Date of Award
2022
Publisher
North Dakota State University
Document Type
Thesis
Degree Awarded
Master of Science (MS)
Program
Computer Science
Department
Computer Science
College
Engineering
Faculty Advisor
Yellavajjala, Ravi Kiran
Description
Palmer amaranth is an invasive pigweed species, possessing rapid growth, posing a threat to the economy of crops including corn. Its early detection and mitigation are of utmost importance; however, it is visually similar to waterhemp in the early growth stages. In this study, automated approaches are employed to distinguish palmer amaranth from waterhemp, within two weeks after emergence, from their RGB images. Morphological characteristics of these weeds are estimated and fed to several Machine Learning (ML) models. To improve classification accuracy, RGB images are augmented, and a Convolutional neural network is trained on 16000 images. Validated on images embedded with gaussian noise, it produced a better accuracy compared to ML approaches. Finally, YOLOv5, an object detection algorithm based on transfer learning, is successfully prepared. Tested on synthetic images consisting of both weeds, YOLOv5 successfully detected a significantly high number of palmer amaranth objects while also distinguishing it from waterhemp.
Recommended Citation
Venkataraju, Akhil, "Automated Approaches for the Early-Stage Distinguishing of Palmer Amaranth from Waterhemp" (2022). Communication. 32.
https://digitalcommons.ndsu.edu/communication/32
Rights
NDSU policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/33385