scholarly journals Prediction of Sweet Corn Seed Germination Based on Hyperspectral Image Technology and Multivariate Data Regression

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4744
Author(s):  
Huawei Cui ◽  
Zhishang Cheng ◽  
Peng Li ◽  
Aimin Miao
Keyword(s):  
Seed Germination ◽  
Correlation Coefficient ◽  
Root Length ◽  
Sweet Corn ◽  
Hyperspectral Image ◽  
Principal Component Regression ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Seed Vigor ◽  
Seedling Length

Vigor identification in sweet corn seeds is important for seed germination, crop yield, and quality. In this study, hyperspectral image (HSI) technology integrated with germination tests was applied for feature association analysis and germination performance prediction of sweet corn seeds. In this study, 89 sweet corn seeds (73 for training and the other 16 for testing) were studied and hyperspectral imaging at the spectral range of 400–1000 nm was applied as a nondestructive and accurate technique to identify seed vigor. The root length and seedling length which represent the seed vigor were measured, and principal component regression (PCR), partial least squares (PLS), and kernel principal component regression (KPCR) were used to establish the regression relationship between the hyperspectral feature of seeds and the germination results. Specifically, the relevant characteristic band associated with seed vigor based on the highest correlation coefficient (HCC) was constructed for optimal wavelength selection. The hyperspectral data features were selected by genetic algorithm (GA), successive projections algorithm (SPA), and HCC. The results indicated that the hyperspectral data features obtained based on the HCC method have better prediction results on the seedling length and root length than SPA and GA. By comparing the regression results of KPCR, PCR, and PLS, it can be concluded that the hyperspectral method can predict the root length with a correlation coefficient of 0.7805. The prediction results of different feature selection and regression algorithms for the seedling length were up to 0.6074. The results indicated that, based on hyperspectral technology, the prediction of seedling root length was better than that of seed length.

scholarly journals Hyperspectral Image Classification with Localized Graph Convolutional Filtering

2021 ◽  
Vol 13 (3) ◽  
pp. 526
Author(s):  
Shengliang Pu ◽  
Yuanfeng Wu ◽  
Xu Sun ◽  
Xiaotong Sun
Keyword(s):  
Hyperspectral Image ◽  
Principal Component ◽  
Representation Learning ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Spectral Graph Theory ◽  
Feature Reduction ◽  
Graph Representation ◽  
Novel Method ◽  
Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

scholarly journals Quality Metrics Evaluation of Hyperspectral Images

2014 ◽  
Vol XL-8 ◽  
pp. 1221-1226 ◽  
Author(s):  
A. K. Singh ◽  
H. V. Kumar ◽  
G. R. Kadambi ◽  
J. K. Kishore ◽  
J. Shuttleworth ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Quality Parameters ◽  
Quality Metrics ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Error Ratio ◽  
Classified Image

In this paper, the quality metrics evaluation on hyperspectral images has been presented using k-means clustering and segmentation. After classification the assessment of similarity between original image and classified image is achieved by measurements of image quality parameters. Experiments were carried out on four different types of hyperspectral images. Aerial and spaceborne hyperspectral images with different spectral and geometric resolutions were considered for quality metrics evaluation. Principal Component Analysis (PCA) has been applied to reduce the dimensionality of hyperspectral data. PCA was ultimately used for reducing the number of effective variables resulting in reduced complexity in processing. In case of ordinary images a human viewer plays an important role in quality evaluation. Hyperspectral data are generally processed by automatic algorithms and hence cannot be viewed directly by human viewers. Therefore evaluating quality of classified image becomes even more significant. An elaborate comparison is made between k-means clustering and segmentation for all the images by taking Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Maximum Squared Error, ratio of squared norms called L2RAT and Entropy. First four parameters are calculated by comparing the quality of original hyperspectral image and classified image. Entropy is a measure of uncertainty or randomness which is calculated for classified image. Proposed methodology can be used for assessing the performance of any hyperspectral image classification techniques.

scholarly journals Effects of different levels of NaCl on the seed germination of Cyamopsis tetragonoloba L.

