Understanding sow’s mothering ability by analyzing their behavioral phenotypes from overhead sensor images

Ahlam Alkiyumi Author

04/06/2021 Added

21 Plays

Description

Studying sow’s behavior and understanding how to improve them will lead to a better mothering ability and low piglet crushing rates which is a fundamental solution to the preweaning mortality issue.

Searchable Transcript

Search:

[00:00:01.980]Hello,
[00:00:02.490]this is Ahlam Alkiyumi and I'm a senior majoring biological systems engineering
[00:00:07.110]and emphasis in biomedical engineering. Today, I will go over my research,
[00:00:11.880]which is about understanding sow's mothering ability by analyzing their
[00:00:16.140]behavioral phenotypes from overhead sensor images.
[00:00:22.250]United State is the world's third largest pork-producing country.
[00:00:26.480]And one of the largest countries that export the pork and pre-weaning
[00:00:31.190]piglet mortality is a major economic and welfare issue in big
[00:00:35.540]production.
[00:00:36.650]There is a study showed that 17.8% of the piglets born
[00:00:41.510]are lost before preweaning and half of the piglets are lost due to crushing by.
[00:00:46.400]Sow it's a very high percentage.
[00:00:50.720]This issue affects piglets well being,worker morale ,and the economic
[00:00:55.250]returns to the us.
[00:00:57.620]So as a way to limit the piglets crushing by sow, swine
[00:01:02.240]industry to develop a farrowing crate.
[00:01:05.780]So in figure one, so the,
[00:01:10.490]sow just has that middle space to move,
[00:01:16.070]but unfortunately,
[00:01:17.990]the crushing still occurs. for our objective in this research
[00:01:23.090]is to identify a serious of sows postures that are potentially related to
[00:01:27.950]their mothering ability from the overhead digital and depth images that were
[00:01:32.930]collected in a swine barn over the pre-weaning period.
[00:01:38.330]And the second one is to analyze the time spent in each posture and
[00:01:43.310]find the difference between high and low mortality.
[00:01:47.150]Yes.
[00:01:50.060]This flow chart shows the method we use in this research.
[00:01:54.800]First thing,
[00:01:55.790]the images were collected by using cameras that takes digital
[00:02:00.590]and depth images,
[00:02:02.030]and the cameras were remotely not attached to the sow's body.
[00:02:06.560]The time interval between images is five seconds.
[00:02:10.550]The second step,
[00:02:13.340]we develop a MATLAB GUI to display the each and,
[00:02:17.450]following pairs of the depth and RGB images
[00:02:20.990]So in this figure one,
[00:02:24.530]this is what we got after we run the program,
[00:02:28.880]the top two images are the current frame
[00:02:33.590]and the bottom two images actually are short running
[00:02:38.480]of the five following frames to help us classify the images.
[00:02:43.400]And here you can see that the posture and the current
[00:02:48.200]frame, which is standing kneeling, sitting,
[00:02:52.370]laying on belly, laying on right side or laying on the left side.
[00:02:56.810]So we have to classify the images and the posture behavior
[00:03:01.570]in these six categories,
[00:03:07.380]labeled images were saved as an Excel file.
[00:03:11.580]The next step is to analyze the total time spent in
[00:03:16.230]each posture for each sow and MATLAB code was developed to
[00:03:20.640]extract the labeled depth images from the saved
[00:03:25.410]Excel file and calculate the time in each behavior for
[00:03:30.150]each Sow over the three days after piglets birth.
[00:03:36.240]And the last step is to find the difference
[00:03:41.040]between low and high mortality.
[00:03:43.020]Sow in their time budget for each
[00:03:47.820]posture. we use Excel data analysis tool to make an
[00:03:52.500]unpaired t-test here's our result and
[00:03:56.670]discussion. more than a hundred thousand images were labeled
[00:04:01.620]for five low piglet pre-weaning mortality sows,
[00:04:05.370]and five high piglet pre-weaning mortality sows.
[00:04:10.650]Uh, in this figure,
[00:04:13.110]two shows the average time spent in each posture for five
[00:04:17.980]low mortality and five high mortality.
[00:04:21.870]Sow within the first 24 hours after farrowing,
[00:04:26.250]so the red is for the high mortality and the blue for
[00:04:31.200]the low mortality. from this figure,
[00:04:36.180]we can highlight some differences in their behavior.
[00:04:40.470]For example,
[00:04:41.910]high mortality spent more time on a standing
[00:04:46.860]sitting and laying on a belly,
[00:04:52.740]but low mortality spent more time on
[00:04:57.420]kneeling within the first 24 hour after piglets
[00:05:02.280]birth,
[00:05:04.800]to see the difference between high and low mortality in the first
[00:05:09.570]24 hours in their behaviors,
[00:05:12.180]we made an unpaired T-test using 0.1 alpha,
[00:05:16.890]and the test showed they are not significantly different in their
[00:05:21.480]behaviors within the first 24 hours.
[00:05:24.990]The last two columns of this table shows the standard deviation
[00:05:29.850]and the p-value for the first day,
[00:05:35.160]we see that p- values
[00:05:40.050]are higher than alpha and maybe this because we have a
[00:05:44.880]very small sample size, just five Sow in each category,
[00:05:51.720]the rest ,of this table shows the mean maximum ,and minimum
[00:05:57.470]of six posture behavior Within the three days after piglet birth,
[00:06:02.390]between low mortality and high mortality sows,
[00:06:06.350]we analyzed from this table,
[00:06:10.550]low mortality spent more time kneeling than high mortality
[00:06:15.350]in all three days,
[00:06:18.350]the saw kneels when she changed from standing to lying.
[00:06:23.270]And it's known that most of the crushing occurs during this
[00:06:27.980]transition from standing to lying.
[00:06:31.010]And this is a good sign of a good mom when she spent
[00:06:35.870]more time kneeling.
[00:06:40.090]based on the 10 sows, we analyzed,
[00:06:42.180]we concluded that there are some marked differences between low mortality and
[00:06:47.020]high mortality sows on kneeling sitting and standing behavior.
[00:06:53.650]We invistigated the potential of using CNN based,
[00:06:58.180]deep learning on automatically classifying.
[00:07:01.240]Sow's posture and the model showed 98.3
[00:07:05.500]3% accuracy on labeling sows behavior.
[00:07:09.400]Using RGB images.
[00:07:12.250]This model will help the analysis of the massive data collected in the
[00:07:17.110]future for the sow's behavior analysis.
[00:07:23.020]I ,
[00:07:23.470]also realized I have to add laying other posyure into the
[00:07:28.270]six categories.
[00:07:32.230]We will add more sows to the research and I'm working on publishing a
[00:07:36.880]journal article.
[00:07:39.460]The last thing I want to thank UCARE program for this opportunity and
[00:07:44.470]thank your Adil and Veronica for helping me. Thank you for listening.

The screen size you are trying to search captions on is too small!

You can always jump over to MediaHub and check it out there.

Comments

0 Comments

Understanding sow’s mothering ability by analyzing their behavioral phenotypes from overhead sensor images

Description

Searchable Transcript

Comments icon comment

Related Channels

Comments