Journal of Researches in Mechanics of Agricultural Machinery

Abstract Transition of mechanical energy from vibrating source to human body has different effects, such as reduce efficiency, skeletal lesions, disorder in the comfort, physiological and psychological problems. The lack of suspension systems on agricultural machines caused, operators of agricultural machinery exposed to a wide range of vibrations indirectly. Use of suitable seat foam is one way to reduce the vibrations.
In this study, the vibration transmission of Polyurethane foam or sponge and effect of various factors on reducing vibrations that occupants will be investigated. So, types of foam and sponge with a thickness of 6, 8, 10 and 12 cm, and densities of 12.5, 20, 30 and 50 kg/m3 were exposed. Also the passenger mass and stimulation acceleration in three different levels was applied to the samples.
Acceleration of data records and analysis was performed with SPSS and MATLAB software. The results showed that vibration transmissibility of the cold foam was less than the hot foam (about 10%). According to the Anthropometrics data and the range of tractor operators mass that in Iran (72-90 kg), cold foam with a density of 50 kg/m3 (transferability of 26%), the thickness of 8-10 cm in acceleration of 3-4 m/s2 (transferability of 17%), due to the principles of ergonomics, is the most appropriate choice for machine with high vibration seat.

Abstract These years, using tractors and self-propelled agricultural machines has been caused to increase of human death numbers due to accident with them. So, having an alarm system on agricultural vehicles for humans and obstacles detection can be beneficial for reducing of accidents. This study aimed to develop a vision system for human detection to use in agricultural tractors. A camera was installed in front of the tractor, and an algorithm was designed in MATLAB for image processing. After trimming the input image, the human was identified using watershed and gradient magnitude techniques, and subtracting human from the background. According to results of the algorithm evaluation in various image acquisitions, the proposed algorithm had high accuracy at distances of ? to ?? meters, in comparison with two common human detection methods (Histogram of oriented gradient and Cascade). Average accuracy of the proposed algorithm in these distances was ?? to ?? percent. The results of analysis of variance for accuracy of human detection showed that the effect of identification method and distance on the accuracy of human detection were significant at the ?% level. Also the interaction effects between different parameters were significant.

Abstract The main jobbery way in olive oil adulteration is mixing with other vegetable oils such as maize, canola, sunflower and soy oil. Therefore, the aim of this study was to evaluate a portable system to detect adulteration in olive oil using a combination of machine vision and capacitive method. To identify adulteration in olive oil by using frequency and color features, Principal Component Analysis (PCA) and Linear discriminant analysis (LDA) were thus applied. The best neural network model with 36-6-1 structure had R2 of 0.944 and mean square error of 0.006, for prediction of adulterated olive oil with sunflower oil. Also, the best model of neural network for olive-canola mixture had the structure of 36-10-1, R2 and mean square error of 0.946 and 0.003, respectively. Finally, a combination of frequency and color properties was used to develop the models. The R2 and mean square error of mixed samples of olive-sunflower were obtained as 0.962 and 0.008 for 38-2-1 network. For mixed samples olive-canola, the R2 and mean square error were obtained 0.961 and 0.0013 for the structure of 38-16-1 neural network.

Abstract Due to the possibility of mixing hard, semi soft and soft almonds with different market value after harvesting, using an effective separation system for supplying uniform products to market is essential. In order to classify almond varieties, an intelligent impact-acoustic system was used. The system operation was done by dropping almond nuts onto a steel impact plate through a pipe. Then features such as amplitude, phase and power spectral density (PSD) of almond nuts were extracted from analysis of sound signal captured by a microphone in both time and frequency domains by means of Fast Fourier Transform. Principal component analysis method was used to reduction of features. Two types of artificial intelligence techniques included Artificial Neural Networks (ANN's) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to classify of almond nuts and their performance was compared. The neural network used, the multi-layer perceptron with back propagation algorithm and LM learning function. In ANFIS system, due to the limitation of inputs number, the three principal components of PSD feature that had higher priority were selected as inputs and hybrid optimization techniques were used for classification of almond classes as outputs. In comparing artificial intelligence techniques, ANN with about 96.2% accuracy had more performance to classify almond nuts than the ANFIS with 81% accuracy.

Abstract Recently, optical GreenSeeker sensor, because of its accuracy and user friendly, has attracted the attention of many farmers to use it for investigation of their farms. The main objective of this study was to measure the total leaf area of plants using this optical sensor and investigate the possibility of using this sensor instead of conventional methods. The images of all plots were recorded using camera and the G-R (Green-Red) was determined as a best function to distinguish the soil from leaf. Results of relationship between total leaf area and NDVI index showed a good correlation (R2=0.84) between these indexes. Results of comparing Greenseeker and chlorophyll meter SPAD-502 indicated a promising accuracy of the Greenseeker sensor (R2=0.82) in measuring the plant chlorophyll. The root mean square error resulted from the effect of organic matter on sensor output was found to be 0.05. It must be noted that every products has a specific reflection in the red and infrared bandwidth, so the model obtained in this study just can be used for corn. For the other crops it is necessary to develop specific models. Furthermore, the model developed here was for a specific growth stage of corn which needs to be generalized for the entire growing stages of corn.

Abstract One of the major concerns in farmlands is soil compaction caused by trafficking of agricultural machinery. Machinery trafficking management on farmland in terms of how and why is depended on the parameters of each section, soil and crop operations. This study is aimed at investigating the effects of wheel forward velocity, load and multi-pass on clay loam soil compaction. Experimental tests were conducted in the soil bin facility equipped with single wheel tester. Input parameters were velocity at three levels (i.e. 0.5, 1 and 1.5 m/s) and wheel load at three levels (i.e. 2, 3, 4 kN). Rut depths and cone index as research target variables were measured in different combinations of treatment and repetition of wheel passes in each direction. The effect of wheel load on the increase of rut depth was only significant up to sixth levels. Moreover, wheel load and number of passes over the same path had a significant effect on the increase of cone index. In contrary, the effect of forward velocity on this parameter is ignorable based on the statistical analysis performed. Furthermore, the effect of wheel pass number on rut depth and cone index are extracted in regression models.

Abstract This study aimed to determine human and machine causes including musculoskeletal disorders for 399MF tractor users as one of the most common one in Iran and hence in the region of study Kohgiluyeh and Boyer (city of Yasouj, Dena, Dehdasht and Gachsaran). The data was collected by using musculoskeletal and aided Nordic questionnaire and analyzed by SPSS 21 software as well as independent T-test and (Chi-Square) X 2. The mean age of the study population, noted to be 36.33, mean weight of 85.35 kg and mean height of 172.82 cm. it was found that 91.9% of studied users were suffered at least once in one part of the body during 12 months. The most frequent injuries and disorders proved to be neck (83.54 %) and back (75.33%) respectively and the lowest was the feet and ankles (17.36 %). The results obtained from REBA technique, proved that in 20 investigated work condition, 13 was in a high dangerous status and 7 was in mid dangerous status which need special attention. Main ergonomic problems were the force exerted on the knee, bending and torsion force on the waist and neck. This study indicated that the prevalence of work-related musculoskeletal disorders of MF399 tractor drivers need to be urgently modified.