2020 ◽  
Vol 49 (3) ◽  
pp. 625-632
Author(s):  
Muhammad Akram ◽  
Mehwish Zahid ◽  
Abu Bakr Umer Farooq ◽  
Muhammad Nafees ◽  
Atta Rasool
Keyword(s):  
Seed Germination ◽  
Germination Percentage ◽  
Seed Vigor ◽  
Stress Index ◽  
Cyamopsis Tetragonoloba ◽  
Vigor Index ◽  
Seedling Length ◽  
Salinity Levels ◽  
Mean Germination Time ◽  
Different Levels

Effects of four different levels of NaCl (05, 10, 15, 20 dS/m) on the seed germination of cluster bean (Cyamopsis tetragonoloba L.) cultivars were investigated. Only distilled water was used in the control. The experiment was conducted in the Petri dishes under laboratory condition and laid out in CRD comprising five replicates. The results indicated that the germination index (GI), seed vigor index (SVI), germination stress index and seedling length (radicle and plumule) decreased with increase of salinity levels and germination percentage (GP) was reduced significantly at the highest level of salinity (200 mM). However, mean germination time (MGT) and time taken for 50% germination (T50) increased by increasing salinity levels. Correlation coefficient between all possible combinations was estimated and the results indicated that GP, GI, MGT, T50, and SVI had significant positive or negative correlation with each other.

scholarly journals Nutritional Characteristics and Seed Germination Improvement of the Forage Shrub Retama sphaerocarpa (L.) Boiss

2020 ◽  
Vol 69 (3-4) ◽  
pp. 53-60
Author(s):  
Abdenour Kheloufi ◽  
Lahouaria Mounia Mansouri ◽  
Mohamed Djelilate ◽  
Mourad Touka ◽  
Abdallah Chater ◽  
...  
Keyword(s):  
Seed Germination ◽  
Sulphuric Acid ◽  
Nutritive Value ◽  
Seedling Emergence ◽  
Principal Component ◽  
Germination Percentage ◽  
Neutral Detergent Fiber ◽  
Retama Sphaerocarpa ◽  
Seedling Length ◽  
Germination Ability

SummaryRetama sphaerocarpa shrubs form populations that can be an important forage resource during the dry season when pasture shortages are common in certain arid and semi-arid Mediterranean basin regions. The leaves of R. sphaerocarpa were analyzed for dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and acid detergent lignin (ADL) contents. Leaves were also analyzed for the concentration of macro- (P, K, Ca and Mg) and microelements (Mn, Zn, Fe, and Na). According to the contents of CP, NDF, ADF and ADL in the leaves examined, this species could strike an appropriate balance between available feed ingredients for daily nutritional needs of animals. The contents of Ca, K, Na, P, Fe, Mg, and Zn in Retama sphaerocarpa shrubs were found to be high, compared to a number of other forage shrub species. Conversely, the rate of natural regeneration of this shrub in situ was estimated at 2-5%. R. sphaerocarpa seeds are affected by seed coat dormancy that prevents seed germination under natural conditions. The seed germination was assessed at a laboratory after the chemical scarification of seeds by concentrated sulphuric acid in the duration range of 0 min and 240 min. The principal component analysis of data related to the germination ability and seedling emergence showed that the best pretreatment was 120 min immersion in sulphuric acid at 25°C (± 2°C), resulting in 86% of the final germination percentage (FGP) and 14.6 cm of the seedling length (SL). According to the results obtained, this species could be considered a ruminant feed of great nutritive value when drought decreases grazing herbaceous biomass yields. These results should encourage farmers and foresters to integrate R. sphaerocarpa into their planting programs.

scholarly journals An Effective Classification Scheme for Hyperspectral Image Based on Superpixel and Discontinuity Preserving Relaxation

2019 ◽  
Vol 11 (10) ◽  
pp. 1149 ◽  
Author(s):  
Fuding Xie ◽  
Cunkuan Lei ◽  
Jun Yang ◽  
Cui Jin
Keyword(s):  
Spatial Structure ◽  
Classification Scheme ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Novel Technique ◽  
Classification Framework ◽  
Comparison Results ◽  
Simple Linear Iterative Clustering ◽  
Land Covers

