Weight estimation and crop uniformity are two of the more difficult metrics to monitor in poultry production. Remote sensing technology uses a combination of image processing and machine learning to deliver touchless, accurate and representative data on bird heath and production practices.
“We are all aware that our main goal is to produce a well-bred, uniform crop at the least possible cost. We know that the more birds that are in spec, the better the return. We know that, by tracking uniformity, we can potentially track early onset of disease challenges to the birds and better understand the impact of management and environmental practices,” Claire Lewis, the CEO of Pondus Technology said October 20 during the 2020 Virtual Poultry Tech Summit.
“As the pressure grows to grow more and more chickens at faster rates and volumes never seen before, we need to rest assured that we can monitor the health status of our birds from afar.”
Challenges to weight estimations
Labor challenges have been exacerbated by COVID-19, but the truth is that the poultry issue has faced increased pressure on labor and labor resources in recent year. This means it can be difficult to maintain the staffing levels necessary to weigh birds and calibrate weights.
“We also have biosecurity challenges from either handling the birds, transfer from equipment between the sheds or from external disease challenges which prevent us from weighing birds as much as we would like,” Lewis explained.
Image processing to the rescue
As a result, many poultry producers have begun exploring automated solutions.
“We gather the information through image processing. The camera takes images and we then estimate the weight of the birds in the image. Those weights are fed into the cloud, where they are further analyzed and distributed to the user’s app,” Lewis said.
These results are ultimately compared with the finish weights of the birds at the processing plants to help increase the accuracy of the program.
The technology uses off the shelf equipment, which helps the company keep things “affordable, easy-to-replace and highly accessible,” said Lewis.
It has been tested in three different shed types with five different bird breeds, with an error rate of less than 10%.
“We will continue to refine our technology through exposing it to more shed types, more bird breeds and different environments with the ultimate goal of providing representative, accurate crop uniformity on demand,” Lewis said.
The company is currently on the lookout for early adopters and people to invest in the technology.
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