"The ClearFarm platform was designed to collect sensor-based animal welfare information from dairy cattle and pigs on farms located in different countries. This information is then transmitted to our computer servers in the cloud for storage and analysis. The data is then processed for end-users, such as farmers, to help them improve welfare monitoring, disease detection and decision making, and for consumers to help them take well informed decisions about the animal products they want to buy. The platform consists of two components: the back-end and the front-end.
Back-end
The data is gathered in dairy and pig farms using Precision Livestock Farming (PLF) technologies, such as collars, rumen bolus, GPS, Copeeks, and SoundTalks. For dairy cattle, welfare data is collected based on their activities (e.g., time spent on eating, walking, ruminating, etc.), health status (e.g., cleanliness of udders, legs, diseases) and milk production. For pigs, data come from a wide range of sources: 3D cameras or automatic feeders, measurements of ambient temperature, humidity, CO2 and NH3 level in the barn, among others. Additional data was obtained from veterinary records and visits to be used as reference values. Although this data is used to calculate the animal welfare indices, it is not as regular as the data generated by the PLF.
All the information gathered is transferred via internet to the platform’s data centre for animal welfare analysis and welfare index calculations. Depending on the technology used, the downloads from these farms can be made in different routines (e.g., daily, hourly, etc.)
The information is saved in MongoDB, an open-source database that does not follow traditional relational principles. Every data entry is in the form of a document, which can have several fields and is adaptable in size. Unlike other types of databases, the data in MongoDB doesn’t require a specific structure. Due to its ability to scale and adapt, we chose it as our database and installed it on a server dedicated primarily to its operation. This server is also utilised for the routine gathering of data from various sources using both PLF and non-PLF technologies.
To make back-end web development easier, we use the Flask framework. Flask is based on Python, a programming language that reduces development time and is easy to use and implement.
A additional machine learning server is set up to connect to the back-end, which usually requires more processing power for intensive calculations. This server is loaded with computer programs that calculate welfare indices using various algorithms developed in the ClearFarm project.
Front-end
The web server is the back-end that connects to the two servers mentioned above. It provides various interfaces in response to requests from end-users of the platform application. Users can only access pages created specifically for them on the web browser after logging in.
There can be as many front-ends as requested by different users: farmers, consumers, processors, retailers, food service providers, feed manufacturers. Here are two examples of front-ends created for dairy cows:
(1) Farmers: The farmer interface includes information on welfare scores (see Figure 1). It shows the animals’ time spent on feeding, health, housing, and global scores (an average calculation of all indicators), which range from 1 to 10. Users can also select a range of dates to view the scores. A colour scheme (red, yellow, green) is used to indicate the welfare of the cows (at individual level, and also at farm level). In addition, the application allows users to set up alerts if the scores fall below a certain threshold. The application also allows farmers to see the likelihood of individual animals suffering from diseases, such as mastitis and acidosis (see Figure 2).
(2) Consumers: By using the app on their mobile phones, consumers can scan the QR code on the product to find out what the welfare scores are for feeding, health, housing and global (see Figure 3). This will help them know that these products are from farms that prioritize animal welfare and enable them to check the animal welfare status on the farm. The scores range from 0 to 10, with 10 being the highest positive score and 0 being the least.
In summary, the ClearFarm platform has proven to be functional in providing users with information on farm animal welfare. It helps farmers to be aware of diseases and health issues that their animals may be suffering from. Other users, such as consumers, have the opportunity to be better informed about the welfare aspects of the animal products they buy."