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Crop Recommendation Model

🎯 AIM

It is an AI-powered Crop Recommendation System that helps farmers and agricultural stakeholders determine the most suitable crops for cultivation based on environmental conditions. The system uses machine learning models integrated with Flask to analyze key parameters and suggest the best crop to grow in a given region.

https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset/data

📓 NOTEBOOK

https://www.kaggle.com/code/kashishkhurana1204/crop-recommendation-system

Kaggle Notebook

⚙️ TECH STACK

Category Technologies
Languages Python
Libraries/Frameworks Pandas, Numpy, Matplotlib, Scikit-learn
Tools Github, Jupyter, VS Code

📝 DESCRIPTION

What is the requirement of the project?

  • To provide accurate crop recommendations based on environmental conditions.
  • To assist farmers in maximizing yield and efficiency.
How is it beneficial and used?
  • Helps in optimizing agricultural planning.
  • Reduces trial-and-error farming practices.
How did you start approaching this project? (Initial thoughts and planning)

  • Initial thoughts : The goal is to help farmers determine the most suitable crops based on their field’s environmental conditions.

  • Dataset Selection : I searched for relevant datasets on Kaggle that include soil properties, weather conditions, and nutrient levels such as nitrogen (N), phosphorus (P), and potassium (K).

  • Initial Data Exploration : I analyzed the dataset structure to understand key attributes like soil pH, humidity, rainfall, and nutrient values, which directly impact crop suitability.

  • Feature Analysis : Studied how different environmental factors influence crop growth and identified the most significant parameters for prediction.

  • Model Selection & Implementation : Researched various ML models and implemented algorithms like Naïve Bayes, Decision Trees, and Random Forest to predict the best-suited crops.

Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.).

🔍 EXPLANATION

DATASET OVERVIEW & FEATURE DETAILS

📂 dataset.csv | Feature| Description | Data Type | |------------|-----------------|----------------| | Soil_pH | Soil pH level | float | | Humidity | Humidity level | float | | Rainfall | Rainfall amount | float | | N | Nitrogen level | int64 | | P | Phosphorus level| int64 | | K | Potassium level | int64 | |Temperature | Temperature | float | | crop | Recommended crop| categorical |

🛤 PROJECT WORKFLOW

  graph 
  Start -->|No| End;
  Start -->|Yes| Import_Libraries --> Load_Dataset --> Data_Cleaning --> Feature_Selection --> Train_Test_Split --> Define_Models;
  Define_Models --> Train_Models --> Evaluate_Models --> Save_Best_Model --> Develop_Flask_API --> Deploy_Application --> Conclusion;
  Deploy_Application -->|Error?| Debug --> Yay!;
  • First, we import all the essential libraries needed for handling, analyzing, and modeling the dataset.
  • This includes libraries like Pandas for data manipulation, Numpy for numerical computations, Matplotlib and Seaborn for data visualization, and Scikit-learn for machine learning models, evaluation, and data preprocessing.
  • These libraries will enable us to perform all required tasks efficiently.
  • We load the dataset using Pandas read_csv() function. The dataset contains crop data, which is loaded with a semicolon delimiter.
  • After loading, we inspect the first few rows to understand the structure of the data and ensure that the dataset is correctly loaded.

Data cleaning is a crucial step in any project. In this step:

  • Handle missing values, remove duplicates, and ensure data consistency.
  • Convert categorical variables if necessary and normalize numerical values.
  • Use heatmaps and scatter plots to understand relationships between features and how they impact crop recommendations.
  • Separate independent variables (environmental parameters) and the target variable (recommended crop).
  • Use train_test_split() from Scikit-learn to divide data into training and testing sets, ensuring model generalization.

We define multiple regression models to train and evaluate on the dataset:

  • RandomForestRegressor: A robust ensemble method that performs well on non-linear datasets.
  • Naive Bayes: A probabilistic classifier based on Bayes' theorem, which assumes independence between features and is effective for classification tasks.
  • DecisionTreeRegressor: A decision tree-based model, capturing non-linear patterns and interactions.
  • Fit models using training data and evaluate performance using accuracy, precision, recall, and F1-score metrics.
  • Use confusion matrices, precision-recall curves, and ROC curves to assess model performance.

== "Conclusion and Observations"

**Best-Performing Models and Insights Gained:**

- The Random Forest model provided the highest accuracy and robustness in predictions.

- Decision Tree performed well but was prone to overfitting on training data.

- Naïve Bayes, though simple, showed competitive performance for certain crop categories.

- Feature importance analysis revealed that soil pH and nitrogen levels had the most significant impact on crop recommendation.

**Potential Improvements and Future Enhancements:**

- Implement deep learning models for better feature extraction and prediction accuracy.

- Expand the dataset by incorporating satellite and real-time sensor data.

- Integrate weather forecasting models to enhance crop suitability predictions.

- Develop a mobile-friendly UI for better accessibility to farmers.

🖥 CODE EXPLANATION

from sklearn.metrics import precision_score, recall_score, f1_score, classification_report

# Initialize a dictionary to store model scores
model_scores = {}

# Iterate through each model and compute evaluation metrics
for name, model in models.items():
    print(f"Evaluating {name}...")

    # Train the model
    model.fit(x_train, y_train)

    # Predict on the test set
    y_pred = model.predict(x_test)

    # Compute metrics
    precision = precision_score(y_test, y_pred, average='weighted')
    recall = recall_score(y_test, y_pred, average='weighted')
    f1 = f1_score(y_test, y_pred, average='weighted')

    # Store results
    model_scores[name] = {
        'Precision': precision,
        'Recall': recall,
        'F1 Score': f1
    }

    # Print results for each model
    print(f"Precision: {precision:.4f}")
    print(f"Recall: {recall:.4f}")
    print(f"F1 Score: {f1:.4f}")
    print("\nClassification Report:\n")
    print(classification_report(y_test, y_pred))
    print("-" * 50)

# Print a summary of all model scores
print("\nSummary of Model Performance:\n")
for name, scores in model_scores.items():
    print(f"{name}: Precision={scores['Precision']:.4f}, Recall={scores['Recall']:.4f}, F1 Score={scores['F1 Score']:.4f}")
  • This code evaluates multiple machine learning models and displays performance metrics such as Precision, Recall, F1 Score, and a Classification Report for each model.

⚖️ PROJECT TRADE-OFFS AND SOLUTIONS

  • Trade-off: Accuracy vs. Computational Efficiency
  • Solution: Optimized hyperparameters and used efficient algorithms.
  • Trade-off: Model interpretability vs complexity.
  • Solution: Selected models balancing accuracy and interpretability.

🖼 SCREENSHOTS

Visualizations of different features

img

model-comparison

📉 MODELS USED AND THEIR EVALUATION METRICS

Model Accuracy Precision Recall F1-score
Naive Bayes 99.54% 99.58% 99.55% 99.54%
Random Forest Regressor 99.31% 99.37% 99.32% 99.32%
Decision Tree Regressor 98.63% 98.68% 98.64% 98.63%

✅ CONCLUSION

🔑 KEY LEARNINGS

Insights gained from the data

  • Soil conditions play a crucial role in crop recommendation.
  • Environmental factors significantly impact crop yield.
Improvements in understanding machine learning concepts
  • Feature engineering and hyperparameter tuning.
  • Deployment of ML models in real-world applications.

🌍 USE CASES

Application of FarmSmart in precision farming.

Use in government agricultural advisory services.

  • Government agencies can use FarmSmart to provide region-specific crop recommendations, improving food security and agricultural productivity through AI-driven insights.