scholarly journals Detection of Scots Pine Single Seed in Optoelectronic System of Mobile Grader: Mathematical Modeling

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 240
Author(s):  
Arthur Novikov ◽  
Viсtor Lisitsyn ◽  
Mulualem Tigabu ◽  
Paweł Tylek ◽  
Sergey Chuchupal
Keyword(s):  
Scots Pine ◽  
Radiation Source ◽  
Near Infrared ◽  
Seed Quality ◽  
Diagnostic System ◽  
Optical Beam ◽  
Detection Accuracy ◽  
Optoelectronic System ◽  
Single Seed ◽  
Beam Formation

The development of mobile optoelectronic graders for separating viable seeds by spectrometric properties with high detection accuracy represents a very relevant direction of development for seed handling operations. Here, the main parameters of the radiation source and receiver for detecting a single seed in the diagnostic system of a mobile grader were modeled based on the principles of technical optics using Scots pine (Pinus sylvestris L.) seeds as a case study. Among the pine seeds in the seed batch, there are fossilized and empty seeds that are exactly the same in geometric and gravitational parameters as live seeds. For their separation from the seed batch, data from spectrometric studies in the near-infrared (980 nm) region can be used. To substantiate the parameters of the light source, a geometric optical model of optical beam formation was considered, while the energy model of optical beam formation was considered to substantiate the parameters of the light detector. The results of this study show that the signal value depended on the orientation of a single seed relative to the recording window. The beam angle from the radiation source should be within 45 degrees. The difference between the optical streams should be 50 microns, which made it possible to clearly detect the signal at a standard noise level of 15 microns and signal-to-noise detection accuracy ratio of 56.3 dB. This study expands theoretical knowledge in the field of the spectrometric properties of a single seed, considering the cases of its orientation relative to the optical beam, which affected the effective area of detection of the seed. The obtained data on the location of the main elements of the diagnostic system will speed up the design of mobile optoelectronic graders, and the development of a contemporary protocol for improving Scots pine seed quality.

scholarly journals Protein, weight, and oil prediction by single-seed near-infrared spectroscopy for selection of seed quality and yield traits in pea (Pisum sativum).

2020 ◽  
Author(s):  
Gokhan Hacisalihoglu ◽  
Jelani Freeman ◽  
Paul R. Armstrong ◽  
Brad W. Seabourn ◽  
Lyndon D. Porter ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Pisum Sativum ◽  
Protein Content ◽  
Near Infrared Spectroscopy ◽  
Seed Weight ◽  
Oil Content ◽  
Near Infrared ◽  
Seed Quality ◽  
Single Seed ◽  
Selection Of

Abstract Background: Pea (Pisum sativum) is a prevalent cool season crop that produces seeds valued for high protein content. Modern cultivars have incorporated several traits that improved harvested yield. However, progress toward improving seed quality has received less emphasis, in part due to the lack of tools for easily and rapidly measuring seed traits. In this study we evaluated the accuracy of single-seed near-infrared spectroscopy (NIRS) for measuring pea seed weight, protein, and oil content. A total of 96 diverse pea accessions were analyzed using both single-seed NIRS and wet chemistry methods. To demonstrate field relevance, the single-seed NIRS protein prediction model was used to determine the impact of seed treatments and foliar fungicides on protein content of harvested dry peas in a field trial. Results: External validation of Partial Least Squares (PLS) regression models showed high prediction accuracy for protein and weight (R2 = 0.94 for both) and less accuracy for oil (R2 = 0.75). Single seed weight was not significantly correlated with protein or oil content in contrast to previous reports. In the field study, the single-seed NIRS predicted protein values were within 1% of an independent analytical reference measurement and were sufficiently precise to detect small treatment effects. Conclusion: The high accuracy of protein and weight estimation show that single-seed NIRS could be used in the dual selection of high protein, high weight peas early in the breeding cycle allowing for faster genetic advancement toward improved pea nutritional quality.

scholarly journals Protein, weight, and oil prediction by single‐seed near‐infrared spectroscopy for selection of seed quality and yield traits in pea ( Pisum sativum )

2020 ◽  
Vol 100 (8) ◽  
pp. 3488-3497 ◽  
Author(s):  
Gokhan Hacisalihoglu ◽  
Jelani Freeman ◽  
Paul R. Armstrong ◽  
Brad W Seabourn ◽  
Lyndon D Porter ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Pisum Sativum ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Seed Quality ◽  
Yield Traits ◽  
Single Seed ◽  
Selection Of

