Rice Leaf disease detection

Project Code :TCMAAN1142

Objective

The objective is to create a mobile application using Jetpack Compose and Kotlin Flow for efficient rice leaf disease detection. This includes implementing user authentication, enabling image upload and real-time analysis capabilities. The app aims to provide farmers with accurate disease identification, timely management recommendations, and educational resources, thereby enhancing agricultural productivity, promoting sustainable farming practices, and facilitating informed decision-making for crop health management.

Abstract

The Rice Leaf Disease Detection App leverages Jetpack Compose and Kotlin Flow to provide a seamless user experience. Users register and log in securely, gaining access to an intuitive interface where they can upload images or capture snapshots of rice leaves. Utilizing advanced image processing algorithms, the system promptly analyzes these images for diseases. Upon detection, detailed results and recommendations are swiftly presented to users, aiding in timely decision-making and proactive disease management. This application not only enhances user convenience but also contributes significantly to agriculture by enabling quick and accurate disease identification, thereby promoting better crop health and yield outcomes.


KEYWORDS: Mobile application, Android.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

H/W CONFIGURATION:

  • Processor                        -    I3/Intel Processor
  • RAM                              -    8 GB
  • Hard Disk                      -    1TB

S/W CONFIGURATION:


  • Operating System                   -   Windows 10          
  • JDK                                         - java
  • Plugin                                     - Kotlin
  • SDK                                        - Android
  • IDE                                         - Android studio
  • Database`                                - Room database

Demo Video