Mohammad Askari; Gholamhosein Shahgholi; Yousef Abbaspour-Gilandeh
Abstract
In this research, horizontal, vertical and side forces on a single bentleg plow (SBLP) and a double bentleg plow (DBLP) at four forward speeds of 1.8, 2.3, 2.9 and 3.5 kmh-1 and at the constant depth of 40 cm was evaluated. The experiment was arranged in the randomized complete block design with four ...
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In this research, horizontal, vertical and side forces on a single bentleg plow (SBLP) and a double bentleg plow (DBLP) at four forward speeds of 1.8, 2.3, 2.9 and 3.5 kmh-1 and at the constant depth of 40 cm was evaluated. The experiment was arranged in the randomized complete block design with four replications. In each experiment, three perpendicular soil forces were measured and recorded. Results showed that increasing forward speed from 1.8 to 3.5 kmh-1 resulted in increasing horizontal, vertical and side forces by 14, 3.5 and 1% for SBLP and 13, 1.2 and 11.5% for DBLP, respectively. Other results indicated that horizontal force for DBLP was more than twice of that for SBLP. The vertical force was lower for SBLP but it was not more than half that of DBLP and the side force for DBLP was very less than that for SBLP. Generally, using the DBLP increases tine penetration and decreases side force which leads to balanced operation of the subsoiler and tractor and therefore recommended.
Mohammad Askari; Yousef Abbaspour-Gilandeh
Abstract
In this research, the adaptive neuro-fuzzy inference system was used for predicting the imposed forces on the tines and tractor fuel consumption during subsoiling operation. The draft and vertical forces imposed on subsoiling tines and tractor fuel consumption were measured under the effect of tine type ...
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In this research, the adaptive neuro-fuzzy inference system was used for predicting the imposed forces on the tines and tractor fuel consumption during subsoiling operation. The draft and vertical forces imposed on subsoiling tines and tractor fuel consumption were measured under the effect of tine type (subsoiler and paraplow), tillage depth (30, 40 and 50 cm) and forward speed (1.8, 2.3, 2.9 and 3.5 km/h). The field data were used to create the regression and ANFIS models for predicting the studied parameters; the results obtained from applying two models were compared with each other. The field results showed that all independent variables were effective on the studied parameters. Increase in forward speed and tillage depth resulted increase in draft force, vertical forces, and also fuel consumption. Moreover, from the point of consumption of fuel, the paraplow tine was more profitable than subsoiler tine. The results of ANFIS part showed that draft force, vertical force, and fuel consumption, the membership functions of Gaussmf, Trimf and dsigmf, with the mean square error of 0.0156, 0.0231 and 0.0212 also correlation coefficient of 0.999, 0.989 and 0.991, respectively, were the best models for prediction. ANFIS models were found more accurate than regression models, and it could be possible to calculate the model outlet for a special inlet using ANFIS outlet surfaces.
Sajad Sabzi; Yousef Abbaspour-Gilande; Hosein Javadikia
Abstract
The weeds must be removed from the field due to their competition with principal crops to use water, nutrients, sunlight, etc. There are different methods to remove the weeds: mechanically, manually or chemically (applying herbicides). For farmers, applying herbicides is a usual way, but brings some ...
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The weeds must be removed from the field due to their competition with principal crops to use water, nutrients, sunlight, etc. There are different methods to remove the weeds: mechanically, manually or chemically (applying herbicides). For farmers, applying herbicides is a usual way, but brings some concerns, from the point of environmental issues, due to equal application of chemicals all over fields, regardless the presence or absence of weed. For this reason, a machine vision system based on video processing was proposed to recognize Secale cereale L. (as a weed) from potato plant (as principal crop) to make herbicide application more accurate. Nine hundred sixty five objects were recognized after taking videos, pre-processing and segmentation. Fourteen features were extracted from each object. Using the hybrid artificial neural network-genetic algorithm, of 14 extracting features, only 6 features were selected as effective ones: average, the third moment, autocorrelation, correlation, dissimilarity, and entropy. Data were classified into two groups: training data (70% of the total data) and testing data (30% of the total data). The classification was performed using hybrid of artificial neural network - Bio-geography Based Optimization (BBO) algorithm. Performance of classification system was evaluated through analysis of confusion matrix and Receiver Operating Characteristic (ROC). Sensitivity, specificity, and accuracy were calculated using confusion matrix. The results showed that the sensitivity, accuracy and specificity of classification system reached to an acceptable level: 99.49 %, 99.65% and 98.91%, respectively. Our conclusion is that it is possible to manufacture the machine vision system with mentioned aims that work as online.
