scholarly journals The Prediction of Chlorophyll Content in African Leaves (Vernonia amygdalina Del.) Using Flatbed Scanner and Optimised Artificial Neural Network

2021 ◽  
Vol 29 (4) ◽  
Author(s):  
Retno Damayanti ◽  
Nurul Rachma ◽  
Dimas Firmanda Al Riza ◽  
Yusuf Hendrawan
Keyword(s):  
Chlorophyll Content ◽  
Selection Process ◽  
Ann Model ◽  
Vernonia Amygdalina ◽  
Flatbed Scanner ◽  
Filter Methods ◽  
Texture Parameters ◽  
Hidden Layer ◽  
The Relationship ◽  
Non Destructive

African leaves (Vernonia amygdalina Del.) is a nutrient-rich plant that has been widely used as a herbal plant. African leaves contain chlorophyll which identify compounds produced by a plant, such as flavonoids and phenols. Chlorophyll testing can be carried out non-destructively by using the SPAD 502 chlorophyll meter. However, it is quite expensive, so that another non-destructive method is developed, namely digital image analysis. Relationships between chlorophyll content and leaf image colour indices in the RGB, HSV, HSL, and Lab* space are examined. The objectives of this study are 1) to analyse the relationship between texture parameters of red, green, blue, grey, hue, saturation(HSL), lightness (HSL), saturation( HSV), value(HSV), L*, a*, and b* against the chlorophyll content in African leaves using a flatbed scanner (HP DeskJet 2130 Series); and 2) built a model to predict chlorophyll content in African leaves using optimised ANN through a feature selection process by using several filter methods. The best ANN topologies are 10-30-40-1 (10 input nodes, 40 nodes in hidden layer 1, 30 nodes in hidden layer 2, and 1 output node) with a trainlm on the learning function, tansig on the hidden layer, and purelin on the output layer. The selected topology produces MSE training of 0.0007 with R training 0.9981 and the lowest validation MSE of 0.012 with R validation of 0.967. With these results, it can be concluded that the ANN model can be potentially used as a model for predicting chlorophyll content in African leaves.

scholarly journals SPAD-502 and atLEAF CHL PLUS values provide good estimation of the chlorophyll content for Hevea brasiliensis Müll. Arg. Leaves

2020 ◽  
Vol 88 (1) ◽  
Author(s):  
Andi Nur CAHYO ◽  
Rudi Hari MURTI ◽  
Eka Tarwaca Susila PUTRA ◽  
Tri Rini NURINGTYAS ◽  
Denis FABRE ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Standard Laboratory ◽  
Laboratory Procedure ◽  
Rubber Plant ◽  
Leaf Chlorophyll ◽  
The Relationship ◽  
Non Destructive ◽  
Destructive Methods

Measurement of chlorophyll content using destructive methods is not efficient due to a large number of samples, cost, and time needed. Estimationof chlorophyll content by nondestructive methods using handheld chlorophyll meter may be considered to improve efficiency. This research aimed to determine the formula to convert SPAD-502 and atLEAF CHL PLUS values (relative indicator of chlorophyll content) to estimated (absolute) rubber leaves chlorophyll content. Twenty leaves of rubber plant were measured using SPAD-502 and atLEAF CHL PLUS at the same time to determine SPAD-502 and atLEAF CHL PLUS values. The measured leaves were then collected to determine the chlorophyll content using a standard laboratory procedure. Regression and correlation analyses (among 3 methods) were conducted using SAS v.9 software. The results showed that between SPAD-502 and atLEAF CHL PLUS values were closely correlated, hence both of the devices can substitute each other to estimate rubber leaf chlorophyll content. In addition, the relationship between atLEAF CHL PLUS and SPAD-502 values with actual chlorophyll content of rubber clone SP 217, PB 260, GT1, and all clones (general) were significant with high coefficient of determination (R2) as well as low Root Mean Square Error (RMSE) and Coefficient of Variation (CV). Therefore, by using formula determined in this study, both atLEAF CHL PLUS and SPAD-502 can be suggested for accurate, fast, and non-destructive estimation of chlorophyll content of rubber plant leaf.

scholarly journals Predicting Rates of Inbreeding in Populations Undergoing Selection

Genetics ◽  
2000 ◽  
Vol 154 (4) ◽  
pp. 1851-1864 ◽  
Author(s):  
John A Woolliams ◽  
Piter Bijma
Keyword(s):  
Overlapping Generations ◽  
Linear Models ◽  
Selection Process ◽  
Correction Term ◽  
Random Mating ◽  
Nonrandom Mating ◽  
Selection Processes ◽  
Genetic Contributions ◽  
The Relationship

