AGRI.AI is a groundbreaking initiative that harnesses the power of machine learning to revolutionize agriculture practices.
ARGI.AI is an innovative project that harnesses the power of Machine Learning (ML) and Deep Learning (DL) to provide valuable insights and recommendations to the agricultural sector. It offers three core applications, including Crop Recommendation, Fertilizer Suggestion, and Disease Detection, aimed at assisting farmers and agricultural enthusiasts in optimizing their crop production.
Agriculture plays a pivotal role in the economic growth of countries worldwide. In countries like India, a significant portion of the population relies on agriculture for their livelihood. ARGI.AI is driven by the motivation to modernize and enhance agriculture practices through the integration of cutting-edge technologies like ML and DL.
Crop Recommendation: ARGI.AI allows users to input soil data, including nutrient values, state, and city information. The system leverages this data to predict the most suitable crop for cultivation under the given conditions.
Fertilizer Suggestion: By providing soil nutrient content and specifying the crop to be grown, users receive precise recommendations on which nutrients the soil lacks or has in excess. These recommendations enable users to make informed decisions about fertilizer usage.
Disease Detection: ARGI.AI offers a disease detection system where users can upload images of plant leaves. The system not only identifies the disease but also provides valuable information about it and suggests measures for prevention and treatment.
- Custom-built datasets for crop recommendation and fertilizer suggestions.
- External datasets for disease detection.
The code for the project is available in Kaggle Notebooks for both crop recommendation and disease detection, facilitating transparency and reproducibility.
ARGI.AI is deployed on Vercel, making it easily accessible online. Please note that the website may take a moment to load if the server is in hibernation.
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Crop Recommendation System: Users are required to input soil nutrient values, state, and city details. The N-P-K (Nitrogen-Phosphorus-Potassium) values should be entered as ratios. It's advisable to use common city names for accurate weather data retrieval.
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Fertilizer Suggestion System: This system mandates users to provide soil nutrient contents and specify the crop to be grown. ARGI.AI then generates recommendations based on soil nutrient deficiencies or excesses.
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Disease Detection System: Users can upload images of plant leaves. ARGI.AI identifies the crop type, checks for diseases, and offers insights into disease causes, prevention, and treatment. Please note that the system currently supports specific crops.
ARGI.AI provides demonstrations for the Crop Recommendation, Fertilizer Suggestion, and Disease Detection systems, offering users a glimpse of its capabilities. Explore the ARGI.AI Demo to see the Crop Recommendation, Fertilizer Suggestion, and Disease Detection systems in action.
Users are welcome to further develop and enhance ARGI.AI, with attribution to the original source and a mention of the repository link in any reports or derivative works being highly appreciated.
Several areas for improvement are identified for ARGI.AI:
- Cleaning up and optimizing CSS code for improved user experience.
- Enhancing the frontend design to make it more visually appealing.
- Collecting additional data manually through web scraping to enhance system accuracy.
- Expanding the database of plant images for more robust and generalized disease detection.
- Implementing modularized code for better maintainability.
ARGI.AI drew inspiration from a GitHub repository and serves as an extended version of the referenced project.
For any inquiries or contributions, please feel free to contact the project creator via email or on LinkedIn.
ARGI.AI is a proof-of-concept project, and the data used comes with no guarantees. It should not be used for making farming decisions, and the creator is not responsible for any consequences resulting from its use.