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

Ahlam Alkiyumi Author

04/06/2021 Added

20 Plays

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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.

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[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.

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

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