Plant leaves Disease Detection

The project  present  a mobile-based system for detecting plant leaf diseases using real-time deep learning (DL).Specifically, we've developed a system  that work offline on the user's mobile devices. The user interface is developed as an Android mobile app, allowing farmers to captur

Project Title

Plant leaves Disease Detection

Project Area of Specialization

Artificial Intelligence

Project Summary

The project  present  a mobile-based system for detecting plant leaf diseases using real-time deep learning (DL).Specifically, we've developed a system  that work offline on the user's mobile devices. The user interface is developed as an Android mobile app, allowing farmers to capture a photo of the infected plant leaves and can use with high accuracy to identify different types of plant diseases. This app will also recommend how to control the diseases on the crop. The developed system uses Convolutional Neural Networks (CNN) as a basic deep learning engine to classify 2 different crop diseases from cotton and mango.  We collected an imagery dataset containing 4,500 images of plant leaves of healthy and infected plants for training, validating, and testing the CNN model.

Project Objectives

The main objective of this project is to detect the plant diseases of different crops using mobile phones at earlier stages of the disease infection.

The other objective of developing this application to help farmers in identification of different plant diseases and recommend its control.

Project Implementation Method

Python:

Python is a high-level object-oriented programming language that was created by Guido van Rossum. It is also called general-purpose programming language in 1991 and was further developed by the Python Software Foundation. 

It was designed with an emphasis on code readability, and its syntax allows programmers to express their concepts in fewer lines of code.

Python is a programming language that lets you work quickly and integrate systems more efficiently.

 

Machine Learning:

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

Machine learning algorithms are often categorized as supervised or unsupervised.
 

Deep Learning:

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans.

In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.

Keras:

Keras is an open-source library of neural network components written in Python. Keras is capable of running atop TensorFlow, Theano, PlaidML, and others. The library was developed to be modular and user-friendly, however, it initially began as part of a research project for the Open-ended Neuro-Electronic Intelligent Operating System.

CNN:

A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data.

CNNs are powerful image processing, artificial intelligence (AI) that use deep learning to perform both generative and descriptive tasks, often using machine vison that includes image and video recognition, along with recommender systems and natural language processing (NLP).

Flutter:

Flutter is Google's free and open-source UI framework for creating native mobile applications. Released in 2017, Flutter allows developers to build mobile applications for both iOS and Android with a single codebase and programming language. This capability makes building iOS and Android apps simpler and faster.

Benefits of the Project

Plant diseases are one of the grand challenges that face the agriculture sector worldwide. One third of crop production is lost every year due to crop diseases.

Diagnosis of crop disease is difficult for limited resources farmers if it is done through theoretical observation of the symptoms of plant leaves.

It is important that farmers are aware of such challenges in their operations in a timely manner. Nevertheless, it will be very helpful for agricultural producers to have easy access to the available technology so that they can be instructed on how to deal with each of these hazards to agricultural production so that the crop Increase production and operational profits.

Technical Details of Final Deliverable

Agriculture faces many problems but diseases in plant is a major thorn in their lives worldwide. Due to scarce wherewithals, farmers are unable to diagnose crop diseases. Making farmers privy to such problems, and disseminating crop disease related knowledge to farmers is the vision of our project. We hope through this app we can make farmers and common people literate in this discourse. This will ultimately help individuals to make most of their crops and prosper. There is much which needs to be improved and enhanced in this, therefore, we hope to make our application error free and simplistic for everyone to use.

This mobile application enables farmers to capture a photo of the infected plant leaves and the application automatically identifies the disease on the leave and also suggested the control methods of that disease. In this way a farmer can easily identify and control the disease at earlier stages of the infection.

Final Deliverable of the Project

Software System

Core Industry

Agriculture

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

No Poverty, Zero Hunger

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com