AbstractTractable forms of predicting rates of inbreeding (ΔF) in selected populations with general indices, nonrandom mating, and overlapping generations were developed, with the principal results assuming a period of equilibrium in the selection process. An existing theorem concerning the relationship between squared long-term genetic contributions and rates of inbreeding was extended to nonrandom mating and to overlapping generations. ΔF was shown to be ~¼(1 − ω) times the expected sum of squared lifetime contributions, where ω is the deviation from Hardy-Weinberg proportions. This relationship cannot be used for prediction since it is based upon observed quantities. Therefore, the relationship was further developed to express ΔF in terms of expected long-term contributions that are conditional on a set of selective advantages that relate the selection processes in two consecutive generations and are predictable quantities. With random mating, if selected family sizes are assumed to be independent Poisson variables then the expected long-term contribution could be substituted for the observed, providing ¼ (since ω = 0) was increased to ½. Established theory was used to provide a correction term to account for deviations from the Poisson assumptions. The equations were successfully applied, using simple linear models, to the problem of predicting ΔF with sib indices in discrete generations since previously published solutions had proved complex.

scholarly journals System for Visualizing Surface Potential Distribution to Eliminate Electrostatic Charge

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4397
Author(s):  
Kazuya Kikunaga
Keyword(s):  
Surface Potential ◽  
Potential Distribution ◽  
Measurement Accuracy ◽  
High Spatial Resolution ◽  
Scanning Speed ◽  
Electrostatic Charge ◽  
Production Quality ◽  
Array Sensor ◽  
The Relationship ◽  
Non Destructive

A mixture of positive and negative static charges exists in the same plane on an insulator surface, and this can cause production quality problems at manufacturing sites. This study developed a system with a vibration array sensor to rapidly measure the surface potential distribution of an object in a non-contact and non-destructive manner and with a high spatial resolution of 1 mm. The measurement accuracy differed greatly depending on the scanning speed of the array sensor, and an optimum scanning speed of 10 mm/s enabled rapid measurements (within <3 s) of the surface potential distribution of a charged insulator (area of 30 mm × 30 mm) with an accuracy of 15%. The relationship between charge and dust on the surface was clarified to easily visualize the uneven static charges present on it and thereby eliminate static electricity.

scholarly journals Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network

2020 ◽  
Vol 15 ◽  
pp. 155892501990083
Author(s):  
Xintong Li ◽  
Honglian Cong ◽  
Zhe Gao ◽  
Zhijia Dong
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Thermal Resistance ◽  
Resistance Test ◽  
Training Algorithm ◽  
Backpropagation Algorithm ◽  
Optimal Network ◽  
Resistance Model ◽  
Hidden Layer ◽  
The Relationship

In this article, thermal resistance test and water vapor resistance test were experimented to obtain data of heat and humidity performance. Canonical correlation analysis was used on determining influence of basic fabric parameters on heat and humidity performance. Thermal resistance model and water vapor resistance model were established with a three-layered feedforward-type neural network. For the generalization of the network and the difficulty of determining the optimal network structure, trainbr was chosen as training algorithm to find the relationship between input factors and output data. After training and verification, the number of hidden layer neurons in the thermal resistance model was 12, and the error reached 10−3. In the water vapor resistance model, the number of hidden layer neurons was 10, and the error reached 10−3.

ANN-Based Structure Optimization with Fatigue Reliability Constrains

2012 ◽  
Vol 204-208 ◽  
pp. 3128-3131
Author(s):  
Li Rong Sha ◽  
Yue Yang
Keyword(s):  
Structural Optimization ◽  
Objective Function ◽  
Reliability Analysis ◽  
Response Surface ◽  
Minimum Weight ◽  
Structure Optimization ◽  
Fatigue Reliability ◽  
Ann Model ◽  
Surface Method ◽  
The Relationship

The ANN-based optimization for considering fatigue reliability requirements in structural optimization was proposed. The ANN-based response surface method was employed for performing fatigue reliability analysis. The fatigue reliability requirements were considered as constraints while the weight as the objective function, the ANN model was adopted to establish the relationship between the fatigue reliability and geometry dimension of the structure, the optimal results of the structure with a minimum weight was reached.

scholarly journals Phenotyping plants: genes, phenes and machines

2012 ◽  
Vol 39 (11) ◽  
pp. 813 ◽  
Author(s):  
Roland Pieruschka ◽  
Hendrik Poorter
Keyword(s):  
Structural Information ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
Special Issue ◽  
Non Invasive ◽  
The Core ◽  
The Relationship ◽  
Non Destructive ◽  
Major Progress ◽  
Meaningful Data

