Smart Agriculture: Optimizing Soil pH and Crop Recommendations Using Machine Learning

This study explores how hyperparameter tuning enhances the performance of machine learning models, achieving a 99.5% accuracy in crop classification and highlighting the potential of AI-driven optimization for sustainable precision agriculture.

Precision agriculture relies heavily on machine learning to provide actionable insights into crop management. This study investigates the application of hyperparameter tuning to improve crop classification models using environmental and soil data. By leveraging a dataset of 22 crop types with features such as nitrogen content, potassium levels, pH, and rainfall, Decision Tree and Random Forest models were developed and optimized. Hyperparameters like max_depth and criterion were systematically adjusted to balance bias and variance. Through extensive tuning, the Decision Tree Classifier achieved a classification accuracy of 99.5%, while the Random Forest Regressor demonstrated a Mean Absolute Error (MAE) of less than 0.4. This paper explores the impact of hyperparameter optimization on model performance and highlights its practical implications for improving crop recommendation systems in agriculture. The findings demonstrate the value of AI-driven decision-making in sustainable farming and pave the way for future innovations in agricultural technology.

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Predicting Unplanned Electric Vehicle Breakdowns using Machine Learning on Sensor and Diagnostic Data

This study explores the use of artificial intelligence (AI) for fault prediction in electric vehicles (EVs) to reduce unexpected breakdowns and improve reliability. Using sensor, driving, and diagnostic data from 2020–2024, the research found that tree-based models, particularly Decision Tree and Random Forest, achieved over 90% predictive accuracy, demonstrating AI’s potential to enable proactive maintenance and enhance user confidence in EV technology.

Richa K.

Mentored by Rami Abi-Akl

A Comprehensive Study of Machine Learning Models for the Prediction of pH in Fairfax County

Water quality is drastically declining as bodies of water become more and more acidic due to global warming. Because of this, it is crucial to understand what factors determine pH, and how to predict pH values. In this study, we determine which features are most important in determining pH, and also what combination of features and machine learning models give us the best accuracy in the prediction of pH values. We demonstrate that pH values from the previous year and the percentage of rehabilitated sanitary sewer lines are the most important features in determining pH. We also demonstrate that Decision Tree Models and Random Forest Models perform the best across all features and feature subsets.

Matthew N.

Mentored by Sarthak Kanodia

Machine Learning Algorithms for Consumer Plastics Identification and Sorting

Seeking a solution to maintain plastic recycling costs while increasing the output of reusable material, this paper poses the question “what type of machine learning algorithm is most suitable for a plastic identification system in consumers’ homes?” It investigates five total machine learning algorithms to determine which one best balances accuracy, management of resources, and time efficiency, ultimately arriving at the conclusion that a Support Vector Machine that uses a Polynomial Kernel is the best algorithm and serves to demonstrate that algorithms such as the ones analyzed have become advanced enough for more efficient, AI driven systems to replace those of the status quo.

Alexander K.

Mentored by Eric Bradford

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Smart Agriculture: Optimizing Soil pH and Crop Recommendations Using Machine Learning

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Research Paper