scholarly journals Hyperspectral reflectance for non-invasive early detection of black shank disease in flue-cured tobacco

2021 ◽  
Author(s):  
Austin Hayes ◽  
T. David Reed
Keyword(s):  
Machine Learning ◽  
Broad Band ◽  
Phytophthora Nicotianae ◽  
Hyperspectral Reflectance ◽  
Black Shank ◽  
Linear Discriminant ◽  
Machine Learning Classification ◽  
Band Index ◽  
Intensively Managed ◽  
Reflectance Data

Flue-cured tobacco (Nicotiana tabacum L.) is a high value-per-acre crop that is intensively managed to optimise the yield of high-quality cured leaf. A 15-day study assessed the potential of hyperspectral reflectance data for detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Hyperspectral reflectance data were taken from a commercial flue-cured tobacco field with a progressing black shank infestation. The effort encompassed two key objectives. First, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Second, evaluate the model’s ability to separate pre-symptomatic plants from healthy plants. Two hyperspectral indices were developed to detect black shank incidence based on differences in the spectral profiles of asymptomatic flue-cured tobacco plants compared to those with black shank symptoms. While one of the indices is a broad-band index and the other uses narrow wavelength values, the statistical difference between the two indices was not significant and both provided an accurate classification of symptomatic plants. Further analysis of the indices showed significant differences between the index values of healthy and symptomatic plants (α = 0.05). In addition, the indices were able to detect black shank symptoms pre-symptomatically (α = 0.09). Subspace linear discriminant analysis, a machine learning classification, was also used for prediction of black shank incidence with up to 85.7% classification accuracy. The implications of using either spectral indices or machine learning for classification for future black shank research are discussed.

PSIX-15 Assessment of machine learning algorithms for prediction of Aleutian disease in American mink

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 264-265
Author(s):  
Duy Ngoc Do ◽  
Guoyu Hu ◽  
Younes Miar
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Models ◽  
American Mink ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Enzyme Linked Immunosorbent Assay ◽  
Linear Discriminant ◽  
Machine Learning Classification

Abstract American mink (Neovison vison) is the major source of fur for the fur industries worldwide and Aleutian disease (AD) is causing severe financial losses to the mink industry. Different methods have been used to diagnose the AD in mink, but the combination of several methods can be the most appropriate approach for the selection of AD resilient mink. Iodine agglutination test (IAT) and counterimmunoelectrophoresis (CIEP) methods are commonly employed in test-and-remove strategy; meanwhile, enzyme-linked immunosorbent assay (ELISA) and packed-cell volume (PCV) methods are complementary. However, using multiple methods are expensive; and therefore, hindering the corrected use of AD tests in selection. This research presented the assessments of the AD classification based on machine learning algorithms. The Aleutian disease was tested on 1,830 individuals using these tests in an AD positive mink farm (Canadian Centre for Fur Animal Research, NS, Canada). The accuracy of classification for CIEP was evaluated based on the sex information, and IAT, ELISA and PCV test results implemented in seven machine learning classification algorithms (Random Forest, Artificial Neural Networks, C50Tree, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) using the Caret package in R. The accuracy of prediction varied among the methods. Overall, the Random Forest was the best-performing algorithm for the current dataset with an accuracy of 0.89 in the training data and 0.94 in the testing data. Our work demonstrated the utility and relative ease of using machine learning algorithms to assess the CIEP information, and consequently reducing the cost of AD tests. However, further works require the inclusion of production and reproduction information in the models and extension of phenotypic collection to increase the accuracy of current methods.

scholarly journals Machine-Learning for Mineral Identification and Ore Estimation From Hyperspectral Imagery in Tin-Tungsten Deposits

2021 ◽  
Author(s):  
Agustin Lobo ◽  
Emma Garcia ◽  
Gisela Barroso ◽  
David Martí ◽  
Jose-Luis Fernandez-Turiel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Hyperspectral Images ◽  
Linear Discriminant ◽  
Machine Learning Classification ◽  
Vector Machines ◽  
Ore Mineralization ◽  
Mine Excavation ◽  
Tungsten Mine ◽  
Set Up

