Crop Recommendation System

Overview

Crop Recommendation System: Data-Driven Agriculture. A solution designed to provide farmers with personalized recommendations for crop selection based on data analysis and machine learning.

Crop Recommendation Dataset: Kaggle

Data Collection and Preprocessing

• Data Gathering -- We collected comprehensive datasets from Kaggle, including soil parameters and crop yields.
• Cleaning and Preprocessing -- Python libraries like Pandas and NumPy were used to clean and structure the data.
• Feature Engineering -- We engineered relevant features to enhance the predictive power of our models.

Machine Learning Models

• Content-Based Filtering -- This approach recommends crops based on soil and climate attributes using cosine similarity.
• Collaborative Filtering -- We implemented matrix factorization using SVD to predict crop-environment interactions.
• Performance Evaluation -- Our models achieved high accuracy, with precision and recall exceeding 0.95 across classes.

Model Training and Validation

• Feature Transformation -- We applied encoding and scaling techniques to prepare our features for modeling.
• Train-Test Split -- Data was split into training, testing, and validation sets to ensure robust evaluation.
• Iterative Refinement -- Multiple iterations of training and validation improved our model's performance and generalization.

MLOps Integration

• Model Versioning -- MLflow tracks different versions of our recommendation models, ensuring reproducibility.
• Performance Monitoring -- Continuous monitoring of model metrics helps identify drift and trigger retraining.
• Deployment Pipeline -- Automated deployment pipelines streamline the process of updating models in production.

Advanced Techniques - GANs

• Synthetic Data Generation -- GANs create realistic synthetic crop profiles to augment our training data.
• Enhanced Recommendations -- GAN-generated profiles improve the robustness of our recommendation system.
• Edge Case Handling -- Synthetic data helps our model handle rare or unseen crop-environment combinations.

Data Augmentation with GANs

• Augmented Dataset Creation -- Utilize GANs to generate diverse and realistic synthetic profiles for expanding our training dataset.
• Recommendation System Integration -- Incorporate synthetic data seamlessly into our recommendation system to enhance predictive capabilities.

Contributors

Mostafa Mohamed Youssef | Hanin Essam Sayed | Abdelrhman Walaa | Abdelrahman Osama Nabih