scholarly journals Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings

2021 ◽  
Vol 13 (18) ◽  
pp. 3595
Author(s):  
Piyush Pandey ◽  
Kitt G. Payn ◽  
Yuzhen Lu ◽  
Austin J. Heine ◽  
Trevor D. Walker ◽  
...  
Keyword(s):  
Spectral Data ◽  
Hyperspectral Imaging ◽  
Loblolly Pine ◽  
Disease Incidence ◽  
Fungal Spores ◽  
The United States ◽  
Classification Model ◽  
Support Vector ◽  
Timber Species ◽  
Fusiform Rust

Loblolly pine is an economically important timber species in the United States, with almost 1 billion seedlings produced annually. The most significant disease affecting this species is fusiform rust, caused by Cronartium quercuum f. sp. fusiforme. Testing for disease resistance in the greenhouse involves artificial inoculation of seedlings followed by visual inspection for disease incidence. An automated, high-throughput phenotyping method could improve both the efficiency and accuracy of the disease screening process. This study investigates the use of hyperspectral imaging for the detection of diseased seedlings. A nursery trial comprising families with known in-field rust resistance data was conducted, and the seedlings were artificially inoculated with fungal spores. Hyperspectral images in the visible and near-infrared region (400–1000 nm) were collected six months after inoculation. The disease incidence was scored with traditional methods based on the presence or absence of visible stem galls. The seedlings were segmented from the background by thresholding normalized difference vegetation index (NDVI) images, and the delineation of individual seedlings was achieved through object detection using the Faster RCNN model. Plant parts were subsequently segmented using the DeepLabv3+ model. The trained DeepLabv3+ model for semantic segmentation achieved a pixel accuracy of 0.76 and a mean Intersection over Union (mIoU) of 0.62. Crown pixels were segmented using geometric features. Support vector machine discrimination models were built for classifying the plants into diseased and non-diseased classes based on spectral data, and balanced accuracy values were calculated for the comparison of model performance. Averaged spectra from the whole plant (balanced accuracy = 61%), the crown (61%), the top half of the stem (77%), and the bottom half of the stem (62%) were used. A classification model built using the spectral data from the top half of the stem was found to be the most accurate, and resulted in an area under the receiver operating characteristic curve (AUC) of 0.83.

scholarly journals Forensic analysis of beverage stains using hyperspectral imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Binu Melit Devassy ◽  
Sony George
Keyword(s):  
Hyperspectral Imaging ◽  
Crime Scene ◽  
Forensic Analysis ◽  
Classification Model ◽  
Support Vector ◽  
Additional Information ◽  
Non Invasive ◽  
Spectral Bands ◽  
Data Dimensionality Reduction ◽  
Gradient Based

AbstractDocumentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the commonly used drinks in a crime scene, we leveraged the additional information present in the HSI data. We used 12 different beverages and four types of paper hand towel to create the sample stains in the current study. A support vector machine (SVM) is used to achieve the classification, and a convolutional auto-encoder is used to achieve HSI data dimensionality reduction, which helps in easy perception, process, and visualization of the data. The SVM classification model was re-established for a lighter and quicker classification model on the basis of the reduced dimension. We employed volume-gradient-based band selection for the identification of relevant spectral bands in the HSI data. Spectral data recorded at different time intervals up to 72 h is analyzed to trace the spectral changes. The results show the efficacy of the HSI techniques for rapid, non-contact, and non-invasive analysis of beverage stains.

scholarly journals Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 97 ◽  
Author(s):  
Siddharth Chaudhary ◽  
Sarawut Ninsawat ◽  
Tai Nakamura
Keyword(s):  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Classification Model ◽  
Support Vector ◽  
Trace Detection ◽  
Svm Model ◽  
Detection Of Explosives ◽  
Non Destructive ◽  
Hyperspectral Sensor

The aim of this study was to investigate the potential of the non-destructive hyperspectral imaging system (HSI) and accuracy of the model developed using Support Vector Machine (SVM) for determining trace detection of explosives. Raman spectroscopy has been used in similar studies, but no study has been published which is based on measurement of reflectance from hyperspectral sensor for trace detection of explosives. HSI used in this study has an advantage over existing techniques due to its combination of imaging system and spectroscopy, along with being contactless and non-destructive in nature. Hyperspectral images of the chemical were collected using the BaySpec hyperspectral sensor which operated in the spectral range of 400–1000 nm (144 bands). Image processing was applied on the acquired hyperspectral image to select the region of interest (ROI) and to extract the spectral reflectance of the chemicals which were stored as spectral library. Principal Component Analysis (PCA) and first derivative was applied to reduce the high dimensionality of the image and to determine the optimal wavelengths between 400 and 1000 nm. In total, 22 out of 144 wavelengths were selected by analysing the loadings of principal components (PC). SVM was used to develop the classification model. SVM model established on the whole spectrum from 400 to 1000 nm achieved an accuracy of 81.11%, whereas an accuracy of 77.17% with less computational load was achieved when SVM model was established on the optimal wavelengths selected. The results of the study demonstrate that the hyperspectral imaging system along with SVM is a promising tool for trace detection of explosives.

