Automatic leaf Infection Identification

Project Title

Automatic leaf Infection Identification

Project Area of Specialization Artificial IntelligenceProject Summary

Identification of plant disease is usually done through visual inspection or during laboratory examination which causes delays resulting in yield loss by the time identification is complete. 

On the other hand, complex deep learning models perform the task with reasonable performance but due to their large size and high computational requirements, they are not suited to mobile and handheld devices.

Project Objectives

 MAIN OBJECTIVE IS TO FIND A DISEASE  ANYWHERE FROM OHONE YOU WILL NOT REQUIRED AN EXPERT .The proposed model of disease identification provides an accuracy of 98.79% with a standard deviation of 0.57 on 10-fold cross validation. 

Project Implementation Method

Image acquisition is the very first step that requires capturing an image with the help of a digital camera.

Pre-processing of input image to improve the quality of image and to remove the undesired distortion from the image.

Mostly green colored pixels, in this step, are masked In the infected clusters, inside the boundaries,

 remove the masked cells. 

 Obtain the useful segments to classify the leaf diseases

Benefits of the Project

In this we not pay extra payment for disease research .In this farmer can easily treat crops from our app .It is less expensive 

Technical Details of Final Deliverable

To improve recognition rate of final classification process hybrid algorithms like Artificial Neural Network

s future enhancement of the project is to develop the open multimedia (Audio/Video) about the diseases and their solution automatically once the disease is detected.

Dataset 

Laptop with 8 gb RAM 

Android Studio

Final Deliverable of the Project Software SystemCore Industry AgricultureOther Industries Others Core Technology Artificial Intelligence(AI)Other Technologies Big DataSustainable Development Goals Decent Work and Economic GrowthRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	5000
Playstore upload fee	Miscellaneous	1	5000	5000