Yousef Abbaspour-Gilandeh; Fereshteh Hasankhani; Gh. Shahgholi
Abstract
Design of agricultural machinery and implements for local needs, requires determination of accurate values of physical and mechanical properties of soil, including soil-metal friction coefficient. In this study, the effects of soil moisture content at five levels and sliding speed (at three levels of ...
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Design of agricultural machinery and implements for local needs, requires determination of accurate values of physical and mechanical properties of soil, including soil-metal friction coefficient. In this study, the effects of soil moisture content at five levels and sliding speed (at three levels of 0.5, 2.5 and 3.5) on four contact materials namely: steel, cast iron, rubber, and Teflon on the soil- metal external coefficient of friction of of loam, sandy loam and loamy sand soil at 5 levels of soil moisture content was investigated In this context a device was designed and evaluated for accurate determination of soil friction coefficient. Data were analyzed based on 5×4×3 factorial experiment in randomized complete block design using MSTATC software. Due to differences in moisture content at different phases of friction, adhesion and fluid in soils with different textures, statistical analysis was performed separately for each soil texture and nested factorial design was used to study the effects of soil texture. Results showed that in all soil types, three sliding speed levels affected soil-metal friction significantly at the probability level of 1%. Also, with increasing sliding speed, soil-metal friction coefficient had incremental trend. Meanwhile, the results also showed that at the experimental sliding speeds of 0.5, 2.5 and 3.5 cm/s, the trend and pattern of the curves of soil friction coefficient versus soil moisture content were similar. Results of this study and the determined values of parameters of soil-metal friction coefficient and adhesion could be used in the design of agricultural machinery and implements, modeling of the relationship between soil & machine, draft calculation and also in tillage implements performance and their wear and tear investigations.
Sedaghat Fazeli; Yousef Abbaspour-Gilandeh; Gholamhosein Shahgoli; Zargham Fazel-Niari
Abstract
Traction efficiency and fuel consumption hgave close affinity and are considered to be important unit operations, especially during primary tillage operations. Therefore, analyzing factors that affect the amount of Traction efficiency and fuel consumption is considered important. Amongst these factors, ...
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Traction efficiency and fuel consumption hgave close affinity and are considered to be important unit operations, especially during primary tillage operations. Therefore, analyzing factors that affect the amount of Traction efficiency and fuel consumption is considered important. Amongst these factors, forward speed of tractor and tillage depth are of prime importance. Experiments were conducted for comparison of draft force and fuel consumption using, three types of cultivator blades (flat Duckfoot, Duckfoot with curve shank and Chisel plow), under sandy loam soil condition by using a factorial experiment based on randomized complete block design (RCBD). The effect of forward speed (3, 6.5 and 9 km/h) and tillage depth (10 and 20 cm) was the experimental conditions. Within each experimental plot, draft force of cultivators, fuel consumption, soil cone index, soil dispersion and percent of soil moisture content were measured.Analysis of variance showed that the effects of the blade type, forward speed and depth on the draft force and fuel consumption was significant at 1%. Comparing the draft force and fuel consumption of blades in different forward speeds and also considering the relatively light texture of the soil, it was found that duckfoot blade with curve shank with forward speed of 3 km/h was more appropriate.
Abstract
The current research investigated the draft force, soil disturbance area, specific draft, tractor fuel consumption, slippage of drive wheels, drawbar power, traction efficiency, and overall energy efficiency of subsoiling. The effects of forward speed (1.8, 2.3, 2.9 and 3.5 km/h) and depth (40 ...
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The current research investigated the draft force, soil disturbance area, specific draft, tractor fuel consumption, slippage of drive wheels, drawbar power, traction efficiency, and overall energy efficiency of subsoiling. The effects of forward speed (1.8, 2.3, 2.9 and 3.5 km/h) and depth (40 and 50 cm) on these parameters were evaluated using a randomized complete block design. An increase in forward speed increased draft force by 7%, specific draft by 15.4%, fuel consumption by 10%, wheel slippage by 2.9%, drawbar power by 108.3%, and overall energy efficiency by 6% and decreased soil disturbance area by 7.2% and traction efficiency 10%. Increasing the subsoiling depth increased the draft force by 21.3%, soil disturbance area by 25.6%, fuel consumption by 39.6%, wheel slippage by 2.8%, and drawbar power by 21.4% and decreased specific draft by 3.4%, traction efficiency by 6.7%, and overall energy efficiency by 1.4%. The most efficient operating settings were a working depth of 40 cm with a forward speed of 2.9 km/h.
