Goal: Developing an AI tool that offers suggestions for optimizing crop yields based on weather patterns, soil conditions, and historical data using ML and a recommendation system.
The key objectives of the artificial intelligence application center around addressing critical challenges in agriculture and the contribution to Sustainable Development Goal 2 (Zero Hunger). The foremost aim is to empower farmers by providing tailored recommendations based on historical data, current weather patterns, and soil conditions, ultimately maximizing crop yields. The application is intended to enhance global food security by assisting farmers in choosing the most suitable crops for their specific conditions, fostering sustainable farming practices, and optimizing the use of resources.
In the end, **XGBoost machine learning algorithm ** was selected for its suitability in handling complex relationships within agricultural data. The model was trained on the preprocessed dataset, allowing it to learn patterns and relationships for accurate crop yield recommendation. Evaluation metrics such as Accuracy, F1, ROC-AUC were utilized to assess model performance.
The model achieved a **95% ** accuracy.
A user-friendly web interface is being created at the moment using Flask, HTML, CSS, and Bootstrap to deploy the AI application and make it accessible to farmers over the internet. (ongoing work)
Further information on the project can be found in Project_Proposal and Concept_Note files.