CoPPNet: A Cosegmentation-Based Deep Learning Network for
Accurate Foreground Segmentation in Plant Imagery
Rubi Quiñones1, Francisco
Munoz-Arriola2,3, Sruti Das
Choudhury1,2, Ashok Samal1
1 Department of Computer Science and Engineering, University of
Nebraska-Lincoln, Lincoln, Nebraska, United States of America,2 School of Natural Resources, University of Nebraska-Lincoln,
Lincoln, Nebraska, United States of America, 3 Department of
Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln,
Nebraska, United States of America
Cosegmentation is a recent and rapidly emerging and rapidly growing
extension of segmentation, which aims to detect the common object(s) in
a group of images. Current cosegmentation methods are ideal and
effective only for certain dataset types with limited features that
still produce errors making it difficult to obtain detailed metrics of
object parts. We propose to build a unified, trainable framework that
incorporates multiple features of a high-throughput dataset’s segmented
images from multiple algorithms using cosegmentation. Specifically, we
propose a novel Cosegmentation for Plant Phenotyping Network (CoPPNet)
that utilizes a Fully Convolutional Neural Network with a K-Means
Clustering feedback loop for optimal temporal loss. The results from
this study will set the benchmark for a novel advancement in computer
vision segmentation accuracy and plant phenomics to better understand a
plant’s environmental interactions for maximal resilience and yield.