HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds

Authors

Tian Gao, University of Nebraska-LincolnFollow
Anil Kumar Nalini Chandran, University of Nebraska-LincolnFollow
Puneet Paul, University of Nebraska-Lincoln, LincolnFollow
Harkamal Walia, University of Nebraska - LincolnFollow
Hongfeng Yu, Department of Computer Science, University of Nebraska LincolnFollow

ORCID IDs

Tian Gao https://orcid.org/0000-0003-4075-4125

Puneet Paul https://orcid.org/0000-0001-8220-8021

Harkamal Walia https://orcid.org/0000-0002-9712-5824

Hongfeng Yu https://orcid.org/0000-0002-0596-8227

Document Type

Article

Date of this Version

12-8-2021

Citation

Gao, T.; Chandran, A.K.N.; Paul, P.; Walia, H.; Yu, H. HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds. Sensors 2021, 21, 8184. https:// doi.org/10.3390/s21248184

Comments

Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license

Abstract

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest.