Exploring the best model for sorting Blood orange using ANFIS method

OranOrange has abundant nutritional properties and is consumed worldwide.  Sorting oranges of different masses based on their physical traits could help reduce packaging and transportation cost.  The ‘Blood’ cultivar of Iranian oranges from Kermanshah province of Iran (7.03 °E 4.22 °N) was used in this study.  100 samples were randomly selected.  During the two-day experiment, all measurements were carried out inside the laboratory at mean temperature of 24°C.  In this study, some physical properties of ‘Blood’ orange were measured, such as length, width, thickness, volume, mass, mean value of geometric diameter, sphericity and projected area.  ANFIS and linear regression models were employed to predict the mass based on sphericity and mean of projected area inputs.  In ANFIS model, samples were divided into two sets, with 70% for training set and 30% for testing set.  The coefficient of determination (R2) for ANFIS and linear regression models were 0.983 and 0.927, respectively.  It is shown that the mass can be estimated based on ANFIS model better than linear regression model.   Keywords: linear regression, orange, packaging, physical properties, sorting

Noise evaluation of MF285 tractor while pulling a trailer in an asphalt road

Tractors have been used for transportation on roads by many farmers in addition to use in the field operations. MF285 tractor is the popular kind of tractor in Iran (about 30% of all tractors) and almost this tractor has been used without cabin.  Despite the problems caused by noise from the tractors and all its adverse effects on users and observers, no comprehensive research has been done on them.  The result of this research indicate that the noise level of MF285 tractor, in 2250 r/min engine speed, will be 90 dB(A) which in comparison with the standard value, 85 dB(A), is dangerous for operator’s ears.  The test site was prepared according to the international standards.  The noise emitted by tractor in three gears (2, 3 and 4) and three speeds (1,500, 1,950 and 2,250 r/min) were measured and then analyzed statistically.  Analysis of variance and Duncan’s mean comparison test showed that the Sound Pressure Level (SPL) at the position of the driver in comparison to the observer position was statistically significant (

Comparative analysis of exhaust gases from MF285 and U650 tractors under field conditions

Agricultural machinery is an important source of emission of air pollutant in rural locations.  This work deals with the effects of types of tractors and operation conditions on engine emission.  The values of some exhaust gases (HC, CO, CO2, O2 and NO) from two common tractors (MF285 and U650) at three situations (use of ditcher, plowing and cultivator) were evaluated in the West of IRAN (Kermanshah).  In addition, engine oil temperature at operation conditions was measured.  Also results showed the values of exhaust HC and O2 of MF285 are lower than U650, while the other exhausts gases (CO, CO2, and NO) of MF285 are higher than U650.  Value of NO emission increased as engine oil temperature increased.  All of exhaust gases except CO have a significant relationship with type of tractors, while all of measured gases have a significant relationship with installed instruments at 1%.   Keywords: environmental pollution, exhaust gases, tracto

Measurement and Analysis of Vibration of Operator in Universal 650, Massey Ferguson 285 & MF 299 Tractors

Abstract Since healthy human is basis of permanent development in any society, and safety & health subject have special importance, then examination and investigation is necessary about of risks working for the purpose of recognition danger and guiding to eliminate them. In this study, object is to compare effect of change engine rotation and ground type on operator of tractors and implements that utilized. In other words, the goal is to measurement and analysis of transmitted vibration on different parts of human body. In this investigation universal tractor and ferguson285 &299 tractors with moldboard plough and disk are used. Hand-Arm vibration's operator in 1300, 1500 and 1700 rpm and in ploughing field and unploughing field with hand-arm vibration meter are measured. After statistical analysis, appeared that effective vibration difference on hand and arm's operator in examined tractors is significant and engine rotation is significant too

Noise evaluation of MF285 and U650 tractors by using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) method

In this research ANFIS method has been used to predict sound pressure levels of MF285 and U650 tractors for following machines: moldboard plow, chisel plow, cultivator, rotary tiller, boom-type sprayer, disk harrow and ditcher. Combination of fuzzy logic with architectural design of neural network leads to creation of neuro-fuzzy systems, which benefit from feed forward calculation of output and back-propagation learning capability of neural networks, while keeping interpret-ability of a fuzzy system. An adaptive neuro-fuzzy inference system architecture based on the Takagi-Sugeno model created to modeling of sound pressure level of MF285 and U650 tractors during agricultural operations. The testing performance of the proposed ANFIS model revealed a good predictive capacity to yield acceptable error measures with, R2= 0.917 and also RMSE= 1.06, SSE= 76.11 and MAE= 0.7495. The study recommends that the ANFIS technique can be successfully used in estimation of sound pressure level of MF285 and U650 tractors

Investigation the effect of outlet air flow and chamber temperature on some bio-char properties of wheat straw in a fixed-bed oxidative pyrolysis

Soil plays an important role in the sustainability of ecosystems. In recent years, the increasing growth in the degradation of soil resources has drawn attention to management strategies for maintaining the soil quality. Researchers have recently studied the impact of using biochar on physical and chemical properties of soil. It has been found that adding biochar improves the soil quality. Some factors such as pyrolysis chamber conditions, pyrolysis peak temperature and air flow rate affect the physical and chemical properties of biochar including the density, pH, ash content, and so on. In this study, the effect of changes in the air flow rate and chamber temperature in the fixed-bed oxidative pyrolysis on the biochar yield, ash content, density and pH were investigated. For this purpose, a fixed-bed biochar production apparatus with varying chamber temperature and flow rate of outlet air was designed and manufactured. The experiments were performed at four air flow rates of 20, 25, 30 and 35 L min-1 and four temperatures of 350, 400, 450 and 500 °C for wheat straw. The results showed that increasing the temperature and flow rate of the outlet air from the chamber increased the ash content and pH. However, increasing these parameters decreased the biochar bulk density and yield

