AbdolMajid Moinfar; Gholamhossein Shahgholi; Yousef Abbaspour-Gilandeh; Tarahom Mesri Gundoshmian
Abstract
The four-wheel drive and rear-wheel drive tractors are commonly used in agricultural operations. In order to investigate the effect of a type of driving system a series of tests were performed usin the three driving systems of foour wheel drive, rear wheel drive and front wheel drive in different axle ...
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The four-wheel drive and rear-wheel drive tractors are commonly used in agricultural operations. In order to investigate the effect of a type of driving system a series of tests were performed usin the three driving systems of foour wheel drive, rear wheel drive and front wheel drive in different axle loads of 0, 150 and 300 kg, tire inflation pressures of 170, 200 and 230 kPa and travel speeds of 1.26, 3.96 and 6.78 km/h. Bulk density was measured as an indicator of soil compaction at different depths of 10, 20, 30 and 40 cm. Also, under the different conditions, the drive wheel slip was measured. To carry out the tests, the four-wheel tractor of Goldoni 240 was used which has the ability to work with mentioned driving systems. The experiments were carried out under controlled conditions in a soil channel with the length of 3 m and a width and depth of 1 and 0.6 m, respectively. Test were conducted in completely randomised block design with three repetations and results were analysied using SPSS 22 software. The results showed that by changing the driving system from 4WD to RWD and FWD, there was a significant increase in soil density, with the lowest density associated with 4WD system and the highest density related to FWD. The reason for increasing the density by changing the driving system can be attributed to different slip levels in each of these systems due to the lower slip percentage of the 4WD system than the other two systems. Increasing axial load increased soil boulk density. Of note that with increasing the axial load, the stress was transferred from the surface soil to the subsoil layers. As the axial load on tire increases, the subsoil density was closer to the surface layer. Increased axial load on tire and decreasing tire pressure reduced wheel slip. Stepwise regression model with determination coefficient of 0.92 and according to calculated standard coefficients showed that axial load, soil depth, type of driving system, tractor speed, and finally tire pressure, have the greatest effect on soil bulk density, respectively.
Zargham Fazel Niari; Amir hossein Afkari-Sayyah; Yousef Abbaspour-Gilandeh
Abstract
The acquisition of basic knowledge in quality control of wheat seed using machine vision technology is important. The objective of this research was to develop hardware and appropriate software to determine seven-grain groups in wheat seed samples. Ninety-one features were extracted through 21000 single ...
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The acquisition of basic knowledge in quality control of wheat seed using machine vision technology is important. The objective of this research was to develop hardware and appropriate software to determine seven-grain groups in wheat seed samples. Ninety-one features were extracted through 21000 single seed images and the shape, texture and color features were ranked. Five classification models were investigated. The highest classification accuracy was obtained by artificial neural network with two hidden layers and the first 35 superior features. In the test run of this model with independent data, classifying accuracy for big white wheat, small white wheat, broken white wheat, wrinkled white wheat, red wheat, barley and rye were 100, 96.7, 99.3, 90.3, 99, 99.7, and 98 percent respectively with the average of 97.6 %. Shape features were more prominent and textural and color characteristics followed it respectively. Average classification accuracy in models of linear discriminant analysis, quadratic discriminant analysis, K- nearest neighbor and artificial neural network with a hidden layer were 95, 96.7, 91.6 and 97.3 % respectively. In the context of this study, the machine vision system comprising an industrial digital camera and artificial neural network with two hidden layers was identified as a valuable system in the investigation of the visual qualities of wheat seeds.
