Analysis of Machine Learning Algorithm to Improve Fruit Yield Estimation
Faculty Mentor Information
Dr. Duke Bulanon (Mentor), Northwest Nazarene University
Presentation Date
7-2024
Abstract
Accurately estimating the fruit yield is an extremely important part of precision agriculture. Northwest Nazarene University is developing a mobile application to help farmers estimate the fruit yield of apple trees more accurately and efficiently. Before the development of fruit yield apps, farmers would have to manually count the fruit and take the averages from multiple trees to estimate the fruit yield. The app currently uses an RGB color mask to count the fruit. A new color masking system was created to see if the RGB color model is the most accurate model to use for the purpose of apple counting. Machine learning algorithms were also investigated and prototyped for fruit yield estimation.
Analysis of Machine Learning Algorithm to Improve Fruit Yield Estimation
Accurately estimating the fruit yield is an extremely important part of precision agriculture. Northwest Nazarene University is developing a mobile application to help farmers estimate the fruit yield of apple trees more accurately and efficiently. Before the development of fruit yield apps, farmers would have to manually count the fruit and take the averages from multiple trees to estimate the fruit yield. The app currently uses an RGB color mask to count the fruit. A new color masking system was created to see if the RGB color model is the most accurate model to use for the purpose of apple counting. Machine learning algorithms were also investigated and prototyped for fruit yield estimation.