Prediction of Physical Parameters of Pumpkin Seeds Using Neural Network

The design of the machines and equipment used in harvest and post-harvest processing should be compatible with the physical, mechanical and rheological characteristics of the fruits and vegetables. In machine design for agricultural products, several characteristics of relevant products and seeds should be known ahead. Designers can either measure all these design parameters one by one, or they may use intelligent systems to estimate such parameters. Neural networks (NNs) are new computational tools that provide a quick and accurate means of physical properties prediction of agricultural materials, and have been shown to perform well in comparison with traditional methods. In this research, some physical properties of pumpkin (Cucurbita pepo L.) seeds, including linear dimensions, volume, surface and projected area, geometric mean diameter and sphericity were calculated tridimensional in lab conditions. Then, prediction of these parameters was carried out using NNs. The research was divided into two parts; experimental investigation and simulation analysis with NNs. Back Propagation Neural Network (BPNN) and Radial Basis Neural Network (RBNN) structures were employed to estimate physical parameters of the pumpkin seeds. The Root Mean Squared Error (RMSE) was 0.6875 for BPNN and 0.0025 for RBNN structures. The RBNN structure was superior in prediction and could be used as an alternative approach to conventional methods

Determination of Size and Shape in the ‘Moro’ Blood Orange and ‘Valencia’ Sweet Orange Cultivar and its Mutants Using Image Processing

Fruit size and shape are important physical characteristics in designing relevant equipment, sorting, sizing and packaging systems. Therefore, the properties of size and shape of the sweet orange cultivar, ‘Valencia’, and its three mutants, ‘A70’, ‘A77’, and ‘A88’ were determined by image processing. The blood orange cultivar, ‘Moro’, was also included in this analysis. The volume of each cultivar and mutant was measured by the liquid displacement method. Linear equations with high R2 values were developed in order to estimate the surface area and geometric mean diameter, which were dependent upon the mass and volume of the orange samples. The results of this study showed that the ‘A70’ mutant differed from the other mutants and the ‘Valencia’ cultivar in regard to most physical properties. The ‘A70’ and ‘A88’ mutants and the ‘Valencia’ cultivar had the highest sphericity values, which varied from 96.41% to 97.18%. The lowest shape factor was found in the ‘Valencia’ cultivar, with a mean of 0.73. The elongation of the ‘A88’ mutant and ‘Valencia’ (1.07 each) was smaller than that of the other cultivars. The highest coefficient of variance was observed within the ‘Valencia’ and ‘Moro’ cultivars in most physical properties, suggesting that the ‘Valencia’ mutants produce more homogeneous fruits than the ‘Valencia’ cultivar itself

The examination of the required multicultural education characteristics in curriculum design

5th International Conference on New Horizons in Education (INTE) -- JUN 25-27, 2014 -- Paris, FRANCEWOS: 000383740203110This phenomenological study focused on what multicultural characteristics can be reflected to the elements of the curriculum objectives, content, learning situations and evaluation. Multicultural literature was examined via content analysis method. The findings were reported according to the themes based on the curriculum's elements. Some results of the research revealed that a curriculum design has multicultural characteristics if the objectives have the learner characteristics such as comprehending human rights and appreciation of different views, the content consists of some subjects such as human rights and citizenship, the learning situations offer different groups bias-free implementations and the evaluation process focused on thinking skills such as reflective thinking. (C) 2015 The authors Published by Elsevier Ltd

Determination of Color Properties of Weed Using Image Processing

Image processing technique has come up with advancement of computer technology and has recently been widely used. This technique consists of two parts. In the first part, the input such as an image or a video obtained by a camera or a scanner is digitized. In the second part, some characteristics or parameters related to the image is gathered by using algorithms. By the improvements that took place in the area of image processing, the technique is now very popular in agricultural field and is an alternative method in identification of weeds, determination of their intensity and color properties. In this study, the color properties of three weeds; Chenopodium album L., Sonchus hierrensis, Lectuca serriola obtained by a digital camera and a Chroma meter were compared

Determination of some physicomechanical and biochemical parameters of hazelnut (Corylus avellana L.) cultivars

Hazelnut is one of the most popular nuts consumed by people; it has different cultivars in Turkey. The aim of the current study was to characterize some physicomechanical characteristics, shape features, color, and biochemical properties of 6 standard and 3 local hazelnut cultivars grown in Turkey. The shape and size properties of the samples were determined using image processing techniques as an alternative to conventional measurement methods. Additionally, principal component analysis (PCA) was used to classify the hazelnut samples in terms of the biochemical parameters of the hazelnut cultivars. According to the findings, the highest crude oil (63.25%) and lowest protein contents (13.63%) were observed in the Kalmkara cultivar. Oleic and linoleic acids were the major fatty acids for all hazelnut samples. While local Devedisi and standard cakildak cultivars had the highest oleic acid levels, the highest linoleic acid level was observed for the Dag findigt cultivar. The cultivars of Fosa had the highest Zn and Mn, while the highest Cu was found in the Tombul cultivar. The greatest surface and projected areas were calculated for the Kara findik and Dag findigi samples, while the greatest hardness value was measured for the Devedisi cultivar. PCA revealed some positive and negative correlations between the physicomechanical and biochemical parameters. The present analyses revealed significant correlations between hardness and internal shell b* values and between Cu content and internal L*. Such correlations should be taken into consideration in food processing applications and machine design for these hazelnut cultivars