Hyperspectral image (HSI) classification is one of the most active topics in remote sensing. However, it is still a nontrivial task to classify the hyperspectral data accurately, since HSI always suffers from a large number of noise pixels, the complexity of the spatial structure of objects and the spectral similarity between different objects. In this study, an effective classification scheme for hyperspectral image based on superpixel and discontinuity preserving relaxation (DPR) is proposed to discriminate land covers of interest. A novel technique for measuring the similarity of a pair of pixels in HSI is suggested to improve the simple linear iterative clustering (SLIC) algorithm. Unlike the existing application of SLIC technique to HSI, the improved SLIC algorithm can be directly used to segment HSI into superpixels without using principal component analysis in advance, and is free of parameters. Furthermore, the proposed three-step classification scheme explores how to effectively use the global spectral information and local spatial structure of hyperspectral data for HSI classification. Compared with the existing two-step classification framework, the use of DPR technology in preprocessing significantly improves the classification accuracy. The effectiveness of the proposed method is verified on three public real hyperspectral datasets. The comparison results of several competitive methods show the superiority of this scheme.

scholarly journals Near Infrared Hyperspectral Imaging in Transmission Mode: Assessing the Weathering of Thin Wood Samples

2016 ◽  
Vol 24 (6) ◽  
pp. 595-604 ◽  
Author(s):  
Knut Arne Smeland ◽  
Kristian Hovde Liland ◽  
Jakub Sandak ◽  
Anna Sandak ◽  
Lone Ross Gobakken ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Near Infrared ◽  
Prediction Models ◽  
Hyperspectral Image ◽  
Principal Component Regression ◽  
Principal Component ◽  
Transmission Mode ◽  
Infrared Wavelength ◽  
Robust Prediction ◽  
Wooden Surface

Untreated wooden surfaces degrade when exposed to natural weathering. In this study thin wood samples were studied for weather degradation effects utilising a hyperspectral camera in the near infrared wavelength range in transmission mode. Several sets of samples were exposed outdoors for time intervals from 0 days to 21 days, and one set of samples was exposed to ultraviolet (UV) radiation in a laboratory chamber. Spectra of earlywood and latewood were extracted from the hyperspectral image cubes using a principal component analysis-based masking algorithm. The degradation was modelled as a function of UV solar radiation with four regression techniques, partial least squares, principal component regression, Ridge regression and Tikhonov regression. It was found that all the techniques yielded robust prediction models on this dataset. The result from the study is a first step towards a weather dose model determined by temperature and moisture content on the wooden surface in addition to the solar radiation.

scholarly journals Assessment of the physiological potential of super sweet corn seeds

2013 ◽  
Vol 35 (3) ◽  
pp. 340-346 ◽  
Author(s):  
Renata Oliveira Alvarenga ◽  
Julio Marcos-Filho ◽  
Tathiana Silva Timóteo
Keyword(s):  
Seed Quality ◽  
Sweet Corn ◽  
Seedling Emergence ◽  
Primary Root ◽  
Accelerated Aging ◽  
Cold Test ◽  
Seed Vigor ◽  
Corn Seed ◽  
Control Programs ◽  
Seedling Length

The assessment of physiological potential is essential in seed quality control programs. This study compared the sensitivity of different procedures for evaluating super sweet corn seed vigor, focusing on the primary root protrusion test. Six seed lots, each of the SWB 551 and SWB 585 hybrids, were used. Seed physiological potential was evaluated by germination and vigor tests (speed of germination, traditional and saturated salt accelerated aging, cold test, seedling length, seedling emergence and primary root protrusion). Primary root protrusion was evaluated every 12 hours at 15 °C, 20 °C and 25 °C using two criteria (primary root protrusion and seedlings at the 2 mm root stage). It was concluded that the primary root protrusion test at 15 °C can evaluate super sweet corn seed vigor by counting the number of seedlings at the 2 mm root stage.

scholarly journals Effects of exogenous ascorbic acid on seed germination and seedling salt-tolerance of alfalfa

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250926
Author(s):  
Zhao Chen ◽  
Xin-long Cao ◽  
Jun-peng Niu
Keyword(s):  
Ascorbic Acid ◽  
Salt Tolerance ◽  
Seed Germination ◽  
Stem Elongation ◽  
Principal Component ◽  
Dry Weight ◽  
Nacl Stress ◽  
Legume Crop ◽  
Seedling Length ◽  
Main Components

