters (bold printed before) for setting the maximal and minimal amount of feed, the timing of the interval-feeding and the extra feeding (figure 4). maximal activity at bottom and middle estimation of motion threshold motion threshold feed on feed off minimal duration extra feeding time maximal duration Figure 2: Example of the algorithm for activity dependent feeding. The time span is about 7 minutes. Validation of camera-controlled feeding Two identical groups of turbots (80 individuals with total 70 kg) were fed four months in two separate tanks in the same water circuit; one with a fixed automated feeder and the other with a camera-controlled feeder. The daily amount of the fixed feeder was set to 1 % of biomass with a progression of 0.8 %/d. The minimal and maximal duration of the cameracontrolled feeding was set to get a minimal feeding amount of 0.5 % biomass and a maximal amount of 2 % biomass with the same progression. The parameters for the algorithm were slightly varied during the trial. Both feedings were split in three times per day. The weight of all fish was determined at the beginning and the end of the experiment. Proceedings International Conference of Agricultural Engineering, Zurich, /8

5 3. Results 3.1 Feeding behavior A total of over 1000 feedings events were evaluated. Some events were neglected due to turbid water or dirty camera window. In table 1 the swimming activity between the feeding events is summarized. It is distinguished between the frequency of swimming expressed by the time slice for swimming related to the whole time and the mean swimming activity in times with swimming activity. Table 1 indicates that all fish groups were swimming less than 1 % of the time and that there was a trend that younger turbots were swimming more often and with a higher mean activity. Table 1: Fish behavior between the feeding events time slice "swimming" (%) mean activity weight mean* bottom middle top mean* bottom middle top observed time (h) < 100 g g g g - 1 kg all *averaged over regions and 60 s Fish activity at feeding events showed the same tendency, that younger turbots were more active during feeding than older turbots. For this purpose the first 20 s after starting of feeding were analyzed by averaging over time and events. The younger fish were swimming pronounced in the upper water layer whereas older turbots preferred the lower water layer during feeding (table 2). Overall fish activity was higher than 1 only in 72 % of the feeding time. This implies, that in the rest of 28 % of the feeding time fish were not swimming and therefore not eating, what could be interpreted as a potential feed saving of approx. 30 % related to the used feed management. Table 2: Fish behavior during the feeding events for different fish size eating time slice (%) mean feeding activity weight total bottom middle top total bottom middle top observed events < 100 g g g g - 1 kg all The classification in appetite levels leads to the evident result that hungry fish were more active during feeding as expressed in table 3. An important result is the mean feeding activity for normal appetite of 4.4, which indicates that on average an abnormal low appetite is described by a mean activity lower than about 4. On the top water level the detected activity showed always the highest values during feeding and often the highest values between the feedings. Manual screening of the corresponding images illustrated motion caused by waves, foam, light reflexion or dropping pellets during feeding. Proceedings International Conference of Agricultural Engineering, Zurich, /8

6 activity middle region Table 3: Fish behavior during the feeding events for fish of different appetite levels eating time slice (%) mean feeding activity appetite total bottom middle top total bottom middle top observed events no low normal high If analyzing the temporal dependent activity during feeding by averaging the measured data over different appetite levels, there is a high variation in the feeding events. This is expressed in figure 3 with the standard deviation of each resampled activity of the middle region at a discrete time step after feeding start. But on average there is clearly a typical rise of activity after starting the feeding, which is highest for hungry (high appetite) fish at approx. 10 s and a distinct decrease of activity until feeding is stopped. A mean threshold between fish with low and normal appetite could be seen already after 5 s after feeding start at an activity of approx appetite: high normal low no standard deviation time after feedstart in s Figure 3: Averaged temporal activity of the middle region during feeding classified in different appetite levels 3.2 Feed trial The results of the feed trial are summarized in table 4. The initial total weight and fish numbers of the two fish groups were the same but after 109 days the camera-controlled fish group did gain about 8 kg more weight than the fixed fed fish group. This is not only due to the more feed applied to the camera-controlled fish group of about 9 kg as the feed conversion ratio of the camera-controlled fish group with 2.27 is more productive than the fixed group with Table 4: Fish performance of the two test groups fixed amount camera-controlled initial weight (kg) final weight (kg) feed applied (kg) growth (kg) feed conversion ratio (kg feed / kg growth) specific growth rate (%/d)* *based on number of feeding days Proceedings International Conference of Agricultural Engineering, Zurich, /8

