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Estimating the surface area of defects of diseased potatoes is a key factor in the automatic grading of this product. In this article, an algorithm has been developed using fuzzy clustering method and image processing functions to estimate the defective areas of potato tubers infected with black scab disease. Fuzzy clustering, which is an unsupervised method, was used to segment color images and extract defective areas of potatoes, and image processing functions have been used to extract the total area of potatoes. In the segmentation method based on fuzzy clustering, the data matrix related to potato images were divided into separate clusters in a fuzzy way, in which the boundaries of the clusters are defined in a fuzzy way instead of being definite and specific. The results showed that this algorithm is very efficient for extracting black scab disease and can be used to extract the amount of diseases that can be used for automatic grading of this product based on the American standards. [ABSTRACT FROM AUTHOR]

Fruits and vegetables are important components of healthy diet. Vegetable freshness is important for both the postharvest industry and consumer appeal. This study focused on freshness detection of parsleys using combined image processing and artificial intelligence techniques. A dataset of color and texture features computed from parsley images. Linear discriminant analysis (LDA) and principal component analysis (PCA) methods were used for feature reduction. Multilayer perceptron (MLP) neural networks, support vector machine (SVM) and decision trees (DTs) classifiers were used for classification. Results showed that MLP with LDA feature selection methods had higher performance and the overall accuracy, root mean square error (RMSE), mean square error (MAE) of MLP classifier (using LDA feature selection) were 97.22%, 0.17, and 0.03, respectively. This approach provided a rapid and nondestructive detection of parsley freshness without using chemical or colorimetric analysis. The results demonstrated that suggested approach could be employed satisfactorily for inspection, classification and automation of vegetables postharvest operations. [ABSTRACT FROM AUTHOR]

The appearance color of bell peppers is significantly related to their quality which affects consumer's acceptance of buy. Also, homogeneity of color is among the most essential export standards of bell peppers. Commercially standard colorimetric devices are very expensive and are often available in scientific research centers. The aim of the present study was to develop and calibrate a simple, cheap, and portable machine vision (MV)-based system to accurately measure the chromatic parameters of bell peppers. For this purpose, a MV system possessing a digital CCD camera, and an artificial lighting system was developed. To calibrate the color of the utilized camera, the standard color cards were used. An appropriate algorithm based on image processing techniques was developed to calculate the chromatic parameters of the crop in the CIELAB color space. The development system was calibrated and compared with a standard colorimetric device using various developed models including linear regression (LR), multivariate linear regression (MLR), and artificial neural networks (ANNs). All the models were employed to diagnose the chromatic properties of bell peppers using the developed MV system. The results revealed that the LR model outperforms MLR and ANN models. The overall accuracy of the proposed MV system was 91.7% in comparison with the standard device. The results showed that the proposed system can be considered a more reliable device compared to traditional commercial devices and could be a suitable alternative in the absence of a specialized color measurement device. [ABSTRACT FROM AUTHOR]

In this paper, we study time-domain and frequency-do-main responses of nonuniform and uniform transmission lines excited by incident electromagnetic waves. Externally excited uniform transmission lines permit closed-form solutions in terms of inverse chain matrix, whereas nonuniform lines cannot be analytically solved, in general. Here, we adopt a method of equivalent cascaded network chain as the method of solving the latter situation. Useful and compact expressions for the load currents induced at terminal loads are derived. To confirm the validity of this method and the forcing terms, theoretical and experimental results of coupling calculations for a few typical (uniform/nonuniform) line geometries, relevant in the EMC field, are presented and discussed. Index Terms - Coupling, EMI, MoM, NEMP.

The goal of this study was to examine the energy flows for poultry breeding in broiler production farms in Alborz province of Iran. Data were obtained randomly from 30 poultry production farms using a face to face questionnaire method. The results indicated that the total input energy was 189805.48 MJ per 1000 birds, while the output energy was 28151.17 MJ/(1000 bird). Net energy was negative, -161654.31 MJ/(1000 bird), implying that energy had been lost. Energy use efficiency was calculated as 0.15, showing the inefficient use of energy in the broiler production farms. The fuel energy (energy content of the fuel) with a share of 58.35% had the highest share of input energy and the feed energy by 29.71% was the next. Greenhouse gas (GHG) emission of the studied farms was 10267.96 kg CO2-eq/(1000 bird). Among different inputs, feed with 48% had the highest share of GHG emissions. Cobb-Douglas frontier production function was adopted to specify the production technology of the farms. Econometric model evaluation showed that the effects of diesel fuel and feed on output energy was significant at 1% level. The marginal physical productivity (MPP) values of energy inputs based on sensitivity analysis were 0.04 and 0.51 for diesel fuel and feed, respectively. The variability results between poultry farms showed that the most variability comes from fuel consumption. [ABSTRACT FROM AUTHOR]

This paper presents a computer program that can be used for teaching tractor performance to undergraduate students of agricultural system management. One of the best ways to apply theory is the usage of simulation software and visualization techniques. Simulation software helps students to understand the performance aspects of tractors in the field from the pure science aspects of the subject. The interface was designed with Visual Basic 6.0 and can be used to predict the performance as well the tractor's specific volumetric fuel consumption for agricultural soils. Other features include provision for changing model parameters, adding new models, saving data using Excel spreadsheets and printing the results. [ABSTRACT FROM AUTHOR]

