Improving crop production using an agro-deep learning framework — Project Overview
| Project Overview |
This B.Tech Agricultural Engineering project is based on the recent AI/ML research direction 'Improving crop production using an agro-deep learning framework'. The project connects agricultural engineering with artificial intelligence, machine learning, deep learning, IoT, computer vision, drone analytics, or RAG-style decision support. Students can use the linked 2023-onward paper/source as the academic base and convert it into an implementation-focused final-year project with sensors, datasets, dashboards, mobile/web interfaces, prediction models, or prototype automation. |
| Research Paper Title |
Improving crop production using an agro-deep learning framework |
| Research Paper / PDF Link |
Open Paper / PDF |
| Year |
2024 |
| Project Area |
Soil and Crop Monitoring |
| Project Type |
Agro Deep Learning |
| Required Tools / Software |
Python, Pandas, Scikit-learn, TensorFlow/PyTorch, Streamlit/Flask, IoT dataset/sensor data |
| Main Features / Working Principle |
Use deep learning for crop monitoring and production improvement |
| Expected Output |
A crop monitoring and production prediction dashboard |
| Possible Add-ons |
Add weather API and advisory module |
| Get Help |
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This B.Tech agricultural engineering project resource helps students connect a recent AI-based research direction with a practical implementation plan, tools, expected output, and possible extensions.