The motive of the project is to develop a under budget system that automates the process of farming by interfacing cutting edge technologies like Drones and ‘NDVI’ to improve the level of productivity in Agriculture. Humans and satellites have a hard time beating a drone’s eye for detail in scanning farming systems from above. Flying below the clouds, collecting and sending images in almost real-time, unmanned aerial vehicles (UAVs) gained ground quickly in agriculture in the last decade as part of so-called precision agriculture. Among their wide range of applications, they can help farmers check crops’ health, track livestock, plan fertilization, assess damages, and map fields at high-resolution. But all this comes with a cost. Currently the models of drones used for such applications cost extensively higher, which makes it unfeasible for the small farmers, especially in India. The projects aim on designing a drone system that can work on both autonomous as well as manual mode and perform mapping, inspecting and spraying processes with efficiency accuracy and considerably good speed which can help boosting the profits of the farmers with large as well as small agricultural lands. As there are lots of restrictions on Drone flight in India. The project aims to follow and implements all the norms stated by the government. (e.g., Permission before flight). The project aims towards overcoming all the above-mentioned problems by automating the procedure.

No abstract

During an epidemic, the information available to individuals in the society deeply influences their belief of the epidemic spread, and consequently the preventive measures they take to stay safe from the infection. In this paper, we develop a scalable framework for ascertaining the optimal information disclosure a government must make to individuals in a networked society for the purpose of epidemic containment. This problem of information design problem is complicated by the heterogeneous nature of the society, the positive externalities faced by individuals, and the variety in the public response to such disclosures. We use a networked public goods model to capture the underlying societal structure. Our first main result is a structural decomposition of the government's objectives into two independent components -a component dependent on the utility function of individuals, and another dependent on properties of the underlying network. Since the network dependent term in this decomposition is unaffected by the signals sent by the government, this characterization simplifies the problem of finding the optimal information disclosure policies. We find explicit conditions, in terms of the risk aversion and prudence, under which no disclosure, full disclosure, exaggeration and downplay are the optimal policies. The structural decomposition results are also helpful in studying other forms of interventions like incentive design and network design.