Application of tractors in farming is undeniable as a power supply. Therefore, performance model for evolving parameters of tractors and implements are essential for farm machinery, operators and manufacturers alike. The objective of this study was to assess the predictive capability of several configurations of ANNs for performance evaluating of tractor in parameters of drawbar power, fuel consumption, rolling resistance and tractive efficiency. A conventional tillage system which included a moldboard plow with three furrows was used for collecting data from MF285 Massey Ferguson tractor. To predict performance parameters, ANN models with back-propagation algorithm were developed using the MATLAB software with different topologies and training algorithms. For drawbar power, the best result was obtained by the ANN with 6-7-1 topology and Bayesian regulation training algorithm with R2 of 0.995 and MSE of 0.00024. The ANN model with 6-7-1 structure and Levenberg-Marquardt training algorithm had the best performance with R2 of 0.969 and MSE of 0.13427 for TFC prediction. The 6-8-1 topology shows the best power for prediction of AFC with R2 and MSE of 0.885 and 0.01348, respectively. Also, the 6-10-1 structure yielded the best performance for prediction of SFC with R2 of 0.935 and MSE of 0.012756. The obtained result showed that the 6-7-1 structured ANN with Levenberg-Marquardt training algorithm represents a good prediction of TE with R2 equal to 0.989 and MSE of 0.001327. The obtained results confirmed that the neural network can be able to learn the relationships between the input variables and performance parameters of tractor, very well. [ABSTRACT FROM AUTHOR]

Due to the ascending importance of energy in the world, prediction and optimization of Fuel Consumption (FC) in agricultural tasks is merit to consideration. In this study a Massey Ferguson (MF285) tractor was implemented with a low cost and precise data acquisition system as a means to record and monitor the affectual parameters on FC such as forward speed and instant fuel flow rate during field operation. Field experiments were carried out in the experimental farm of Agricultural Engineering Department of Tehran University, Karaj province, Iran, which had loamy soil texture. A mouldboard plow was used as tillage toll during the experiments at various tillage depths, engine speeds, forward speeds, tire inflation pressures, moisture contents and cone indexes. Acquired data were used to elicit an accurate model for Temporal, Area-specific and Specific Fuel Consumption (TFC, AFC and SFC). Results showed considerable effect of all measured parameters on TFC, AFC and SFC. For instance the TFC, AFC and SFC decreased by 11%, 13% and 56% respectively when the cone index increased from 105 to 1161 kPa. And also augmenting tillage depth from 10 to 20 cm led to 44% increase of TFC while SFC decreased by 164% oppositely. AFC rate was 1.1 liter per cm of tillage depth. Increasing the engine speed from 1200 to 2000 r/min led to increase of TFC, AFC and SFC by 56%, 71% and 46%, respectively. The forward speed was the most influential parameter on TFC, AFC and SFC while the moisture content and tire inflation pressure effects were minor. Models validation was acceptable and the fuel consumption rate could be predicted with accuracy of about 95%. [ABSTRACT FROM AUTHOR]

To get a proper energy consumption pattern and an increase in energy productivity, determining a relationship between energy inputs and outputs is necessary. In this study, the equivalent energy of inputs and outputs data used in wheat production in Abyek town of Ghazvin province, Iran was collected from farmers over three years. The energy ratio was obtained as 2.11, 2.08 and 2.03 and energy productivity was obtained as 0.15, 0.14 and 0.14 (kg MJ-1) for 2010, 2009 and 2008, respectively. It was found that the contributions of indirect and non-renewable energies on wheat yield were more than the impacts of direct and renewable energies. To determine the effects of energy inputs on wheat yield, the Cobb-Douglas production function was used. Model 1 was composed of individual energy inputs: labor, machinery, electricity, diesel fuel, water for irrigation, fertilizer, chemicals and seed energies In Model 2 energy inputs divided to direct and indirect energies and in Model 3 they divided to renewable and non-renewable energies. The R² values in all three models were more than 0.98 and showed that the models can estimate well. The sensitivity analysis results for Model I showed that the major marginal physical productivities (MPPs) were water for irrigation, human labor and water for irrigation in 2010, 2009 and 2008, respectively. In Model II, the major MPP belongs to for renewable energy in the same years. [ABSTRACT FROM AUTHOR]

Adulteration is a serious problem in food industry. Olive oil is widely adulterated with other cheap edible oils such as sunflower and canola oils. Therefore, developing a low-cost, practical and rapid analytical method for detecting such adulteration in olive oil would be useful and needed. In this research, we aimed to develop a dielectric measurement based system combined with complementary analytical intelligent techniques to recognize authentication of virgin olive oil from adulterated with vegetable oils (canola and sunflower). 192 sinusoidal signals in the range of 20 kHz and 20 MHz were feed into the cylindrical dielectric sensor filled with oil sample. Correlation based feature selection (CFS) was applied to select the most appropriate dielectric features and eliminate irrelevant data. Support vector machines (SVMs), artificial neural networks (ANNs) and decision trees (DTs) were developed to classify virgin olive oil samples from adulterated ones. The obtained results indicated that ANN with topology of 2-5-3 had the best performance with accuracy of 100%. [ABSTRACT FROM AUTHOR]