scholarly journals Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods

2020 ◽  
Vol 10 (19) ◽  
pp. 6724
Author(s):  
Youngwook Seo ◽  
Ahyeong Lee ◽  
Balgeum Kim ◽  
Jongguk Lim
Keyword(s):  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Partial Least Square ◽  
Hyperspectral Images ◽  
Classification Model ◽  
Support Vector ◽  
Chemometric Methods ◽  
Infrared Wavelength ◽  
Linear Discriminant

(1) Background: The general use of food-processing facilities in the agro-food industry has increased the risk of unexpected material contamination. For instance, grain flours have similar colors and shapes, making their detection and isolation from each other difficult. Therefore, this study is aimed at verifying the feasibility of detecting and isolating grain flours by using hyperspectral imaging technology and developing a classification model of grain flours. (2) Methods: Multiple hyperspectral images were acquired through line scanning methods from reflectance of visible and near-infrared wavelength (400–1000 nm), reflectance of shortwave infrared wavelength (900–1700 nm), and fluorescence (400–700 nm) by 365 nm ultraviolet (UV) excitation. Eight varieties of grain flours were prepared (rice: 4, starch: 4), and the particle size and starch damage content were measured. To develop the classification model, four multivariate analysis methods (linear discriminant analysis (LDA), partial least-square discriminant analysis, support vector machine, and classification and regression tree) were implemented with several pre-processing methods, and their classification results were compared with respect to accuracy and Cohen’s kappa coefficient obtained from confusion matrices. (3) Results: The highest accuracy was achieved as 97.43% through short-wavelength infrared with normalization in the spectral domain. The submission of the developed classification model to the hyperspectral images showed that the fluorescence method achieves the highest accuracy of 81% using LDA. (4) Conclusions: In this study, the potential of non-destructive classification of rice and starch flours using multiple hyperspectral modalities and chemometric methods were demonstrated.

Hyperspectral Imaging with Cost-Sensitive Learning for High-Throughput Screening of Loblolly Pine (Pinus taeda L.) Seedlings for Freeze Tolerance

2021 ◽  
Vol 64 (6) ◽  
pp. 2045-2059
Author(s):  
Yuzhen Lu ◽  
Kitt G. Payn ◽  
Piyush Pandey ◽  
Juan J. Acosta ◽  
Austin J. Heine ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hyperspectral Imaging ◽  
Loblolly Pine ◽  
High Throughput Screening ◽  
Freeze Tolerance ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Full Spectrum ◽  
Cost Sensitive Learning ◽  
Pinus Taeda L

HighlightsA hyperspectral imaging approach was developed for freeze-tolerance phenotyping of loblolly pine seedlings.Image acquisition was conducted before and periodically after artificial freezing of the seedlings.A hyperspectral data processing pipeline was developed to extract the spectra from seedling segments.Cost-sensitive support vector machine (SVM) was used for classifying stressed and healthy seedlings.Post-freeze scanning of seedlings on day 41 achieved the highest screening accuracy of 97%.Abstract. Loblolly pine (Pinus taeda L.) is a commercially important timber species planted across a wide temperature gradient in the southeastern U.S. It is critical to ensure that the planting stock is suitably adapted to the growing environment to achieve high productivity and survival. Long-term field studies, although considered the most reliable method for assessing cold hardiness of loblolly pine, are extremely resource-intensive and time-consuming. The development of a high-throughput screening tool to characterize and classify freeze tolerance among different genetic entries of seedlings will facilitate accurate deployment of highly productive and well-adapted families across the landscape. This study presents a novel approach using hyperspectral imaging to screen loblolly pine seedlings for freeze tolerance. A diverse population of 1549 seedlings raised in a nursery were subjected to an artificial mid-winter freeze using a freeze chamber. A custom-assembled hyperspectral imaging system was used for in-situ scanning of the seedlings before and periodically after the freeze event, followed by visual scoring of the frozen seedlings. A hyperspectral data processing pipeline was developed to segment individual seedlings and extract the spectral data. Examination of the spectral features of the seedlings revealed reductions in chlorophylls and water concentrations in the freeze-susceptible plants. Because the majority of seedlings were freeze-stressed, leading to severe class imbalance in the hyperspectral data, a cost-sensitive learning technique that aims to optimize a class-specific cost matrix in classification schemes was proposed for modeling the imbalanced hyperspectral data, classifying the seedlings into healthy and freeze-stressed phenotypes. Cost optimization was effective for boosting the classification accuracy compared to regular modeling that assigns equal costs to individual classes. Full-spectrum, cost-optimized support vector machine (SVM) models achieved geometric classification accuracies of 75% to 78% before and within 10 days after the freeze event, and up to 96% for seedlings 41 days after the freeze event. The top portions of seedlings were more indicative of freeze events than the middle and bottom portions, leading to better classification accuracies. Further, variable selection enabled significant reductions in wavelengths while achieving even better accuracies of up to 97% than full-spectrum SVM modeling. This study demonstrates that hyperspectral imaging can provide tree breeders with a valuable tool for improved efficiency and objectivity in the characterization and screening of freeze tolerance for loblolly pine. Keywords: Cost-sensitive learning, Freeze tolerance, Hyperspectral imaging, Plant phenotyping, Support vector machine.

