Deep Learning-Based Robust Analysis of Laser Bio-Speckle Data for Detection of Fungal-Infected Soybean Seeds

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020635" target="_blank" >RIV/62690094:18450/23:50020635 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10216973" target="_blank" >https://ieeexplore.ieee.org/document/10216973</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3305273" target="_blank" >10.1109/ACCESS.2023.3305273</a>

Result language

angličtina

Original language name

Deep Learning-Based Robust Analysis of Laser Bio-Speckle Data for Detection of Fungal-Infected Soybean Seeds

Original language description

Seed-borne diseases play a crucial role in affecting the overall quality of seeds, efficient disease management, and crop productivity in agriculture. Detection of seed-borne diseases using machine learning (ML) and deep learning (DL) can automate the process at large-scale industrial applications for providing healthy and high-quality seeds. ML-based methods are accurate for detecting and classifying fungal infection in seeds; however, their performance degrades in the presence of noise. In this work, we propose a laser bio-speckle based DL framework for detection and classification of disease in seeds under varying experimental parameters and noises. We develop a DL-based spatio-temporal analysis technique for bio-speckle data using DL networks, including neural networks (NN), convolutional neural networks (CNN) with long-short-term memory (LSTM), three-dimensional convolutional neural networks (3D CNN), and convolutional LSTM (ConvLSTM). The robustness of the DL models to noise is a key aspect of this spatio-temporal analysis. In this study, we find that the ConvLSTM model has an accuracy of 97.72% on the test data and is robust to different types of noises with an accuracy of 97.72%, 94.31%, 98.86%, and 96.59%. Furthermore, the robust model (ConvLSTM) is evaluated for variations in experimental data parameters such as frame rate, frame size, and number of frames used. This model is also sensitive towards detecting bio-speckle activity of different order, and it shows average test accuracy of 99% for detecting four different classes.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Volume of the periodical

11

Issue of the periodical within the volume

August

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

89331-89348

UT code for WoS article

001058758000001

EID of the result in the Scopus database

2-s2.0-85168296417