Do you know Data Analytics plays a prominent role in Dairy production rates? Use of data analytics not only yield financial benefits but for improving the health of animals thereby improving milk yield. Integrated dairy farm of Mid Valley Foods demonstrated this. All the records about health, breeding, individual cow’s milk production, changes in milk production are captured and used for analysis.
Blue Collar Sensor Tag:
Data Analytics provides the opportunity for constant monitoring of cows. A blue-collar sensor tag with a unique identification tag is hanged around the neck of the cow. This tags continuously monitor the well being of cows and health condition. As a part of the experiment, a blue-collar sensor is hanging on to 400 plus cows which include Jersey, Ongole, Gir, Holstein, Ghashian. All these cows are allowed to gaze on 125 acres land near the foothill of Panchali malai in Arumbavur village of Tamil Nadu. All the information about the cow is carried to the central server with the help of these tags. Apart from this, a veterinarian always monitors the cows for taking appropriate actions.
If a cow is ready to inseminate an alert is being sent to the server. In the past veterinaries used to touch the cow’s nose for detecting the readiness of insemination. But at present after receiving of a signal from the server, veterinarian artificially inseminate the cow. If a particular cow is not yielding and pastured, it will be detected with the help of a software alert.
Milk yield will vary from 8 to 32 instantly by providing good living conditions, providing healthy food and using technology. Nowadays dairy farming is all about analytics, data and predictive analytics. Likely yield of each and every cow for the next five years can be forecasted by software with the help of cow’s behavioural pattern.
At present milk is sold in Chennai through the application which charges 80rs/- per litre. As soon as the milk is delivered, the delivery person will be able to scan the milk box with QR code for getting exact information about the delivery with exact timing.400 litre of milk is to be delivered every day through this app.
Improving dairy production:
Data Analytics plays a major role after the production of milk and other dairy products. Robotic milking system will help to take much of the milking work and milk analysis is done. Each product is assigned with barcodes and log ID is updated into the database. In case of any defective product in quality check, the database is to be accessed for information which led to the effect.
Data supports the idea that precision dairy farming practices are valuable. Cargill Inc., one of the largest agricultural companies in the world, reports an 11.7 per cent increase in milk production on Italian farms using its Dairy Intelligent application. Data monitoring also allows Chitale Dairy based in India to produce more than five litres of milk per animal.
By 2050 the precision agriculture industry is expected to be worth around 240$. For making the data and insight accessible to farmers it is working to improve the associated technologies. Machine learning is adopted into smart agriculture systems which mean accuracy is improved when used more.
Herd location tracking:
Cow’s movement can be tracked with the help of sensors. These sensors will also give information regarding the health of animals. The location tracking system is developed by an Israeli company which is called cattle watch. It is used to pinpoint the individual location of animals and regularly counts the herd. For ensuring cows to stay in correct field geofences are being set up which helps to send out drones for checking the herd. In case of any theft or sense of possible predator to attack cows, the system will give an alert.
Monitoring Animal Health:
Farm operations can be done with the help of data collected from sensors. Usually, the sensors are in the form of internet-connected tags and smart collars which are connected to cow’s ears. Even pills which stay inside the rumen which is the largest of cow’s stomach after cow’s swallow them.
Cow’s location and health can be collected with the help of this device which is then transmitted through the cloud. Farmers can access the data through smartphones and tablets. These devices help to ensure that cows are getting sufficient nutrition.
Not only cows get benefitted with this data analytics, but also farmers can be beneficial from it.
In dairy farming, information is accumulated through different kinds of detectors based on creatures (e.g., fever, accelerometer, feed consumption, weight), structures (e.g., temperatures, humidity), or trapping robots (e.g., quantity, milk makeup ). These detectors are typical in the present farms and ease action coverage.
On the other hand, the degree of information analysis provided doesn’t permit the farmer to choose like inseminate or provide medical therapy. Indeed, alarms from the current apparatus often include many false positives to be dependable. The degree of detail will be too large to decrease the workload. By way of instance, false positives for illness detection might lead to additional medical therapy and monitoring expenses, the reverse of the primary intent. Another vital management facet is a heating discovery for insemination functions. It’s essential since a cow has to have a calf to start lactating and is predicted to give birth once a year later to keep a specific degree of creation.
Present-day techniques utilized by animal scientists concentrate mainly on mono-sensor approaches (e.g., accelerometer) that tend to be inadequate to decrease false positives and facilitate decision-making. There exist practical mono-sensor approaches like progesterone dosage from the milk. However, such technology remains overly expensive for a vast execution in farming.
Our purpose is to utilize widespread detectors to present dependable recommendations to ease farmers’ decisions regarding insemination, illness detection, and creature choice. Our intent is to unite standard sensors in milk control (e.g., accelerometer and temperature) to diagnose occasions and elaborate upon those recommendations. This complicated structure demands the usage of machine learning procedures. Because of an experimental plantation, branded data can be obtained. The challenge will be to design new calculations that consider information heterogeneity, both for their character (e.g., fever, weight, feed consumption ) and time scales (e.g., every five minutes, two times each day, each day ).
Really, our strategy will depend upon multivariate time series classification without a present method is intended to effectively handle data using different time scales for each measurement. To begin with, most approaches are window. They extract attributes with the exact same temporal granularity for each factor. Afterward, one of the methods unique to every element possessions, no comprehensive and practical approach to characterize memorable occasions, has surfaced.