Alfalfa (Medicago sativa L.) is an important legume crop for forage, agriculture, and environment in the world. Ascorbic acid (AsA) plays positive roles in plants. However, its effects on germination and salt-tolerance of alfalfa are unknown. The effects of AsA applications on seed germination and seedling salt-tolerance of alfalfa were investigated. The results revealed that 0.1 and 1 mmol L-1 of exogenous AsA increased germination, amylase, and protease, as well as seedling length, fresh weight (FW), dry weight (DW), and endogenous AsA both in the shoots and roots, except that 1 mmol L-1 AsA reduced the activities of α-amylase, β-amylase and protease on day 3. However, 10 and 100 mmol L-1 AsA inhibited these parameters and even caused serious rot. It indicates that 0.1 mmol L-1 AsA has the optimal effects, whereas 100 mmol L-1 AsA has the worst impacts. Another part of the results showed that 0.1 mmol L-1 AsA not only enhanced stem elongation, FW and DW, but also increased chlorophyll and carotenoids both under non-stress and 150 mmol L-1 NaCl stress. Furthermore, 0.1 mmol L-1 AsA mitigated the damages of membrane permeability, malondialdehyde, and excessive reactive oxygen species (ROS) and ions both in the shoots and roots under 150 mmol L-1 NaCl stress. Hence, 0.1 mmol L-1 AsA improves growth and induces salt-tolerance by inhibiting excessive ROS, down-regulating the ion toxicity and up-regulating the antioxidant system. The principal component analysis included two main components both in the shoots and roots, and it explained the results well. In summary, the optimum concentration of 0.1 mmol L-1 AsA can be implemented to improve the seed germination and seedling growth of alfalfa under salt stress.

scholarly journals BIOMETRIC METHOD OF AGE ESTIMATION: DEVELOPMENT AND EFFICIENCY, IN CASES OF PATHOLOGIES OF TEETH HARD TISSUES

2017 ◽  
Author(s):  
M. Yu. Honcharuk-Khomyn ◽  
Kh. V. Pohoretska ◽  
L. O. Patskan
Keyword(s):  
Root Length ◽  
Age Estimation ◽  
Morphological Changes ◽  
Principal Component Regression ◽  
Principal Component ◽  
Structural Defects ◽  
Estimation Technique ◽  
Radiographic Technique ◽  
Mathematical Analyses ◽  
Different Levels

Background. The physiological changes of tooth are the criteria used in evaluation of regressive formula by Kvaal et al. age estimation technique. But in cases of abnormal occlusion, abnormal chewing habits, bruxism, abrasive factors or structural defects of teeth the intensity of tooth aging accelerates.Objective. The aim of the research was to define the options of age estimation according to dental state of individuals with pathological attrition.Methods. 108 panoramic x-ray photos of patients with pathological attrition of teeth were chosen by a randomized selection (49 males and 59 females). All photos were made by means of Planmeca PROMAX orthopantomograph. Nine measurements were made for each tooth: the tooth length, pulp length, root length, root width and pulp width at three different levels: cement-enamel junction (level A, beginning of root), one-quarter of root length from a cement-enamel junction (level B), and mid-root (level C). Due to these measurements, a number of ratios were calculated in accordance with Kvaal et al. method.Results. The errors that reached 27±8.4 years were found when evaluating the dental age using primary coefficients of equations suggested by the authors of the method used. By means of mathematical analyses, principal component regression method as well, the correlation coefficient of Pearson and method of combining linear regression due to the tooth changes in cases of pathological attrition (lowering level of occlusal surface, dystrophy of pulp structures and deposition of tertiary reparative dentine) by regression analysis, the modified formulas for age estimation using radiographic technique were found. Modified coefficients decreased the error to 13±0.8 years, which was relative to the real age upto nearly 42-48% compared to the primary coefficients of equations for pathological attrition.Conclusions. Age estimation technique can be improved taking into account morphological changes in pathological attrition and the calculated coefficients make it possible to expand the circle of person’s age which needs to be found.

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