AgroCure

Problem Statement

Farmer's economic growth depends on the quality of the products that they produce, which relies on the plant's growth and the yield they get. Therefore, in the field of agriculture, the detection of disease in plants plays an instrumental role. Plants are highly prone to diseases that affect the growth of the plant which in turn affects the ecology of the farmer. To detect a plant disease at the very initial stage, the use of an automatic disease detection technique is advantageous. The symptoms of plant diseases are conspicuous in different parts of a plant such as leaves, etc. Manual detection of plant disease using leaf images is a tedious job. Hence, it is required to develop computational methods which will make the process of disease detection and classification using leaf images automatic.

What it does

We use the image processing method to detect and identify these diseases and highlight the infected crop area using coordinates from the GPS module attached to the drone.

Challenges we ran into

The components to be used in Drone.
The level at which the drone would be inspecting the minute crop leaves.
Finding an architecture to successfully achieve notable accuracy in R-CNN.

Accomplishments that we're proud of

The idea we proposed and we have successfully applied an advanced neural network to detect healthy and infected crops. The tech that we have used is scalable and prominent to become MVP.

What we learned

We have learnt a lot about the agriculture sector and drone technologies. In addition to that, we have tried and tested the plant disease dataset with a custom advanced neural network. We have learnt a lot about the networking and simulation of drones.