Infrared fluorescence lymphography in experimental and clinical practice

2018 ◽  
Vol 17 (2) ◽  
pp. 84-91 ◽  
Author(s):  
G. V. Papayan ◽  
A. L. Akopov ◽  
P. A. Antonyan ◽  
A. A. Ilin ◽  
N. N. Petrishchev
Keyword(s):  
Lymph Nodes ◽  
Near Infrared ◽  
Lymphatic System ◽  
Lymphatic Vessels ◽  
Diagnostic System ◽  
Biological Tissues ◽  
Peritumoral Injection ◽  
Intradermal Administration ◽  
Nir Fluorescence ◽  
Real Time Visualization

Introduction. Near infrared (NIR) fluorescent diagnostics is promising due to a deeper penetration into biological tissues. Material and methods. In experiments on rabbits and in clinical studies evaluation the lymphatic system with the use of the instrument complex FLUM-808 was analysed. Results. For visualization of the lymphatic vessels of the skin, the intradermal administration of ICG, dissolved in 20 % albumin in the order of 0.02 mg/ml, is optimal. Peritumoral injection of ICG allows visualizing sentinel lymph nodes in patients with lung cancer. Conclusions. The developed NIR fluorescence diagnostic system FLUM-808 allows to real time visualization of lymphatic vessels and lymph nodes.

Single-seed respiration: a new method to assess seed quality

2013 ◽  
Vol 41 (3) ◽  
pp. 420-438 ◽  
Author(s):  
K.J. Bradford ◽  
P. Bello ◽  
J.-C. Fu ◽  
M. Barros
Keyword(s):  
Seed Quality ◽  
New Method ◽  
Single Seed ◽  
Seed Respiration

scholarly journals A Gated Recurrent Units (GRU)-Based Model for Early Detection of Soybean Sudden Death Syndrome through Time-Series Satellite Imagery

2020 ◽  
Vol 12 (21) ◽  
pp. 3621
Author(s):  
Luning Bi ◽  
Guiping Hu ◽  
Muhammad Mohsin Raza ◽  
Yuba Kandel ◽  
Leonor Leandro ◽  
...  
Keyword(s):  
Time Series ◽  
Early Detection ◽  
Sudden Death ◽  
Near Infrared ◽  
Time Series Data ◽  
Sudden Death Syndrome ◽  
Plant Diseases ◽  
Series Data ◽  
Detection Accuracy ◽  
Test Accuracy

In general, early detection and timely management of plant diseases are essential for reducing yield loss. Traditional manual inspection of fields is often time-consuming and laborious. Automated imaging techniques have recently been successfully applied to detect plant diseases. However, these methods mostly focus on the current state of the crop. This paper proposes a gated recurrent unit (GRU)-based model to predict soybean sudden death syndrome (SDS) disease development. To detect SDS at a quadrat level, the proposed method uses satellite images collected from PlanetScope as the training set. The pixel image data include the spectral bands of red, green, blue and near-infrared (NIR). Data collected during the 2016 and 2017 soybean-growing seasons were analyzed. Instead of using individual static imagery, the GRU-based model converts the original imagery into time-series data. SDS predictions were made on different data scenarios and the results were compared with fully connected deep neural network (FCDNN) and XGBoost methods. The overall test accuracy of classifying healthy and diseased quadrates in all methods was above 76%. The test accuracy of the FCDNN and XGBoost were 76.3–85.5% and 80.6–89.2%, respectively, while the test accuracy of the GRU-based model was 82.5–90.4%. The calculation results show that the proposed method can improve the detection accuracy by up to 7% with time-series imagery. Thus, the proposed method has the potential to predict SDS at a future time.

Prediction of Stilbene Content from Heartwood Increment Cores of Scots Pine Using near Infrared Spectroscopy Methodology

2016 ◽  
Vol 24 (6) ◽  
pp. 517-528 ◽  
Author(s):  
Susanna Pulkka ◽  
Vincent Segura ◽  
Anni Harju ◽  
Tarja Tapanila ◽  
Johanna Tanner ◽  
...  
Keyword(s):  
Scots Pine ◽  
High Throughput ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Tree Breeding ◽  
Decay Resistance ◽  
Increment Core ◽  
Automatic Selection ◽  
Non Destructive ◽  
Selection Of

High-throughput and non-destructive methods for quantifying the content of the stilbene compounds of Scots pine ( Pinus sylvestris L.) heartwood are needed in the breeding for decay resistance of heartwood timber. In this study, near infrared (NIR) spectroscopy calibrations were developed for a large collection of solid heartwood increment core samples in order to predict the amount of the stilbene pinosylvin (PS), its monomethyl ether (PSM) and their sum (STB). The resulting models presented quite accurate predictions in an independent validation set with R2V values ranging between 0.79 and 0.91. The accuracy of the models strongly depended on the chemical being calibrated, with the lowest accuracy for PS, intermediate accuracy for PSM and highest accuracy for STB. The effect of collecting one, two or more (up to five) spectra per sample on the calibration models was studied and it was found that averaging multiple spectra yielded better accuracy as it may account for the heterogeneity of wood along the increment core within and between rings. Several statistical pretreatments of the spectra were tested and an automatic selection of wavenumbers prior to calibration. Without the automatic selection of wavenumbers, a first derivative of normalised spectra yielded the best accuracies, whereas after the automatic selection of wavenumbers, no particular statistical pretreatment appeared to yield better results than any other. Finally, the automatic selection of wavenumbers slightly improved the accuracy of the models for all traits. These results demonstrate the potential of NIR spectroscopy as a high-throughput and non-destructive phenotyping technique in tree breeding for the improvement of decay resistance in heartwood timber.