No matter how fascinating the discoveries in the field of molecular biology are, in the end it is the phenotype that matters. In this paper we pay attention to various aspects of plant phenotyping. The challenges to unravel the relationship between genotype and phenotype are discussed, as well as the case where ‘plants do not have a phenotype’. More emphasis has to be placed on automation to match the increased output in the molecular sciences with analysis of relevant traits under laboratory, greenhouse and field conditions. Currently, non-destructive measurements with cameras are becoming widely used to assess plant structural properties, but a wider range of non-invasive approaches and evaluation tools has to be developed to combine physiologically meaningful data with structural information of plants. Another field requiring major progress is the handling and processing of data. A better e-infrastructure will enable easier establishment of links between phenotypic traits and genetic data. In the final part of this paper we briefly introduce the range of contributions that form the core of a special issue of this journal on plant phenotyping.

scholarly journals An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey)

2007 ◽  
Vol 7 (5) ◽  
pp. 557-570 ◽  
Author(s):  
M. C. Tunusluoglu ◽  
C. Gokceoglu ◽  
H. Sonmez ◽  
H. A. Nefeslioglu
Keyword(s):  
Engineering Geology ◽  
Source Area ◽  
Learning Ability ◽  
Susceptibility Map ◽  
Ann Model ◽  
New Approach ◽  
Failure Tolerance ◽  
Accelerate Convergence ◽  
Artificial Neural ◽  
Hidden Layer

Abstract. Various statistical, mathematical and artificial intelligence techniques have been used in the areas of engineering geology, rock engineering and geomorphology for many years. However, among the techniques, artificial neural networks are relatively new approach used in engineering geology in particular. The attractiveness of ANN for the engineering geological problems comes from the information processing characteristics of the system, such as non-linearity, high parallelism, robustness, fault and failure tolerance, learning, ability to handle imprecise and fuzzy information, and their capability to generalize. For this reason, the purposes of the present study are to perform an application of ANN to a engineering geology problem having a very large database and to introduce a new approach to accelerate convergence. For these purposes, an ANN architecture having 5 neurons in one hidden layer was constructed. During the training stages, total 40 000 training cycles were performed and the minimum RMSE values were obtained at approximately 10 000th cycle. At this cycle, the obtained minimum RMSE value is 0.22 for the second training set, while that of value is calculated as 0.064 again for the second test set. Using the trained ANN model at 10 000th cycle for the second random sampling, the debris source area susceptibility map was produced and adjusted. Finally, a potential debris source susceptibility map for the study area was produced. When considering the field observations and existing inventory map, the produced map has a high prediction capacity and it can be used when assessing debris flow hazard mitigation efforts.

scholarly journals The relationship between SPAD chlorophyll and disease severity index in Ganoderma-infected oil palm seedlings

2019 ◽  
Vol 17 (3) ◽  
pp. 355-358 ◽  
Author(s):  
M.R.M. Rakib ◽  
A.H. Borhan ◽  
A.N. Jawahir
Keyword(s):  
Chlorophyll Content ◽  
Disease Severity ◽  
Oil Palm ◽  
Single Photon ◽  
Linear Trend ◽  
Severity Index ◽  
Quadratic Model ◽  
Wood Block ◽  
Disease Severity Index ◽  
The Relationship

Establishment of disease in oil palm seedlings through artificial inoculation of Ganoderma are widely used for studies of various aspects of plant pathology, including epidemiology, etiology, disease resistance, host-parasite interaction and disease control. The estimation of chlorophyll content in the infected seedlings possibly could provide a good indicator for degree of disease or infection, and changes during pathogenesis. Thus, the objective of this study was to evaluate the relationship between disease severity index (DSI) and chlorophyll content in Ganoderma infected oil palm seedlings. Three-month-old oil palm seedlings were infected with Ganoderma inoculum on rubber wood block (RWB), where 44 isolates of Ganoderma were tested. Disease severity index (DSI) and chlorophyll content using a single-photon avalanche diode (SPAD) meter were recorded at 4 weeks interval for a period of 24 weeks after inoculation (WAI). Pearson's correlation analysis and regression analysis were performed to evaluate the relationship between the variables. It was found that the relationship between DSI and SPAD chlorophyll value was inversely proportional (R = -0.92) in a linear trend (R2 = 0.85). Furthermore, the increasing trend of the DSI across the weeks were fitted in a quadratic model (R2 = 0.99). In contrast, the SPAD chlorophyll value declined in a linear trend (R2 = 0.98). The SPAD chlorophyll value could be considered as a better alternative over the DSI as the SPAD chlorophyll value was strongly related to DSI, as well as able to detect physiological changes in the infected oil palm seedlings at the early stages of pathogenesis. J Bangladesh Agril Univ 17(3): 355–358, 2019

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