Most Important Soil Properties to Consider for High Crop Yield

With the current population of Earth massively growing, there is a real risk coming up in the near future of not having enough food to feed the planet. One of the best ways to solve this upcoming problem is to improve the quality of the soil because that will help increase Earth’s overall crop yield in a sustainable way. However, figuring out how to improve soil can be a big challenge since there are so many elements and properties of soil to analyze. To help solve this problem we looked into the elements or properties of soil that could have the biggest influence on soils ability to be productive for growing crops. We found online datasets and used unsupervised machine learning by applying both principal component analysis (PCA) and hierarchal clustering on our data. We concluded that calcium, potassium, pH, and soil organic carbon are the elements or properties of soil that have the biggest influence on soil's ability to be productive for growing crops. If these results are accurate, it can massively help many farmers and people in improving their soil quality. This can lead to us taking a big step in the direction of increasing Earth’s overall crop yield.

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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

Plant Toxicity Classification by Image

Since differentiating between dangerous and safe plants is a complex task for a human brain, this study approaches the issue through machine learning models starting with a convolutional neural network (CNN) and discovering that a logistic regression model—trained on a dataset with manually designed features—has the best performance with the particular dataset used.

Eera B.

Mentored by Clayton Greenberg

Using Logistic Regression in the Early Detection of Tsunamis caused by Earthquakes

Many tsunamis have enough energy to cause a large amount of human death, as well as trillions of dollars in damage repair. The problem arises on how to better prepare for these natural disasters and limit its damage, especially in countries such as Japan that are frequented by tsunamis.

Ian C.

Mentored by

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Most Important Soil Properties to Consider for High Crop Yield

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Research Paper