Overview / Usage

The farmers are facing a lot of challenges and one of the main problems they face is because of plant diseases. Disease fungi take their energy from the plants on which they live. They are responsible for a great deal of damage and are characterized by wilting, scabs, moldy coatings, rusts, blotches, and rotted tissue. Most of the farmers are being effected with huge losses as they don’t find the right solution for a certain disease that their crops have. We want to build a solution for the betterment of the situation that the farmers are facing right now. This research work is designed to help identify some of the more common plant diseases and provides optimal solutions to reduce the fault rate of crops and increase the crop yield. This would lower the crop damage drastically and the consumers can purchase better quality products. We propose to use deep learning techniques to identify diseases in crops in real-time, the same can be made as a mobile app that could help farmers or anyone to detect the diseases for the plants. The system is found to be functionally very stable and works under all ideal conditions

Methodology / Approach

The system is designed in such a way that the users do not have to learn anything new to use. The user needs to take a snap of the affected plant and upload it using our web application. If there is no network connectivity, they can use our stand-alone mobile application which takes the input image, processes with a back-end deep learning image classification model and then extracts other information such as the name and other details about the identified disease to the user without connecting to the internet. We have a database that stores information of all the solutions to the disease which is used for suggesting the user the most optimal resource to be used for reviving the crop. Our Model would identify over 30 diseases caused by various reasons such as pests, fungus, algae, etc. affecting a wide variety of crops including food crops, cash crops, horticulture crops, etc. and presents it to the farmers in an easy-to-comprehend manner, etc.

Technologies Used

Deep Learning, VGG-16

Collaborators