Presenter(s)

Thomas Donnelly

Abstract

This study aimed to develop a Multilayer Perceptron (MLP) that accurately predicts crop yields within 10% of ground truth in 80% of cases using weather data, region, soil type, temperature, fertilizer, irrigation, days taken to harvest, and rail fall. The dataset has 1 million unique data points and 10 columns. In order to process the data, all Boolean data had to be converted to integers, Numerical data standardized, while Categorical data was checked for non-null values. The model will be trained using a random selection of 90% of the data for training and 10% for testing. The effectiveness of the model will be derived from the accuracy and the Mean Square Error (MSE) of the model. The learning rate of 0.001 was chosen so as not to overfit the data. As of the writing of this abstract, the best model has an MSE of 0.3992, a percentage of predictions within 10% of the actual of 62.10%, and a percentage of predictions within 20% of the actual of 88.02%.

College

College of Science & Engineering

Department

Computer Science

Campus

Winona

First Advisor/Mentor

Minigrui Zhang

Second Advisor/Mentor

Sudharsan Iyengar

Third Advisor/Mentor

Trung Nguyen

Start Date

4-24-2025 9:00 AM

End Date

4-24-2025 10:00 AM

Presentation Type

Poster Session

Format of Presentation or Performance

In-Person

Session

1a=9am-10am

Poster Number

25

Share

COinS
 
Apr 24th, 9:00 AM Apr 24th, 10:00 AM

Using Multilayer Perceptron (MLP) to predict crop yields

This study aimed to develop a Multilayer Perceptron (MLP) that accurately predicts crop yields within 10% of ground truth in 80% of cases using weather data, region, soil type, temperature, fertilizer, irrigation, days taken to harvest, and rail fall. The dataset has 1 million unique data points and 10 columns. In order to process the data, all Boolean data had to be converted to integers, Numerical data standardized, while Categorical data was checked for non-null values. The model will be trained using a random selection of 90% of the data for training and 10% for testing. The effectiveness of the model will be derived from the accuracy and the Mean Square Error (MSE) of the model. The learning rate of 0.001 was chosen so as not to overfit the data. As of the writing of this abstract, the best model has an MSE of 0.3992, a percentage of predictions within 10% of the actual of 62.10%, and a percentage of predictions within 20% of the actual of 88.02%.

 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.