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Coffee Leaf Disease Detection With simple CNN Flask App in Python Projects
र 1000
Coffee Leaf Disease Detection With simple CNN Flask App in Python Projects
Technical Details
Domain : Python
Database : Sqlite
Tools : Anaconda
Run Tools: VS Code
Database : Sqlite
Tools : Anaconda
Run Tools: VS Code
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About This Product
Coffee Leaf Disease Detection With simple CNN Flask App in Python Projects
Abstract
Coffee leaf diseases, caused by pathogens such as fungi and bacteria, can significantly reduce coffee yield and quality. Early detection of these diseases is crucial for effective crop management and prevention of large-scale losses. This project, Coffee Leaf Disease Detection with Simple CNN Flask App in Python, implements a lightweight Convolutional Neural Network (CNN) model to classify coffee leaf images as healthy or diseased. Using Python libraries such as TensorFlow/Keras, OpenCV, NumPy, Pandas, and Flask, the system preprocesses leaf images, trains the CNN for accurate disease recognition, and integrates the trained model into a Flask web application. Users can upload coffee leaf images to the app and receive real-time predictions, providing an accessible solution for farmers and agricultural experts to monitor plant health.
Existing System
Traditional coffee disease detection relies on manual inspection by farmers or agricultural specialists, which is time-consuming, error-prone, and requires expertise. Some automated solutions use classical machine learning algorithms with handcrafted features like color, texture, and shape, but these approaches often lack scalability and robustness when applied to diverse datasets. Additionally, many existing systems do not provide user-friendly deployment, limiting accessibility for end users such as small-scale farmers.
Proposed System
The proposed system introduces a simple CNN model for coffee leaf disease detection, integrated with a Flask web application for real-time predictions. The workflow includes image preprocessing (resizing, normalization, and augmentation), CNN training to automatically extract hierarchical features, and classification of leaves into healthy or diseased categories. The Flask app allows users to upload images through a browser interface and receive instant predictions. Compared to existing methods, this approach provides higher accuracy, automation, scalability, and ease of access, empowering farmers to detect diseases early and take timely action to protect coffee crops.
Abstract
Coffee leaf diseases, caused by pathogens such as fungi and bacteria, can significantly reduce coffee yield and quality. Early detection of these diseases is crucial for effective crop management and prevention of large-scale losses. This project, Coffee Leaf Disease Detection with Simple CNN Flask App in Python, implements a lightweight Convolutional Neural Network (CNN) model to classify coffee leaf images as healthy or diseased. Using Python libraries such as TensorFlow/Keras, OpenCV, NumPy, Pandas, and Flask, the system preprocesses leaf images, trains the CNN for accurate disease recognition, and integrates the trained model into a Flask web application. Users can upload coffee leaf images to the app and receive real-time predictions, providing an accessible solution for farmers and agricultural experts to monitor plant health.
Existing System
Traditional coffee disease detection relies on manual inspection by farmers or agricultural specialists, which is time-consuming, error-prone, and requires expertise. Some automated solutions use classical machine learning algorithms with handcrafted features like color, texture, and shape, but these approaches often lack scalability and robustness when applied to diverse datasets. Additionally, many existing systems do not provide user-friendly deployment, limiting accessibility for end users such as small-scale farmers.
Proposed System
The proposed system introduces a simple CNN model for coffee leaf disease detection, integrated with a Flask web application for real-time predictions. The workflow includes image preprocessing (resizing, normalization, and augmentation), CNN training to automatically extract hierarchical features, and classification of leaves into healthy or diseased categories. The Flask app allows users to upload images through a browser interface and receive instant predictions. Compared to existing methods, this approach provides higher accuracy, automation, scalability, and ease of access, empowering farmers to detect diseases early and take timely action to protect coffee crops.
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