Library-Based Raman Spectral Identification Using Multi-Input Hybrid ResNet

This paper introduces an effective algorithm for classifying maize leaf diseases using a publicly available image dataset. While the dataset itself, consisting of maize leaf disease images, was developed by Indian researchers for agricultural applications, this study's contribution is the development and optimization of an image classification algorithm tailored for this dataset. The paper details the preprocessing and data augmentation techniques applied, such as size adjustment, normalization, and enhancement to ensure data uniformity and diversity. These methods significantly improve the model's accuracy and generalization capabilities. The study employs a deep learning model based on resnet18, dividing the dataset into training, validation, and test sets in a 6:2:2 ratio. The training process is analyzed through loss and accuracy curves, revealing a steady decrease in loss and increase in accuracy over time. The model achieves 98% accuracy, 95% Precision, 95% Recall, and a 95% F1-score, indicating high effectiveness in classifying maize leaf diseases. A confusion matrix analysis confirms the model's precision across most image types. The research demonstrates the algorithm's strong performance in classifying various types of maize leaf diseases, highlighting its potential to aid farmers in disease identification and treatment, thereby improving agricultural productivity and quality.

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A Fully Connected Network (FCN) Trained on a Custom Library of Raman Spectra for Simultaneous Identification and Quantification of Components in Multi-Component Mixtures

Zhao,

Kusnierek

2024

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Raman spectroscopy provides detailed information about the molecular composition of a sample. The classical identification of components in a multi-component sample typically involves comparing the preprocessed spectrum with a known reference stored in a database using various spectral matching or machine-learning techniques or relies on universal models based on a two-step analysis including first, the component identification, and then the decomposition of the mixed signal. However, although large databases and universal models cover a wide range of target materials, they may be not optimized to the variability required in a specific application. In this study, we propose a single-step method using deep learning (DL) modeling to decompose a simulated mixture of real measurements of Raman scattering into relevant individual components regardless of noise, baseline and the number of components involved and quantify their ratios. We hypothesize that training a custom DL model for applications with a fixed set of expected components may yield better results than applying a universal quantification model. To test this hypothesis, we simulated 12,000 Raman spectra by assigning random ratios to each component spectrum within a library containing 13 measured spectra of organic solvent samples. One of the DL methods, a fully connected network (FCN), was designed to work on the raw spectra directly and output the contribution of each component of the library to the input spectrum in form of a component ratio. The developed model was evaluated on 3600 testing spectra, which were simulated similarly to the training dataset. The average component identification accuracy of the FCN was 99.7%, which was significantly higher than that of the universal custom trained DeepRaman model, which was 83.1%. The average mean absolute error for component ratio quantification was 0.000562, over one order of magnitude smaller than that of a well-established non-negative elastic net (NN-EN), which was 0.00677. The predicted non-zero ratio values were further used for component identification. Under the assumption that the components of a mixture are from a fixed library, the proposed method preprocesses and decomposes the raw data in a single step, quantifying every component in a multicomponent mixture, accurately. Notably, the single-step FCN approach has not been implemented in the previously reported DL studies.

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Library-Based Raman Spectral Identification Using Multi-Input Hybrid ResNet

Cited by 3 publications

References 30 publications

An interpretable multi-scale convolutional attention residual neural network for glioma grading with Raman spectroscopy

An interpretable multi-scale convolutional attention residual neural network for glioma grading with Raman spectroscopy

Maize leaf disease image classification based on ResNet18 image classification model

A Fully Connected Network (FCN) Trained on a Custom Library of Raman Spectra for Simultaneous Identification and Quantification of Components in Multi-Component Mixtures

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