This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin-tungsten mine excavation faces using machine-learning classification. We compiled a set of hand samples of minerals of interest from a tin-tungsten mine and analyzed two types of hyperspectral images: 1) images acquired with a laboratory set-up under close-to-optimal conditions; and 2) scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. We have analyzed the following minerals: cassiterite (tin ore), wolframite (tungsten ore), chalcopyrite, malachite, muscovite, and quartz. Classification (Linear Discriminant Analysis, Singular Vector Machines and Random Forest) of laboratory spectra had a very high overall accuracy rate (98%), slightly lower if the 450 – 950 nm and 950 – 1780 nm ranges are considered independently, and much lower (74.5%) for simulated conventional RGB imagery. Classification accuracy for the simulation was lower than in the laboratory but still high (85%), likely a consequence of the lower spatial resolution. All three classification methods performed similarly in this case, with Random Forest producing results of slightly higher accuracy. The user’s accuracy for wolframite was 85%, but cassiterite was often confused with wolframite (user’s accuracy: 70%). A lumped ore category achieved 94.9% user’s accuracy. Our study confirms the suitability of hyperspectral imaging to record the spatial distribution of ore mineralization in progressing tungsten-tin mine faces.

Utility of Hyperspectral Reflectance for Differentiating Soybean (Glycine max) and Six Weed Species

2009 ◽  
Vol 23 (1) ◽  
pp. 108-119 ◽  
Author(s):  
Cody J. Gray ◽  
David R. Shaw ◽  
Lori M. Bruce
Keyword(s):  
Short Wavelength ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Species Discrimination ◽  
Species Differentiation ◽  
Electromagnetic Spectrum ◽  
Hyperspectral Reflectance ◽  
Weed Species ◽  
Linear Discriminant ◽  
Reflectance Data

Reflectance data were subjected to a variety of analysis methods to determine the utility of hyperspectral reflectance for differentiating soybean, soil, and six weed species commonly found in Mississippi agricultural fields. Weed species evaluated were hemp sesbania, palmleaf morningglory, pitted morningglory, prickly sida, sicklepod, and smallflower morningglory. Hyperspectral reflectance data were collected from mature plant leaves three times in 2002 and two times in 2003. Vegetation indices were calculated and subjected to principal component analysis (PCA) and linear discriminant analysis (LDA). The PCA, using vegetation indices, produced the poorest classification accuracies for the plant species studied, generally less than 50%, whereas LDA resulted in classification accuracies greater than those from PCA. Best spectral band combination (BSBC) provided the greatest classification accuracies, with all better than 80% for all data sets. The BSBC indicated three wavelength bands of interest for species discrimination in the short wavelength infrared portion of the electromagnetic spectrum, which are not commonly used in current vegetation indices for species differentiation. These areas of interest were located from 1,445 to 1,475 nm, 2,030 to 2,090 nm, and 2,115 to 2,135 nm. The top 10 wavelengths determined by BSBC were then added to the vegetation indices and reanalyzed using PCA and LDA. Classification accuracies increased for all species when these wavelengths were added rather than using vegetation indices alone, suggesting greater crop and weed species differentiation can be obtained when using sensors that include these wavelength regions of the short wavelength infrared portion of the electromagnetic spectrum.

P09.01 Radiomics-based machine learning approach in differentiation between vestibular schwannoma and meningioma in the cerebellopontine angle

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii26-ii26
Author(s):  
Y Zhang ◽  
C Chen ◽  
J Xu
Keyword(s):  
Machine Learning ◽  
Vestibular Schwannoma ◽  
Cerebellopontine Angle ◽  
Gradient Boosting ◽  
Surgical Approaches ◽  
Support Vector ◽  
Selection Methods ◽  
Linear Discriminant ◽  
Machine Learning Classification ◽  
Validation Set

Abstract BACKGROUND Vestibular schwannoma (VS) and meningioma are the most two common tumors in the cerebellopontine angle (CPA). Accurate preoperative differentiation of the two lesions is important due to their different surgical approaches and outcomes for the preservation of hearing and facial nerve function. Magnetic resonance (MR) scan is commonly performed to preoperatively evaluate CPA tumors and to differentiate VS from meningioma in clinical routine. However, in some cases, overlaps of conventional MR imaging patterns between the two lesions could make preoperative diagnosis challenging. The purpose of this study is to investigate the ability of radiomics, a novel method providing objective and quantitative information beyond visual assessment, in differentiation between VS and meningioma located at CPA using machine learning technology. MATERIAL AND METHODS This retrospective study enrolled eligible patients who were diagnosed with VS (N = 50) or meningioma (N = 41) in the CPA. A set of mineable three-dimensional radiomic parameters were extracted from preoperative contrast-enhanced T1-weighted images. Optimal features were selected first with three selection methods including distance correlation, least absolute shrinkage and selection operator (LASSO) and gradient boosting decision tree (GBDT). Then three machine learning classification algorithms, namely linear discriminant analysis (LDA), support vector machine (SVM) and random forest were employed to build discriminative models. Area under the curve (AUC), accuracy, sensitivity and specificity were calculated to assess the performance of each model. RESULTS Nine models were established with different combinations of selection methods and machine learning classifiers. Three classifiers with the suitable selection method all represented feasible ability in differentiation with AUC more than 0.86 in the validation set, and LDA-based models seemed to show better diagnostic performance than those based on the other two classifiers. The combination of LASSO and LDA classifier was found to show the highest AUC of 0.942 in the validation set. CONCLUSION Radiomics-based models via machine learning approaches could potentially be utilized to assist in preoperative differentiation between VS and meningioma in the CPA.