Prediction of Physical Parameters of Pumpkin Seeds Using Neural Network

The design of the machines and equipment used in harvest and post-harvest processing should be compatible with the physical, mechanical and rheological characteristics of the fruits and vegetables. In machine design for agricultural products, several characteristics of relevant products and seeds should be known ahead. Designers can either measure all these design parameters one by one, or they may use intelligent systems to estimate such parameters. Neural networks (NNs) are new computational tools that provide a quick and accurate means of physical properties prediction of agricultural materials, and have been shown to perform well in comparison with traditional methods. In this research, some physical properties of pumpkin (Cucurbita pepo L.) seeds, including linear dimensions, volume, surface and projected area, geometric mean diameter and sphericity were calculated tridimensional in lab conditions. Then, prediction of these parameters was carried out using NNs. The research was divided into two parts; experimental investigation and simulation analysis with NNs. Back Propagation Neural Network (BPNN) and Radial Basis Neural Network (RBNN) structures were employed to estimate physical parameters of the pumpkin seeds. The Root Mean Squared Error (RMSE) was 0.6875 for BPNN and 0.0025 for RBNN structures. The RBNN structure was superior in prediction and could be used as an alternative approach to conventional methods

Morphological Characteristics of Grapevine Cultivars and Closed Contour Analysis with Elliptic Fourier Descriptors

Morphology is the most visible and distinct character of plant organs and is accepted as one of the most important tools for plant biologists, plant breeders and growers. A number of methods based on plant morphology are applied to discriminate in particular close cultivars. In this study, image processing analysis was used on 20 grape cultivars (“Amasya beyazı“, “Antep karası“, “Bahçeli karası”, “Çavuş“, “Cevşen“, “Crimson“, “Dimrit“, “Erenköy beyazı“, “Hafızali“, “Karaşabi“, “Kırmızı“, “İzabella (Isabella) “, “Morşabi“, “Müşgüle“, “Nuniya“, “Royal“, “Sultani çekirdeksiz (Sultanina)“, “Yalova incisi“, “Yerli beyazv“, “Yuvarlak çekirdeksiz“) to classify them. According to image processing analysis, the longest and the greatest projected area values were observed in “Antep karası“ cultivar. The “Sultani çekirdeksiz“ cultivar had the least geometric mean diameter. The greatest sphericity ratios were observed in “Yerli beyaz“, “Erenköy beyazı“ and “Amasya beyazı“ cultivars. According to principal component analysis, dimensional attributes were identified as the most significant source of variation discriminant grape cultivars from each other. Morphological differences between the cultivars were explained by sphericity and elongation variables. According to elliptic Fourier analysis (EFA) results, grape morphology largely looks like ellipse and sphere. However, there are some cultivars that look similar to a water drop. The cultivars with similar morphology were identified by a pair-wise comparison test conducted with the use of linear discriminant analysis, and they were presented in a scatter plot. According to cluster analysis, present grape cultivars were classified into seven sub-groups, which indicated great diversity

Successful management of pulmonary hemorrhage and aspergillosis in a patient with acute myeloid leukemia (AML-M3)

A 35-year-old man presented with a one month history of gingival bleeding. He was diagnosed with Acute Myeloid Leukemia (AML-M3). During treatment he developed alveolar hemorrhage for which he was treated with a steroid. After the steroid treatment he developed a nodule, a cavitary lesion and atelectasia in the left lung. He was treated with voriconazole. After therapy with voriconazole his lesion significantly decreased. This case illustrates the efficacy and safety of antifungal therapy with voriconazole for aspergillosis complicated by AML

Possible Use of Data Mining for Analysis and Prediction of Apple Physical Properties

Data mining is used as a popular technique in several scientific researches. In agriculture, application of data mining is a relatively new approach. One of the most popular data mining approaches is to find prediction rules from experimental data sets. The present study was conducted in two stages to find out a rule for estimation of width of stalk cavity, depth of stalk cavity, width of eye basin and depth of eye basin of different apple varieties ('Amasya', 'Starking', 'Granny Smith', 'Pink Lady', 'Golden Delicious' and 'Arapkizi') based on physical properties and to propose an equation for calculating these parameters. In the first stage, data processing was performed and in the second stage, Find Laws was used for prediction of apple properties. Current results revealed that data mining technique had a superior performance and could reliably be used in estimation of physical characteristics of agricultural products. Further research is recommended for possible use of datamining in other agricultural application