Single Day Analysis

This subsection will provide a more detailed analysis of a single day - 24 February. This day was selected because technical difficulties with the demand feeder during previous days meant that fish had not been fed for 3 days. Their response on this particular day was therefore noticeable both in terms of speed variation and distribution of fish, and was probably associated with compensatory feeding or feeding following food deprivation.

Distribution of fish varied little throughout the day except during the morning (Fig. 8.4). During the first 30 minute block, fish activity occurred throughout the tank, while they activated the demand feeder expecting food (Fig. 8.4i). During the second 30 minute period the activity decreased, the number of activations decreased, and fish were separated from each other and were generally station- ary (Fig. 8.4ii). This changed once the food became available after 09:00h. The activity increase over the next 60 minutes, as shown in Figures 8.4iii and 8.4iv, corresponded to increased number of activations and increased speed of swim- ming (Fig. 8.5). Once the demand feeder stopped dispensing the feed, activity decreased and fish distribution was primarily in the area of the water inlet. Spo- radic feeder activations resumed after 12:30h with increased frequency between 15:00h and 16:00h but the distribution of fish and their swimming speed did not change noticeably. The distance from the feeding measure revealed only one observable change that could be related to feeding. Shortly after 09:00h the range and inter-quartile range have decreased noticeably for about 30 minutes (Fig. 8.6).

(i) (ii) (iii) (iv)

(v) (vi) (vii) (viii)

(ix) (x) (xi) (xii)

(xiii) (xiv) (xv) (xvi)

(xvii) (xviii) (xix) (xx)

(xxi) (xxii) (xxiii) (xxiv)

Figure 8.4: Heat maps of activity on 24 Feb 2010 starting from 0700h (each colour image represents 30 min. of activity). Images (iii), (iv) show activity when food from the demand feeder was available. The feeding area is located roughly at the bottom of each image.

8 9 10 11 12 13 14 15 16 17 18 0 7 14 21 28 35 42 49 56 Time of Day (hrs) Speed (pix/s) 8 9 10 11 12 13 14 15 16 17 18 0 1 2 3 4 5 6 7 8 Activation count Mean Speed Std Dev No. of Activations

Figure 8.5: Mean swimming speed of 10 fish in a research tank on 24 Feb 2010.

8 9 10 11 12 13 14 15 16 17 18 0 15 30 45 60 75 90 105 120 Time of Day (hrs) Distance (pixels) 8 9 10 11 12 13 14 15 16 17 18 0 1 2 3 4 5 6 7 8 Activation count

Median Distance IQR Range No. of Activations

Figure 8.6: Median distance from the feeding area of 10 fish in a research tank on 24 Feb 2010.

8.4

Discussion

It was observed that initially activations of the demand feeder were random but after c. 20 days (from 15 February) fish tended to activate the feeder mainly in the morning. This confirms the observations of other researchers when using the demand feeder approach: trout develop a pattern of feeding in the morning (Bailey and Alan¨ar¨a, 2006).

There was an expectation that a daily swimming speed pattern would emerge (high in the morning, low during the day, high late in the day). Such a pattern was observed on 15 February only, and on the following days it became less no- ticeable. Unrestricted access to food meant that fish did not have to compete for food and therefore did not have to increase their speed for longer periods. Short accelerations which occurred during feeding activations might have been smoothed within the 1 minute sampling period. (The one minute sampling pe- riod was selected to coincide with the activation sampling period of the demand feeder.) It is possible that some activities of shorter durations were not measured. On 24 February, the swimming speed during the period of feed availability was higher than during the rest of the day. Because of technical difficulties in pre- vious days, fish had not received food for 3 days. When the food did become available between 08:00h and 09:00h, there was increased competition for food and therefore increased speed of swimming. Once food was no longer available, the high activity stopped. On the following day there was also an increase in speed up until 08:30h. After this time, activations ceased and the speed started to decrease. Activity during these two days might suggest that after a 3 days of hunger, the feeding response was stronger (compensatory feeding) than after an overnight fasting. Activity on 24 February was high because of hunger, while on 25 February, the hunger had less effect on fish. Research has shown that restricted rations created anticipation of a meal event from fish after a period of acclimatisation. This experiment was too short in duration to observe this anticipatory behaviour.

This study showed that the fish tracking system can be used in tanks on groups of fish in order to analyse daily movement patterns. While the experiment did not achieve significant aquaculture outcomes due to limitations in the access and equipment, it did demonstrate that differences in fish movement could be detected by the system.

8.5

Summary

Manually observing groups of fish in controlled tanks experiments is a tedious task and usually requires sub-sampling if experiments last for extended periods. The system proposed in this chapter demonstrated that it is possible to automatically track fish and extract useful information from the data gathered. Daily heat maps of activity, combined to run as a video sequence, provided the best visual differentiation between preferred feeding times and other times. However analysis of swimming speed could be improved upon and some suggestions are presented below.

The demand feeder activation counts were sampled at one minute intervals and therefore the swimming activity was sampled at the same interval. It is possible that this sampling rate may have obscured some agonistic activities which were very short in duration. While this system did not have the capability to differentiate between behaviours, it can detect changes of speed and direction associated with it. Decreasing the sampling to 5 or 10 seconds might reveal some instantaneous speed bursts. In addition it may be desirable to identify high speed bursts detected by the system and provide a count for a given period (per minute for example). It has been observed on video that most of the time fish were stationary and only a few fish would accelerate and exhibit agonistic behaviour. An automated count of such behaviour could perhaps show a change in behaviour that could not be observed otherwise (due to the averaging process).

Finally, feeding events usually involved surface activity and tracking of fish is difficult during this time due to multiple reflections associated with turbulent water at the surface. An alternative method is therefore required which will look at changes within an image or a sequence of images.

The next chapter of the thesis summarises findings from the thesis work, explains limitations and problems encountered and suggests paths for further research.

CHAPTER 9

CONCLUSIONS

Currently the analysis of fish behaviour in aquaculture is a manual, tedious pro- cess carried out mainly during feeding and for specific reasons. Limited by lack of technology, the analysis relies on sub-sampling at a particular time of day in order to generate meaningful and manageable data sets. The aim of this thesis was to investigate a computer vision based tracking system to analyse fish behaviours automatically and to examine its ability to detect these behaviours in experi- ments in sea cages and tanks. In sea cages the objective was to track individual fish for short periods of time in order to calculate an average swimming speed and direction. In tanks, the goal was to track individuals on a prolonged basis for the purpose of identifying agonistic behaviours and observing behaviours of small groups of fish.

9.1

Summary of Results

This thesis described research into a computer vision system to automate the analysis of fish behaviour in sea cages and tanks. It demonstrated that the extracted data can be meaningful if paired with environmental data (e.g. current speed, tidal cycle, lunar cycles).