Advisor

Younan, Nicholas

Committee Member

Thomasson, J. Alex

Committee Member

Moorhead, Robert J.

Date of Degree

1-1-2002

Document Type

Graduate Thesis - Open Access

Degree Name

Master of Science

College

College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

A study of the soil characteristics, weather conditions, and effect of management skills on the yield of the agricultural crop requires site-specific details, which involves large amount of labor and resources, compared to the traditional whole field based analysis. This thesis discusses the design and implemention of yield monitor for sweetpotatoes grown in heavy clay soil. A data acquisition system is built and image segmentation algorithms are implemented. The system performed with an R-Square value of 0.80 in estimating the yield. The other main contribution of this thesis is to investigate the effectiveness of statistical methods and neural networks to correlate image-based size and shape to the grade and weight of the sweetpotatoes. An R-Square value of 0.88 and 0.63 are obtained for weight and grade estimations respectively using neural networks. This performance is better compared to statistical methods with an R-Square value of 0.84 weight analysis and 0.61 in grade estimation.

URI

https://hdl.handle.net/11668/18375

Comments

Machine Vision||Classification||Neural Networks||Image Segmentation||Yield Monitoring

Share

COinS