scholarly journals Classifying Wood Properties of Loblolly Pine Grown in Southern Brazil Using NIR-Hyperspectral Imaging

Forests ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 686
Author(s):  
Laurence Schimleck ◽  
Jorge L. M. Matos ◽  
Antonio Higa ◽  
Rosilani Trianoski ◽  
José G. Prata ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Hyperspectral Imaging ◽  
Loblolly Pine ◽  
Microfibril Angle ◽  
Wood Property ◽  
Wood Properties ◽  
Dry Density ◽  
Timber Species ◽  
Southern Brazil ◽  
Improvement Programs

Loblolly pine (Pinus taeda L.) is one of the most important commercial timber species in the world. While the species is native to the southeastern United States of America (USA), it has been widely planted in southern Brazil, where it is the most commonly planted exotic species. Interest exists in utilizing nondestructive testing methods for wood property assessment to aid in improving the wood quality of Brazilian grown loblolly pine. We used near-infrared hyperspectral imaging (NIR-HSI) on increment cores to provide data representative of the radial variation of families sampled from a 10-year-old progeny test located in Rio Negrinho municipality, Santa Catarina, Brazil. Hyperspectral images were averaged to provide an individual NIR spectrum per tree for cluster analysis (hierarchical complete linkage with square Euclidean distance) to identify trees with similar wood properties. Four clusters (0, 1, 2, 3) were identified, and based on SilviScan data for air-dry density, microfibril angle (MFA), and stiffness, clusters differed in average wood properties. Average ring data demonstrated that trees in Cluster 0 had the highest average ring densities, and those in Cluster 3 the lowest. Cluster 3 trees also had the lowest ring MFAs. NIR-HSI provides a rapid approach for collecting wood property data and, when coupled with cluster analysis, potentially, allows screening for desirable wood properties amongst families in tree improvement programs.

Annual variation in fusiform rust infection of slash and loblolly pine seed lots in time-replicated plantings

1989 ◽  
Vol 19 (12) ◽  
pp. 1531-1537
Author(s):  
R. C. Froelich
Keyword(s):  
Loblolly Pine ◽  
Annual Variation ◽  
Disease Incidence ◽  
Growing Season ◽  
Slash Pine ◽  
Family Interactions ◽  
Fusiform Rust ◽  
Rust Infection ◽  
Location Family ◽  
High Hazard

Trees from two putatively fusiform rust resistant and two susceptible seed lots of slash pine (Pinuselliottii Engelm. var. elliottii) and loblolly pine (P. taeda L.) were planted in 4 or 5 consecutive years at three locations in south Mississippi and examined annually for rust galls that originated the previous year. Disease incidence was usually less than 20% in the first growing season. Resistant and susceptible slash pine had light incidence of rust after about age 5. The susceptible lobloly pine often developed considerable numbers of branch galls after the fifth season, but most were several metres from the main stem of the trees. The resistant loblolly pine of Livingston Parish, Louisiana, never developed more than 23% infection in any 1 year in thirty 80-tree plots. The three remaining seed lots often exceeded 50% infection in some years, but differences among seed lots were usually small. Rankings of the four species seed lots or the six families that made up the resistant slash seed lot varied by trial (year planted and year infected). Replicating experiments in time on high-hazard sites for rust should better define occurrence and importance of location × family interactions.

scholarly journals NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Identify Shrimp Freshness

2020 ◽  
Vol 10 (16) ◽  
pp. 5498
Author(s):  
Rongke Ye ◽  
Yingyi Chen ◽  
Yuchen Guo ◽  
Qingling Duan ◽  
Daoliang Li ◽  
...  
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Support Vector ◽  
Optimal Model ◽  
Model Combination ◽  
Modeling Analysis ◽  
Characteristic Wavelength ◽  
Discriminant Models

