🌾 Agricultural Crop Yield Prediction using Machine Learning in R

🚀 Project Overview

Successfully developed a crop yield regression system using advanced machine learning algorithms in R. This project aims to predict crop yields (in tons per hectare) using agro-environmental, soil, and weather data. The solution includes a fully automated pipeline for data preprocessing, model training, and performance evaluation—tailored for practical decision-making in agriculture.

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🔑 Key Features

• Exploratory Data Analysis (EDA):
  Comprehensive visualizations using ggplot2, corrplot, and DataExplorer for pattern recognition and outlier detection.

• Feature Engineering:

  • One-hot encoding for categorical variables
  • Handling missing values and constant columns
  • Ensured numerical format compatibility for ML models

• Feature Scaling:
  Min-max normalization with protection against division-by-zero for constant features.

• Model Building:
  Implemented and evaluated 20+ regression models, including:

  • Linear Regression, Ridge, Lasso
  • Decision Tree, Random Forest, Bagging
  • Gradient Boosting, XGBoost, LightGBM, CatBoost
  • K-Nearest Neighbors (KNN), Support Vector Machines (SVM)
  • Bayesian Regression, Quantile Regression
  • Neural Networks (nnet, neuralnet)

• Hyperparameter Tuning:
  Optimized models using caret, xgboost::xgb.cv, lgb.cv, and randomForest::tuneRF.

• Performance Metrics:
  RMSE, MAE, MAPE, R², Adjusted R², and RMSLE.

• Model Comparison:
  Side-by-side metric comparison to select the best-performing algorithm.

• Interpretability:

  • Variable importance via varImp, xgb.importance, plotnet, olden, garson
  • Uncertainty estimation using Quantile Regression

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📊 Results

• Top-performing models included:

  • ⭐ Linear Regression (R² = 0.9132)
  • ⭐ Tuned GAM / Generalized Additive Model (R² = 0.9128)
  • ⭐ Gaussian GLM (R² = 0.9128)

• Other strong performers:
  Tuned CatBoost, XGBoost, LightGBM, and Neural Networks also achieved R² > 0.91 after tuning.

• Underperformers:
  Simpler models like Decision Trees, Random Forests, and KNN lagged behind.
  Bayesian Regression, Lasso, Ridge, and Elastic Net showed poor generalization.

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⚙️ Tech Stack

• Language: R
• Libraries:
  caret, xgboost, lightgbm, catboost, randomForest, rpart, neuralnet, nnet, Metrics, quantreg, ggplot2, dplyr, glmnet, e1071, DataExplorer, mlbench, MASS, corrplot, smotefamily

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🏆 Use Cases

• Government and NGOs for agricultural planning and subsidy allocation
• Agribusiness firms for forecasting crop production and optimizing supply chain logistics
• Farm-level decision support systems for precision agriculture

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📈 Future Enhancements

• Deploy via Shiny dashboard for interactive yield prediction tools
• Incorporate remote sensing and real-time weather data
• Build a REST API for integration into farm management systems