scholarly journals Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis

2019 ◽  
Vol 9 (8) ◽  
pp. 1530 ◽  
Author(s):  
Guangjun Qiu ◽  
Enli Lü ◽  
Ning Wang ◽  
Huazhong Lu ◽  
Feiren Wang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Seed Quality ◽  
Sweet Corn ◽  
Full Range ◽  
Classification Model ◽  
Support Vector ◽  
Corn Seed ◽  
Discriminant Analyses

Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.

scholarly journals Single-Kernel FT-NIR Spectroscopy for Detecting Maturity of Cucumber Seeds Using a Multiclass Hierarchical Classification Strategy

2019 ◽  
Vol 9 (23) ◽  
pp. 5058 ◽  
Author(s):  
Zeng ◽  
Lü ◽  
Qiu ◽  
Lu ◽  
Jiang
Keyword(s):  
Near Infrared ◽  
Seed Quality ◽  
Hierarchical Classification ◽  
Nir Spectroscopy ◽  
Predictive Performance ◽  
Nondestructive Method ◽  
Maturation Stage ◽  
Seed Industry ◽  
Intrinsic Quality ◽  
Classification Strategy

The maturity of seeds at harvest determines their intrinsic quality characteristics such as longevity and vigor, and these characteristics are dominant factors for seed quality evaluation in the seed industry. However, little information is available on how to identify and further classify the maturation stage of seeds in a way that is nondestructive, precise, rapid, and inexpensive, while also exactly meeting the need for the uniform control of seed performance in the seed industry to improve crop yield. This study demonstrated a nondestructive method for detecting seed maturity by using the single-kernel near-infrared spectroscopy (SK-NIRS) technique. The results showed that five classes of cucumber seeds with different maturation levels can be distinguished successfully. A tree-structured hierarchical classification strategy consisting of one soft independent modeling of class analogy (SIMCA) model and three partial least squares discriminant analysis (PLS-DA) models were proposed ending up with 99.69% of the overall classification accuracy and 0.9961 of Cohen’s kappa in the test set, and its predictive performance was superior to both SIMCA and PLS-DA for direct multiclass classification. SK-NIRS in combination with a multiclass hierarchical classification strategy was proved to be both intuitive and efficient in classifying cucumber seeds according to maturation levels.

scholarly journals Application of Near-infrared Spectroscopy and Multiple Spectral Algorithms to Explore the Effect of Soil Particle Sizes on Soil Nitrogen Detection

Molecules ◽  
2019 ◽  
Vol 24 (13) ◽  
pp. 2486 ◽  
Author(s):  
Shupei Xiao ◽  
Yong He
Keyword(s):  
Plant Growth ◽  
Soil Nitrogen ◽  
Precision Agriculture ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soil Particle ◽  
Detection Accuracy ◽  
Particle Sizes ◽  
Reflectivity Spectra ◽  
Nitrogen Detection

Soil nitrogen is the key parameter supporting plant growth and development; it is also the material basis of plant growth. An accurate grasp of soil nitrogen information is the premise of scientific fertilization in precision agriculture, where near-infrared (NIR) spectroscopy is widely used for rapid detection of soil nutrients. In this study, the variation law of soil NIR reflectivity spectra with soil particle sizes was studied. Moreover, in order to precisely study the effect of particle size on soil nitrogen detection by NIR, four different spectra preprocessing methods and five different chemometric modeling methods were used to analyze the soil NIR spectra. The results showed that the smaller the soil particle sizes, the stronger the soil NIR reflectivity spectra. Besides, when the soil particle sizes ranged 0.18–0.28 mm, the soil nitrogen prediction accuracy was the best based on the partial least squares (PLS) model with the highest Rp2 of 0.983, the residual predictive deviation (RPD) of 6.706. The detection accuracy was not ideal when the soil particle sizes were too big (1–2 mm) or too small (0–0.18 mm). In addition, the relationship between the mixing spectra of six different soil particle sizes and the soil nitrogen detection accuracy was studied. It was indicated that the larger the gap between soil particle sizes, the worse the accuracy of soil nitrogen detection. In conclusion, soil nitrogen detection precision was affected by soil particle sizes to a large extent. It is of great significance to optimize the pre-treatments of soil samples to realize rapid and accurate detection by NIR spectroscopy.

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