In this study, a hyperspectral imaging system of 866.4–1701.0 nm, combined with a variety of spectral processing methods were adopted to identify shrimp freshness. To gain the optimal model combination, three preprocessing methods (Savitzky-Golay first derivative (SG1), multivariate scatter correction (MSC), and standard normal variate (SNV)), three characteristic wavelength extraction algorithms (random frog algorithm (RFA), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)), and four discriminant models (partial least squares discrimination analysis (PLS-DA), least squares support vector machine (LSSVM), random forest (RF), and extreme learning machine (ELM)) were employed for experimental study. First of all, due to the full wavelength modeling analysis, three preprocessing methods were utilized to preprocess the original spectral data. The analysis showed that the spectral data processed by the SNV method had the best performance among the four discriminant models. Secondly, due to the characteristic wavelength modeling analysis, three characteristic wavelength extraction algorithms were utilized to extract the characteristic wavelength of the SNV-processed spectral data. It was found that the CARS algorithm achieved the best performance among the three characteristic wavelength extraction algorithms, and the combining adoption of the ELM model and different characteristic wavelength extraction algorithms obtained the best results. Therefore, the model based on SNV-CARS-ELM obtained the best performance and was elected as the optimal model. Lastly, for accurately and explicitly displaying the refrigeration days of shrimps, the original hyperspectral images of shrimps were substituted into the SNV-CARS-ELM model, thus obtaining the general classification accuracy of 97.92%, and the object-wise method was used to visualize the classification results. As a result, the method proposed in this study can effectively detect the freshness of shrimps.

Identification of Wine Grape Varieties Based on Near-infrared Hyperspectral Imaging

2019 ◽  
Vol 35 (6) ◽  
pp. 959-967
Author(s):  
Xu Liu ◽  
Enyu Zhang
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Spectral Data ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Variety Identification ◽  
Support Vector ◽  
Wine Grape ◽  
Full Spectrum ◽  
Grape Varieties

Abstract. Wine grape variety is one of the main determinants of wine quality. The objective of this study is to explore the feasibility of using hyperspectral imaging (HSI) to identify six red and six white wine grape cultivars during the ripening period. Abnormal spectral data were removed by the Mahalanobis distance, and six different methods were employed to preprocess the spectral data. Next, the effective wavelengths for the classification of grape varieties were selected using principal component analysis (PCA) loadings to improve the HSI processing speed. Finally, three methods were applied to classify grape samples: a support vector machine (SVM), a random forest (RF), and an AdaBoost model. The results indicated that the model established by Savitzky-Golay (S-G) Filter + PCA + SVM achieves the best classification result. The average calibration and validation accuracy for red grapes reached 93.06% and 90.01%, respectively, and for white grapes, they reached 83.77% and 81.09%, respectively, which are slightly lower than those achieved by the full-spectrum model. This study revealed that hyperspectral imaging has great potential for rapid variety discrimination of different wine grapes. Keywords: Hyperspectral imaging, Random forest, Support vector machine, Variety identification, Wine grape.

Detection of the Freshness State of Cooked Beef During Storage Using Hyperspectral Imaging

2017 ◽  
Vol 71 (10) ◽  
pp. 2286-2301 ◽  
Author(s):  
Dong Yang ◽  
Dandan He ◽  
Anxiang Lu ◽  
Dong Ren ◽  
Jihua Wang
Keyword(s):  
Least Squares ◽  
Hyperspectral Imaging ◽  
Meat Products ◽  
Principal Component ◽  
Viable Count ◽  
Classification Model ◽  
Support Vector ◽  
Meat Production ◽  
Svm Classification ◽  
Svm Model

The freshness of meat products during storage has received unprecedented attention. This study was conducted to investigate the feasibility of a hyperspectral imaging (HSI) technique to determine the freshness state of cooked beef during storage and identify the contaminated areas on the surface of spoiled samples. Hyperspectral images of cooked beef were acquired in the wavelength range of 400–1000 nm and the freshness state of all samples was divided into three classes (freshness, medium freshness, and spoilage) using the measured total viable count (TVC) of bacteria. Fifteen feature spectra variables were extracted by random frog (RF); based on this, six optimal wavelength variables were further selected by correlation analysis (CA). Partial least squares (PLS) and least squares–support vector machine (LS-SVM) classification models were established using different spectral variables. The results indicated that the performance of the RF-CA-LS-SVM classification model with a high overall classification accuracy of 97.14%, the results of sensitivity and specificity in the range of 0.92–1, and the κ coefficient of 0.9575 in the prediction set were obviously superior to other models. Spoiled samples were further obtained using a RF-CA-LS-SVM model, and then six feature images were extracted and further fused by principal component analysis (PCA). A PC3 image was used to segment successfully the contaminated areas from normal areas of cooked beef images using the Otsu threshold algorithm. The results demonstrated that HSI has great potential in classifying the freshness of cooked beef and identifying the contaminated areas. This current study provides a foundational basis for the classification and grading of meat production in further online detection.

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