Multimodal plant recognition through hybrid feature fusion technique using imaging and non-imaging hyper-spectral data

Salve, Pradip; Yannawar, Pravin; Sardesai, Milind

doi:10.1016/j.jksuci.2018.09.018

Cited by 21 publications

(13 citation statements)

References 15 publications

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“…An example for such a variation is given in Figure 22 for moss. Although not implemented in this work, one possible way to deal with such heterogeneous spectra could be the addition of rules based on texture features or other local image features [52][53][54].…”

Section: Classification Results For Plantsmentioning

confidence: 99%

A New Rule-Based Classification Method Using Shape-Based Properties of Spectral Curves

Polat

Trémeau²,

Boochs

2022

Journal of Spectroscopy

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Due to its high spatial and spectral information content, hyperspectral imaging opens up new possibilities for a better understanding of data and scenes in a wide variety of applications. An essential part of this process of understanding is the classification part. However, the high spatial and spectral resolution also leads to enormous amounts of data. The effective handling and use of such datasets for classification requires processing steps (dimensionality reduction through feature selection or feature extraction) that are not always goal-oriented. In this article, a new general classification approach is presented that uses the geometric shape of spectral signatures instead of purely statistical methods. In contrast to classical classification approaches (e.g., SVM, KNN), not only are reflectance values taken into account, but also parameters such as curvature points, curvature values, and the curvature behavior of spectral signatures are used to develop shape-describing rules in order to use them for classification by a rule-based procedure with IF-THEN queries. The flexibility and efficiency of the methodology are demonstrated on datasets from two different application domains and lead to convincing results with good performance.

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Section: Classification Results For Plantsmentioning

confidence: 99%

A New Rule-Based Classification Method Using Shape-Based Properties of Spectral Curves

Polat

Trémeau²,

Boochs

2022

Journal of Spectroscopy

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“… Domain Objective Data Source Data Fusion Level Modeling Method Variable Selection/Feature Extraction Complementarity Evaluation Performance Results Robustness/Validation Reference CLASSIFICATION Agriculture Discrimination of different crop types CCD digital camera; Spectro-radiometry MLDF DISCRIM (SAS) PCA / MLDF > individual model / [ 181 ] Botanical Plant recognition Spectro-radiometer; Imaging MLDF Euclidean distance Spectral signatures; leaf venation feature extraction DF-individual model comparison MLDF > individual model e.d. [ 182 ] Chemical iIdentification of essential oils in Melaleuca sp. GC-MS; NMR LLDF - - Statistical Total Correlation LLDF > individual model / [ 160 ] Classification of pigments...…”

Section: Table A1mentioning

confidence: 99%

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

et al. 2022

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The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

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“…In the review of [26], the authors also discussed the potential of information fusion for plant stress phenotyping. Various studies have shown the good performances of information fusion for plant phenotyping, including the fusion of data acquainted by different types of sensors (representing different techniques) [27][28][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

“…The first step is to train a feature extractor or directly use a manually defined one to produce the features for fusion. The second step is to train another model for discrimination based on the fused features obtained in the first step [27,28,33]. These information fusion models are complex and need manual intervention.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses

et al. 2022

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Herbicides and heavy metals are hazardous substances of environmental pollution, resulting in plant stress and harming humans and animals. Identification of stress types can help trace stress sources, manage plant growth, and improve stress-resistant breeding. In this research, hyperspectral imaging (HSI) and chlorophyll fluorescence imaging (Chl-FI) were adopted to identify the rice plants under two types of herbicide stresses (butachlor (DCA) and quinclorac (ELK)) and two types of heavy metal stresses (cadmium (Cd) and copper (Cu)). Visible/near-infrared spectra of leaves (L-VIS/NIR) and stems (S-VIS/NIR) extracted from HSI and chlorophyll fluorescence kinetic curves of leaves (L-Chl-FKC) and stems (S-Chl-FKC) extracted from Chl-FI were fused to establish the models to detect the stress of the hazardous substances. Novel end-to-end deep fusion models were proposed for low-level, middle-level, and high-level information fusion to improve identification accuracy. Results showed that the high-level fusion-based convolutional neural network (CNN) models reached the highest detection accuracy (97.7%), outperforming the models using a single data source (<94.7%). Furthermore, the proposed end-to-end deep fusion models required a much simpler training procedure than the conventional two-stage deep learning fusion. This research provided an efficient alternative for plant stress phenotyping, including identifying plant stresses caused by hazardous substances of environmental pollution.

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Multimodal plant recognition through hybrid feature fusion technique using imaging and non-imaging hyper-spectral data

Cited by 21 publications

References 15 publications

A New Rule-Based Classification Method Using Shape-Based Properties of Spectral Curves

A New Rule-Based Classification Method Using Shape-Based Properties of Spectral Curves

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses

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