7 applied feed (g) It can be assumed that the difference in feed conversion ratio is mainly caused by the smaller amount of non-eaten food of the camera controlled fish group, because physiologically the feed conversion ratio for turbot is normally around or even lower 1.0 (Bonaldo et al. 2011). In figure 4 the daily applied feed is plotted for both fish groups in an exemplary time period. The camera-controlled feeding system did create a higher total daily feed amount by following the actual feeding behavior: in the morning when the fish are very hungry they were fed almost at maximum and in the evening when they are full the applied feed is almost at minimum (figure 4). The increase of the specific growth rate for the camera-controlled feeding of 0.13 %/d compared to the fixed feeding confirmed the better feed management of the activity adapted feeding fix feeding camera feeding camera feeding min camera feeding max elapsed days Figure 4: Applied feed from day 20 to 30 of the two groups with indication of the limits in the variable amount of the camera-controlled feeding algorithm. 4. Discussions 4.1 The method of activity estimation The activity measurement with a digital camera is simple, robust and worked reliable. The differentiation in three image regions (bottom, middle and top area) can support additional information: because fish are not identified by the applied motion detection, surface waves, light reflexion and foam at the top can wrongly be assessed as fish activity. For this reason the top area is excluded in the camera controlled feeding algorithm. Although it is not possible to differentiate between a small fish swimming near the camera or big fish swimming afar the camera the averaging over a lot of individuals leads to realistic activity results. If the water is turbid the sight of the camera is reduced and typically the measured fish activity is lower. In the night with no light there is no activity measurable; but it is assumed that turbots in aquaculture are not active in the night without feeding. The activity of turbots was monitored continuously the first time in the current study. Compared with the activity measurements of trouts (Fábregas et al.1993) the differences between feeding and non-feeding activity is very pronounced for turbots. This is explicable as a turbot is a still lying benthic lurking predator. Proceedings International Conference of Agricultural Engineering, Zurich, /8

8 4.2 Feed trial The results of the feed trial are in general promising. A feeding strategy with fixed amounts of feed per feeding event results in feed conversion rations over 2 which indicates a lot of feed leavings. High feed losses were especially observed during the feeding times in the evening or at accidental day-periods when fish were - why ever - less hungry. The camera-controlled feeding system was able to recognize these special situations and to respond by minimizing the amount of feed applied. Therefore success of feed saving of the camera controlled feeding system is strongly influenced by the settings of the minimal feed amount (maximal saving at zero limit). The presented results demonstrate the potential of better feed utilization for fish growth although the fish growth with a specific growth rate of less than 0.5 %/d is not optimal. Normally the feed conversion ratio for turbot should be around 1.0 and the specific growth rate around 2 %/d (Bonaldo et al. 2011). 5. Conclusions The analyzed behavior of turbot by image analysis is reproducible and utilizable. The observed potential of feed saving in the range of 30 % for an optimal fish feeding system with no leavings is high, but has to be related to the use of automated feeder with a high amount of 2 % biomass daily. Feeding manually by hand is still seen as the most effective method (and in this project used as reference for finding the algorithm) to maximize gain. Further feed trials possibly with other species should be carried out to prove the current results. 6. Acknowledgements The project was part of AquaEdel founded by the Federal Office for Agriculture and Food in Germany. The author would like to thank the staff of the Gesellschaft für Marine Aquakultur mbh in Büsum for fish handling. 7. References Alanärä, A. & Brännäs, E. (1993). A test of the individual feeding activity and food size preference in rainbow trout using demand feeders. Aquaculture International 1, Alanärä, A. (1992). Demand feeding as a self-regulating feeding system for rainbow trout (Oncorhynchus mykiss) in net-pens. Aquaculture, 108, Bonaldo, A., Parma, L., Mandrioli, L., Sirri, R., Fontanillas, R., Badiani, A., Gatta, P.P. (2011). Increasing dietary plant proteins affects growth performance and ammonia excretion but not digestibility and gut histology in turbot (Psetta maxima) juveniles. Aquaculture, 318 (1 2), Covès D., Beauchaud, M., Attia, J., Dutto, G.,Bouchut, C., Bégout, M.L. (2006). Long-term monitoring of individual fish triggering activity on a self- feeding system: An example using European sea bass (Dicentrarchus labrax). Aquaculture, 253 (1-4), Fábregas, J., Nascimento, P., Vecino, E., Gamallo, Y., Otero, A., González, C. (1993). Development of an electromechanical sensor and computer data acquisition system for monitoring the movement of cultured fish. Aquacultural Engineering, 12 (1), Juell, J.E., Furevik, D.M., Bjordal, Å. (1993). Demand feeding in salmon farming by hydroacoustic food detection. Aquacultural Engineering, 12 (3), Wilson, R.P., (2002). Protein and amino acids. In: Halver, J.E., Hardy, R.W. (Eds.), Fish Nutrition, 3rd Version. Elsevier Science, San Diego, USA, Proceedings International Conference of Agricultural Engineering, Zurich, /8