## 6 Feed Monitoring  
Food is essential for animal nutrition since it determines the amount of nutrients available to the animal for health and productivity. Actual or valued food reduces nutrient under- or over-feeding and promotes optimal nutrient utilization [84]. Nutrient deficiency reduces productivity and may have a negative impact on animal health, and overfeeding nutrients raises feed costs, increases environmental nutritional load, and can be harmful or dangerous to one's health [85]. The high-yielding cow breeds of today demand a constant supply of feed and fodder.  
While most bovine feeds for conventional dairy production are purchased from the market, sustainable dairy feed must be grown or purchased locally. Dry fodder can be purchased locally, but green fodder must be farmed on the property. Guinea grass may be produced in desolate rain-fed ground, while high yielding Bajra Napier hybrids can be grown in fertile and well-irrigated land [86]. While poultry and dairy farms are growing across the country, their long-term viability is still a concern.  
Figure 7: IoT enabled system for monitoring milk quality and supply chain.  
Dairy production, in particular, necessitates a huge amount of cattle feed, meal supplements, antibiotics, and other inputs. In the previous few decades, India's dairy farming business has exploded, resulting in the world's largest animal population [87]. After years of consistent success in milk and cattle production, the business is now confronted with a number of obstacles. A chronic scarcity of cattle feed, along with inadequate fodder quality, has become the most significant limitation in Indian dairy farming. The current intensive animal production system places a great emphasis on concentrate feeding, which has raised the cost of milk production and significantly reduced farmer income [87]. The increasing cost of feed ingredients and its seasonal variability also adds to the gravity of the situation.  
The architecture illustrated in Figure 8 empowers the implementation of the edge device with robots for planning a healthy diet and feeding the cattle with respect to the health conditions. The edge mote is placed in the position of the cow where the image captured by the camera module is analyzed by the pre-trained model embedded in it. Further, the results of analytics are communicated to the robots in the premise of dairy cattle through LoRa. Based up on received health information, the robots prepare the food to the dairy cattle. This food is placed in the chamber of mobile robot, that carry the food to the particular cattle position. Mobile robot identifies the ID of the particular cattle that is provided during firmware development in both mobile robot and edge mote. The implementation of robots also ensures the health environment to the cattle, as it is difficult for the cattle, if the farmer/user are unhealthy. The co-processor in the edge mote provides the computing power for analyzing the data with the ML model. Moreover, the information is logged on the digital platform i.e., cloud server through gateway. The integration of robots, ML, IoT and edge devices has facilitated the implementation of intelligent systems to ensure a healthy diet for dairy cows.  
Figure 8: IoT and Robotics Inspired architecture for diet planning and feeding.