Rapid Detection of Urea Fertilizer Effects on VOC Emissions from Cucumber Fruits Using a MOS E-Nose Sensor Array

The widespread use of nitrogen chemical fertilizers in modern agricultural practices has raised concerns over hazardous accumulations of nitrogen-based compounds in crop foods and in agricultural soils due to nitrogen overfertilization. Many vegetables accumulate and retain large amounts of nitrites and nitrates due to repeated nitrogen applications or excess use of nitrogen fertilizers. Consequently, the consumption of high-nitrate crop foods may cause health risks to humans. The effects of varying urea–nitrogen fertilizer application rates on VOC emissions from cucumber fruits were investigated using an experimental MOS electronic-nose (e-nose) device based on differences in sensor-array responses to volatile emissions from fruits, recorded following different urea fertilizer treatments. Urea fertilizer was applied to cucumber plants at treatment rates equivalent to 0, 100, 200, 300, and 400 kg/ha. Cucumber fruits were then harvested twice, 4 and 5 months after seed planting, and evaluated for VOC emissions using an e-nose technology to assess differences in smellprint signatures associated with different urea application rates. The electrical signals from the e-nose sensor array data outputs were subjected to four aroma classification methods, including: linear and quadratic discriminant analysis (LDA-QDA), support vector machines (SVM), and artificial neural networks (ANN). The results suggest that combining the MOS e-nose technology with QDA is a promising method for rapidly monitoring urea fertilizer application rates applied to cucumber plants based on changes in VOC emissions from cucumber fruits. This new monitoring tool could be useful in adjusting future urea fertilizer application rates to help prevent nitrogen overfertilization

Rapid Detection of Urea Fertilizer Effects on VOC Emissions from Cucumber Fruits Using a MOS E-Nose Sensor Array

The widespread use of nitrogen chemical fertilizers in modern agricultural practices has raised concerns over hazardous accumulations of nitrogen-based compounds in crop foods and in agricultural soils due to nitrogen overfertilization. Many vegetables accumulate and retain large amounts of nitrites and nitrates due to repeated nitrogen applications or excess use of nitrogen fertilizers. Consequently, the consumption of high-nitrate crop foods may cause health risks to humans. The effects of varying urea–nitrogen fertilizer application rates on VOC emissions from cucumber fruits were investigated using an experimental MOS electronic-nose (e-nose) device based on differences in sensor-array responses to volatile emissions from fruits, recorded following different urea fertilizer treatments. Urea fertilizer was applied to cucumber plants at treatment rates equivalent to 0, 100, 200, 300, and 400 kg/ha. Cucumber fruits were then harvested twice, 4 and 5 months after seed planting, and evaluated for VOC emissions using an e-nose technology to assess differences in smellprint signatures associated with different urea application rates. The electrical signals from the e-nose sensor array data outputs were subjected to four aroma classification methods, including: linear and quadratic discriminant analysis (LDA-QDA), support vector machines (SVM), and artificial neural networks (ANN). The results suggest that combining the MOS e-nose technology with QDA is a promising method for rapidly monitoring urea fertilizer application rates applied to cucumber plants based on changes in VOC emissions from cucumber fruits. This new monitoring tool could be useful in adjusting future urea fertilizer application rates to help prevent nitrogen overfertilization

Prediction of Residual NPK Levels in Crop Fruits by Electronic-Nose VOC Analysis following Application of Multiple Fertilizer Rates

The excessive application of nitrogen in cucumber cultivation may lead to nitrate accumulation in fruits with potential toxicity to humans. Harvested fruits of agricultural crops should be evaluated for residual nitrogen, phosphorus, and potassium (NPK) nutrient levels. This is necessary to avoid nutrient toxicity from the consumption of fresh produce with excessive nutrient levels. Electronic noses are instruments well-suited for the nondestructive detection of fruit and vegetable quality based on volatile organic compound (VOC) emissions. This proof-of-concept study was designed to test the efficacy of using an electronic nose with statistical regression models to indirectly predict excessive fertilizer application based on VOC emissions from cucumber fruits grown under controlled greenhouse conditions to simulate field conditions but eliminate most environmental variables affecting plant volatile emissions. To identify excess nitrogen in cucumber plants, five different levels of urea fertilizer application rates were tested on cucumbers (control without fertilizer, 100, 200, 300, and 400 kg/ha). Chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models to predict nitrogen (N), phosphorus (P), and potassium (K) levels in cucumber fruits following application of different fertilizer rates to greenhouse soils. The correlation coefficients for the MLR model (based on the optimal parameters of PCR and PLSR) were 0.905 and 0.905 for the calibration sets and 0.900 and 0.900 for the validation sets, respectively. The nitrogen prediction model for fruit nitrates was more accurate than other nutrient models. The proposed method could potentially be used to indirectly detect excessive use of fertilizers in cucumber field crops