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NEWS BRIEF demonstrates how NLP techniques can be applied to extract valuable structured data from raw, unstructured news articles. By automating the extraction process, businesses can save time and resources while gaining deeper insights into ongoing events and trends.
The project achieved over 95% accuracy in detecting wheat crop diseases like septoria and stripe rust using a CNN model. Integrated into a React Native app, it allows farmers to capture or upload images for instant diagnosis and recommendations.
Our project approach drives measurable results, supports marketers in achieving their goals, and addresses customer needs within their budget..
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In this project, we implemented a system for Information Extraction (IE) from raw news articles. The system automatically extracts structured data such as entities, relationships, events, and metadata from unstructured text, making it easier for businesses to analyze and utilize news content.
Objective
The goal of this project is to extract key pieces of information from news articles, such as:
This data is utilized for:
Tools & Technologies Used
Key Features
Extract additional metadata such as the news source, category, importance rating, and timeliness rating.
The project is about detecting different diseases like septoria or strip rust to help the former in detecting the diseases. The CNN model is trained on augmented images of wheat crop leaves and then tested. The accuracy was more than 95% and finalized model was integrated in frontend using react native.
This data is utilized for:
Tools & Technologies Used
Results and Deliverables
The system provides an interactive interface. This will help the former to use the front-end to upload or take the image using camera, including:
Future Improvements
Conclusion
The project achieved over 95% accuracy in detecting wheat crop diseases like septoria and stripe rust using a CNN model. Integrated into a React Native app, it allows farmers to capture or upload images for instant diagnosis and recommendations. This solution offers a practical step toward smarter, tech-enabled agriculture, with future potential for real-time detection and multi-crop support.
