Leaf disease detection is a critical issue for farmers and agriculturalists. The detection of leaf diseases at an early stage can help prevent the spread of diseases and ensure a better yield. However, manual detection of leaf diseases is time-consuming and often inaccurate. With the advancement of technology, machine learning, and computer vision techniques can be used to develop automated solutions for leaf disease detection.
In this article, we will discuss the development of a Leaf Disease Detection Flask App that uses a deep learning model to automatically detect the presence of leaf diseases.
Checkout the video here – https://youtu.be/GglezQhUDVk
- HTML, CSS for frontend
- Flask(Python) for backend
Introduction to Flask
Flask is a web application framework written in Python. It is lightweight and easy to use, making it a popular choice for developing web applications. Flask provides tools and libraries to handle web requests and responses, making it easy to develop RESTful APIs.
Leaf Disease Detection Dataset
Sneak Peek at our Leaf Disease Detection App…
The Deep Learning Model
The Leaf Disease Detection Flask App uses a deep learning model to detect the presence of leaf diseases. The model is trained on a dataset of images of healthy and diseased leaves. The model uses a convolutional neural network (CNN) to extract features from the images and classify them as healthy or diseased.
The deep learning model is implemented using Keras, a high-level neural networks API written in Python. Keras provides a simple and intuitive interface for building and training deep learning models.
The Flask App
The Leaf Disease Detection Flask App is a web application that provides an interface for users to upload images of leaves and receive a prediction of whether the leaves are healthy or diseased. The Flask App uses the deep learning model to make predictions on the uploaded images.
The Flask App consists of two main components:
1. The Web Interface
2. The Prediction Engine
The prediction engine is responsible for processing the uploaded image and making a prediction on whether the leaves are healthy or diseased. The prediction engine uses the deep learning model to make predictions on the uploaded image.
The prediction engine is built using Flask and Keras. The Flask application receives the uploaded image and passes it to the prediction engine. The prediction engine processes the image using the deep learning model and returns a prediction to the Flask application. The Flask application then displays the prediction to the user.
Working of our Leaf Disease Detection App…
About Training Code and App
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The Leaf Disease Detection Flask App provides an automated solution for the detection of leaf diseases. The app uses a deep learning model to make predictions on whether the leaves are healthy or diseased. The app is built using Flask, a lightweight web application framework, and Keras, a high-level neural networks API. The app provides a simple and intuitive interface for users to upload images of leaves and receive predictions on whether the leaves are healthy or diseased.
The Leaf Disease Detection Flask App has the potential to improve the efficiency of leaf disease detection and improve crop yields for farmers and agriculturalists. With further development, the app can be expanded to include more types of leaf diseases and additional features to provide more comprehensive information on leaf health.
So this is all for this blog folks. Thanks for reading it and I hope you are taking something with you after reading this and till the next time …
Read my last blog – URL Shortener using Flask – with Source Code
Check out my other machine learning projects, deep learning projects, computer vision projects, NLP projects, and Flask projects at machinelearningprojects.net.