Fruit Disease Detection Android App

Fruit Disease Detection Android App

Author Name : Naitik Ajay Soni, Vedant Jagdish Giri, Shrikrushna Suhas Bagal, Rahul Ganesh Chinchalkar

ABSTRACT

The prevalence of diseases in fruit crops poses significant challenges to agricultural productivity and food security. Timely detection and accurate diagnosis of these diseases are crucial for effective management and prevention of crop losses. In recent years, advancements in mobile technology have opened up new possibilities for rapid disease identification and monitoring. This abstract presents an Android app designed to address the need for efficient fruit disease detection and diagnosis. The Fruit Disease Detection app leverages the power of image recognition algorithms and machine learning techniques to enable farmers, agronomists, and researchers to identify and diagnose diseases affecting various fruit crops. The app's user-friendly interface allows users to capture images of fruit samples exhibiting disease symptoms using the built-in camera of their Android devices. The captured images are then processed and analyzed using sophisticated computer vision algorithms. The core of the app is a trained deep learning model that has been trained on a comprehensive dataset of fruit disease images. The model has the ability to recognize and classify numerous common fruit diseases accurately. By comparing the captured images with the pre-trained model, the app rapidly determines the presence of diseases, providing users with real-time diagnostic results.

Keywords: Fruit disease detection, Android app, image recognition, machine learning, deep learning, crop management, agricultural technology.