scholarly journals Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

2021 ◽  
Vol 11 ◽  
Author(s):  
Mohsen Yoosefzadeh-Najafabadi ◽  
Hugh J. Earl ◽  
Dan Tulpan ◽  
John Sulik ◽  
Milad Eskandari
Keyword(s):  
Machine Learning ◽  
Seed Yield ◽  
High Throughput ◽  
Early Growth ◽  
Support Vector ◽  
Growth Stages ◽  
Hyperspectral Reflectance ◽  
Ensemble Techniques ◽  
Soybean Genotypes ◽  
Reflectance Data

Recent substantial advances in high-throughput field phenotyping have provided plant breeders with affordable and efficient tools for evaluating a large number of genotypes for important agronomic traits at early growth stages. Nevertheless, the implementation of large datasets generated by high-throughput phenotyping tools such as hyperspectral reflectance in cultivar development programs is still challenging due to the essential need for intensive knowledge in computational and statistical analyses. In this study, the robustness of three common machine learning (ML) algorithms, multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were evaluated for predicting soybean (Glycine max) seed yield using hyperspectral reflectance. For this aim, the hyperspectral reflectance data for the whole spectra ranged from 395 to 1005 nm, which were collected at the R4 and R5 growth stages on 250 soybean genotypes grown in four environments. The recursive feature elimination (RFE) approach was performed to reduce the dimensionality of the hyperspectral reflectance data and select variables with the largest importance values. The results indicated that R5 is more informative stage for measuring hyperspectral reflectance to predict seed yields. The 395 nm reflectance band was also identified as the high ranked band in predicting the soybean seed yield. By considering either full or selected variables as the input variables, the ML algorithms were evaluated individually and combined-version using the ensemble–stacking (E–S) method to predict the soybean yield. The RF algorithm had the highest performance with a value of 84% yield classification accuracy among all the individual tested algorithms. Therefore, by selecting RF as the metaClassifier for E–S method, the prediction accuracy increased to 0.93, using all variables, and 0.87, using selected variables showing the success of using E–S as one of the ensemble techniques. This study demonstrated that soybean breeders could implement E–S algorithm using either the full or selected spectra reflectance to select the high-yielding soybean genotypes, among a large number of genotypes, at early growth stages.

scholarly journals Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin–Tungsten Deposits: Simulation under Indoor Conditions

2021 ◽  
Vol 13 (16) ◽  
pp. 3258 ◽  
Author(s):  
Agustin Lobo ◽  
Emma Garcia ◽  
Gisela Barroso ◽  
David Martí ◽  
Jose-Luis Fernandez-Turiel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Hyperspectral Images ◽  
Linear Discriminant ◽  
Machine Learning Classification ◽  
Ore Mineralization ◽  
Mine Excavation ◽  
Tungsten Mine ◽  
Set Up ◽  
Indoor Conditions

This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin–tungsten mine excavation faces using machine learning classification. We compiled a set of hand samples of minerals of interest from a tin–tungsten mine and analyzed two types of hyperspectral images: (1) images acquired with a laboratory set-up under close-to-optimal conditions, and (2) a scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. We have analyzed the following minerals: cassiterite (tin ore), wolframite (tungsten ore), chalcopyrite, malachite, muscovite, and quartz. Classification (Linear Discriminant Analysis, Singular Vector Machines and Random Forest) of laboratory spectra had a very high overall accuracy rate (98%), slightly lower if the 450–950 nm and 950–1650 nm ranges are considered independently, and much lower (74.5%) for simulated conventional RGB imagery. Classification accuracy for the simulation was lower than in the laboratory but still high (85%), likely a consequence of the lower spatial resolution. All three classification methods performed similarly in this case, with Random Forest producing results of slightly higher accuracy. The user’s accuracy for wolframite was 85%, but cassiterite was often confused with wolframite (user’s accuracy: 70%). A lumped ore category achieved 94.9% user’s accuracy. Our study confirms the suitability of hyperspectral imaging to record the spatial distribution of ore mineralization in progressing tungsten–tin mine faces.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

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