Nurettin Senyer

The Estimation of Adult and Nymph Stages of Aphis fabae (Hemiptera: Aphididae) Using Artificial Neural Network

ABSTRACT In this research, the estimation of adult and nymph stages and adult of Aphis fabae was investigated using artificial neural network. Determining A. fabae nymph stages is difficult. Morphometric study of different parts of an insects body is needed to obtain an index to distinguish between different immature stages. The study was aimed to develop a model of A. fabae nymph stages and adult using length of hind tibia, antenna and body length. It was found that the constructed artificial neural network (ANN) exhibited high performance for predicting A. fabae nymph stages. Correlation was 99% and the estimation of the best ANN model was determined to be 0.016289 at epoch 18. Software computing techniques are very useful tools for precision agriculture and also determining which method gives the most accurate result.

DETERMINA TION OF REFLECTANCE VALUES OF HYPERICUM'S LEAVES UNDER STRESS CONDITIONS USING ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM

The effects of water stress and salt levels on hypericums leaves were examined on greenhouse-grown plants of Hypericum perforatum L. by spectral reflectance. Salt levels and irrigation levels were applied 0, 1, 2.5 and 4 deci Siemens per meter (dS/m), 80%, 100% and 120% respectively. Adaptive Network based Fuzzy Inference System (ANFIS) was performed to estimate the effects of water stress and salt levels on spectral reflectance. As a result of ANFIS, it was found that there was close relationship between actual and predicted reflectance values in Hypericum perforatum L. leaves. Performance of ANFIS was examined under different numbers of epoch and rules. On the other hand, RMSE, correlation and analysis time values were found as outputs. Correlation was 99%. The estimation of optimal ANFIS model was determined in 3*3*3 number of rules with 400 epochs.

Using Artificial Neural Network and Multiple Linear Regression for Predicting the Chlorophyll Concentration Index of Saint John’s Wort Leaves

ABSTRACT This research investigates and compares artificial neural network and multiple linear regression for predicting the chlorophyll concentration index of Saint John’s wort leaves (Hypericum perforatum L.). Plants were fertilized with 0, 30, 60, 90, and 120 kg ha−1 nitrogen [34% nitrogen ammonium nitrate (NH4NO3)]. Chlorophyll concentration index of each leaf was measured using SPAD meter. Afterwards, rgb (red, green, and blue color) values of all leaf images were determined by image processing. Values obtained were modeled using both multiple regression analysis and artificial neural networks. Using multiple regression analysis R2 values were between 0.61 and 0.97. Coefficient of determination values (R2) using artificial neutral network values were found to be 0.99. Artificial neutral network modeling successfully described the relationship between actual chlorophyll concentration index values and predicted chlorophyll concentration index values.

Comparision of some models for estimation of reflectance of hypericum leaves under stress conditions

Lack of water resources and high water salinity levels are among the most important growth-restricting factors for plants species of the world. This research investigates the effect of irrigation levels and salinity on reflectance of Saint John’s wort leaves (Hypericum perforatum L.) under stress conditions (water and salt stress) by multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Empirical and heuristics modeling methods were employed in this study to relate stress conditions to leaf reflectance. It was found that the constructed ANN model exhibited a high performance than multiple regression and ANFIS in estimating leaf reflectance accurately.

Automatic antibiogram inhibition zone diameter determination through circular hough transform

The analysis of antibiotics towards a microorganism responsible of an illness is evaluated using the antibiogtam test. In the diffusion method which is the most popular implementation of this test, several paper disks, each one impregnated with a different antibiotic, are spread all over a agar plate which is diffused standard cultivation prepared from test microorganism. After some time, the reaction produced by the antibiotics against the microorganism appear as circular areas of different texture that have grown around the antibiotic disks. Currently, the evaluation and measurement of these areas, called Inhibition Zones, is carried out by human visual inspection via caliper in the laboratories. In this study, a new segmentation and analysis strategy to automatically measure the inhibition zone and disk diameters in antibiograms is presented. It is based on the application of circular Hough transform surrounded appropriate pre- and post-processing stages.

Estimation of Chlorophyll Concentration Index at Leaves using Artificial Neural Networks

In this study, the effectiveness of an SPAD-502 portable chlorophyll (Chl) meter was evaluated for estimating the Chl contents in leaves of some medicinal and aromatic plants. To predict the individual chlorophyll concentration indexes of St. John’s wort (Hypericum perforatum L.), mint (Mentha angustifolia L.), melissa (Melissa officinalis L.), thyme (Thymus sp.), and echinacea (Echinacea purpurea L.), models were developed using SPAD value. Multi-layer perceptron (MLP), adaptive neuro fuzzy inference system (ANFIS), and general regression neural network (GRNN) were used for determining the chlorophyll concentration indexes.

A novel approach to detect QRS based on partition and intensity on ECG signals

This study presents a novel approach to define the formal information of heartbeat and detect the R-wave case in the workspace of Electrocardiogram (ECG). Analyze of QRS wave location gives opportunity to define the heart abnormality A few methods were developed to detection of rhythm disorder by using classical classification methods. The method proposed in this study is a recursive algorithm based on partition and intensity. In this study, MIT-BIH rhythm disorder database was used. The position of R waves detected by using this new method has high reliability. It is able to evaluate heartbeat with high accuracy thanks to proposed method. Detection of QRS wave location was achieved with 95% accuracy rates.

Bird's-eye view images taken plant material and counting

In this study, the recognition of agri-food plants out of the images obtained by the UAV and are intended to implement the counting process. Images obtained with the UAV from plants separation from the background; K-Means (K-Means) with the help of visual elements in the classifier was classified as soil and plants. A better image segmentation and noise in the resulting plants were made to eliminate the morphological filtering. The plants on the noise-free image separation nested data for individual numbers of watershed algorithm was applied. To represent the resulting plant was subjected to the binary image acquisition and counting process. Plant identification methods applied and the counting process accuracy and 87.7% sensitivity, 86.6% were found to implement.

Estimate angle information of hand open-close from surface electromyogram (sEMG)

In this paper, an estimation of angle of hand opening-closing movements by using the Artificial Neural Network (ANN) from surface electromyography (sEMG) signal is presented. The first step of this method is to record sEMG signal from the subjects right forearm and to acquired video frames of hand at the same time. The second step is to synchronize the beginning and the end of recorded video frame and obtain sEMG signals. The third step is to extract some most commonly used feature vectors for sEMG in the literature. Finally, feature vectors sets are fed to the ANN to estimate angle of hand movements. The obtained success rate of the ANN is given as 94.06% in the train set and 93.41% in the test set.

Feature extraction of wavelet transform for sEMG pattern classification

In this study, we have investigated usefulness of extraction of the surface electromiyogram (sEMG) features from multi-level wavelet decomposition of the yEMG signal. The first step of this method is to analyze sEMG signal detected from the subjects right upper forearm and extract features using the mean absolute value (MAV), MAV of wavelet approximation and details coefficients, MAV of wavelet approximation and details of sEMG which is calculated Inverse Wavelet Transform. The second step is to import the feature values into an ANN to identify the speed of hand open-çlose (SHOC). Finally, based on the results of experiments, feature vectors obtained by wavelet transform is effective in prediction of SHOC.