Desease Detection Using Machine Learning

Farmers are under increasing strain on all fronts: they must supply expanding food demand while working with less space, adapting to climate
change, and using fewer chemical treatments to comply with rigorous laws. It's a global issue, and it's a key component of the 2030 Sustainable Development Goals, which encourage more sustainable agricultural methods and beyond.

The expansive layouts of capacity and type of anything are discussed in a reasonable plan, which is the first stage of the
plan interaction. Collaborations, interactions, cycles, and systems are all part of the plan. It entails an understanding of people's needs and how to address them with products, services, and cycles. Idea pictures and models are common old rarities of decent
design.

Implementation

Computer vision implemented with drone robotics provides the unique opportunity to increase Agriculture efficiency. Automated search algorithms could identify density disparities in forest patches and provide management personally with efficient time and warming such that greater potential damage could be minimized.

 The Proposed System will use drone-based mapping and analytics for agriculture aided by GPS, Sensors and Algorithms based on using MATLAB, Python with a few more additions in it.

The Algorithm and system Developed will then be deployed in a virtual simulation tool to understand and verify the system’s capabilities
and improve them using Machine Learning and other data as ground truth information to understand its real-life implications.