Cropable - The Crop Disease Detection WebApp

SCOPE Vellore Institute of Technology Vellore, India

^* Corresponding author: shashwat.kumar2@gmail.comarchisa26@gmail.comdishagoyal2001@gmail.comanannya.anannya02@gmail.commaniktaliariddhi@gmail.comdeepa.k@vit.ac.in1

Abstract

AbstractAccording to estimates, every year 10% of global production, goes waste due to pests and crop pathogens. For instance, India is a leading producer of many crops, including wheat, rice, lentils, sugarcane, and cotton. But a majority of the farmers are unable to detect whether a crop is infected or not simply by looking at it. As crop pathogens develop greater resistance to fungicides and pesticides, there is an urgent need to find new antifungal compounds to effectively combat them, which over time are rendered useless as the pathogens again develop resistance to these compounds. Thus, the food security of any country is always at risk due to the vulnerability of the current agricultural systems to climate, pests, pathogens, and associated diseases. To solve this problem, we have developed Cropable, The Crop Protection App. In the proposed work, we have used Deep Convolution Neural Networks( CNN) models to detect the disease and further created a web app using flask. Cropable is an Artificially Intelligent Web Application that can help to identify whether the crop is infected or not. We also provide farmers with a treatment for the detected disease, which not only helps them in identifying a disease but also assists them in solving it.