Journal of Researches in Mechanics of Agricultural Machinery

Abstract The movement of the tractor on the side or lateral slopes causes overturning and, consequently, its damage and, in most cases, may also the driver’s death. More than 50% of accidents are related to overturning. In general, tractor overturning can be in the form of lateral and backward overturning, caused by various factors, one of which is the Sharp and uneven side slopes. In this paper, two controllers are designed as a hybrid unit, the first controller is a fuzzy controller, and the second controller is a Proportional Integral Derivative controller (PID). Simultaneously employing these two controllers, the necessary force for the stability and balance of the tractor on side slopes is determined. The performance of the integrated controller was investigated using the Simulink MATLAB environment. Combining these two controllers creates a combinatory control system that can monitor the tractor’s balance on the go on the side slope to prevent overturning phenomena. The fuzzy controller determines the required force for the tractor’s stability, and the PID controller corrects the amount of this force by considering the existing errors. The information obtained from these controllers, which is in the form of determining the force required for the tractor’s stability, is transferred to a hydraulic actuator to apply this force to the tractor body during overturning conditions on side slopes. 

Abstract Corn is one of the most important agricultural products that play an important role in human nutrition. Corn kernels are highly sensitive to mechanical damage due to their large size and mass. The main purpose of this study was to evaluate the effect of the cushion box and closed let-down ladder usage in minimizing mechanical damage to corn seed during free fall. Corn seeds from a single lot of cultivars KSC 705 were loaded into bins in three methods (free fall (no obstacle), with cushion box, and with closed let-down ladder) at five levels of grain moistures (10, 15, 20, 25, and 30%), and three fall heights (2, 4 and 6 m) and evaluated for percentage of seed breakage. The results showed that the seed fall methods significantly affected the amount of damage to the seeds. The minimum and maximum grain breakage were 3.97% and 8.85%, created with a closed let-down ladder and pipe without a system (free fall). The average seed breakage was 6.38% which was observed using a cushioned box. The analysis of variance showed that all three factors, drop method, drop height, and moisture content, and their interactions had significant effects (P <0.01) on the percentage breakage of corn seeds. 

Abstract Tracks have advantages over wheels, part of which is related to the traction performance of the machine, and the other part is associated with reducing soil compaction. In this study, an experimental comparison of wheel and track in terms of vertical soil stress and vertical deformation of the soil during multi-passes has been made. This study used two-wheel and track testers in a loam-clay soil bin environment. The amount of vertical stress in the soils at 8, 16, and 24 cm depths was measured and recorded using a stress measuring system during wheel (track) multi-passes at five levels 1, 3, 7, 15, and 20. Also, the amount of vertical rut depth was measured during the movement of wheel and track passes. The most significant advantage of the track over the wheel on soil compaction occurred at depths of less than 10 cm, and the amount of vertical stress in the soil was only about 40% of the wheel. At greater depths, the wheel and the track were almost the same creating stress, so that at a depth of more than 24 cm, more than 90%, the vertical stress under the wheel and the track were similar. The vertical deformation of the soil showed a significant difference between the wheel's performance and the track. The amount of soil rut depth due to the passage of the track at all levels of the pass number did not exceed 45% of rut depth due to wheel passage. 

Abstract Farmers in our country, usually after harvesting corn, face residual problems. These residues are controlled by burning, grazing, and partially taking out of the farm. There are also remnants of residues after the conventional tillage method and common problems (most seeds usage) for the next crop (usually wheat). Therefore, research was performed to choose the proper method of mechanized residue harvesting and determine the nutritional value of residues to feed animal husbandry. To achieve the objectives, the research was performed with harvesting method treatment and the yield of harvested residues and their botanical yield. Then the following features were measured by harvested residue in the laboratory: dry matter, chemical ingredients (including Crud Protein, Ash, NDF, ADF, Lignin …), physical decay (the weight), digestibility, and moisture content in different parts of corn residues (stems, leaves, and stubble). Combined analysis of results showed stalk puller and mower were taken the first rank by harvesting 6994.2 & 6426.2 kg/ha residue, respectively, but stem shredder was placed on next rank with 2362.4 kg/ha. Most stalks were harvested using a stalk puller machine and ranked first; mower and stem shredder were in the following classes. Finally, the highest field capacity was for the stalk puller (0.86 ha/h), and mower and stem Shredders (0.54 & 0.43 ha/h) were in the following classes.  Combined analysis of qualitative results shows crude protein and metabolizable energy of harvested residues were high using stalk puller, mower, and stem shredder (359.5, 311.4 &141.4 kg/ha) and 18996.7, 16841.7 & 9569.6 Mcal/ha, respectively positioned in first-class. A combined analysis of nutritious substance performance results based on botanical division shows that yield of leaves higher than stems, and stems are higher than stubbles. Therefore, the stalk puller machine was chosen as the most suitable machine performance than the other residue harvesting machines with 99% significant confidence.

Abstract To rank the factors affecting waste in the citrus production cycle in three northern provinces include, Guilan, Mazandaran, and Golestan, first of all using the literature review and consulting with experts in four stages: cultivation, harvesting, post-harvest, and the market as the main criteria and 17 effective factors (4 sub-criteria in cultivation, four sub-criteria in
harvesting, five sub-criteria in post-harvesting, four sub-criteria in market) as sub-criteria were identified. The questionnaires were designed as pairwise comparisons and by targeted sampling method completed by face-to-face interviews with 10 experts. Data were analyzed using Analytical Hierarchy Process (AHP). The results showed that among the main criteria, the most effective stage in the citrus losses was the stage of cultivation by weight of 0.542. After that, from the expert's point of view, the post-harvest stage by weight of 0.284, the harvest stage by weight of 0.129, and the market stage by 0.045 are in the next rank. In comparing and ranking the sub-criteria, the indicator of "Lack of timely and correct control of pests and diseases" by weight of 0.320 was identified as the most important factor in citrus losses. Afterward, the indicators of "lack of frost control" (0.156) in the second place, "Inappropriate stockrooms and cold storage" (0.134) in the third place, "Harvesting at the wrong time" (0.075) in the fourth place, and "Lack of suitable control of post-harvest diseases" (0.064) in the fifth-place, were ranked in importance respectively. 

Abstract Today, the implementation of precision agriculture to manage and control citrus pests can effectively optimize pesticide use, reducing adverse environmental effects and ensuring human health. But managing individual trees on a large scale is a big challenge. Therefore, using a machine vision system seems necessary to monitor and identify pests in different parts
of the trees at different times. In this study, an Unmanned Aerial Vehicle (UAV) equipped with a camera was used to identify pests in other parts of the citrus orchard. For the optimal selection of the UAV linear speed, three speeds in the range of 10, 20, and 30 cm/s were considered. After framing and formatting, the recorded videos were trained in three pretrained models: AlexNet, VGG-16, and GoogleNet. Three optimization algorithms were used in the network training process: SGDm, RMSProp, and Adam. The evaluation results showed that the AlexNet model, with the help of SGDm algorithm, had the best performance in terms of detection accuracy. The highest pest detection accuracy was 96.43% at a velocity of 10 cm/s, so increasing the linear velocity to 30 cm/s reduced the detection accuracy by 13%. The results of this study show that using a combination of UAV technology and artificial intelligence methods can help professionals and farmers manage and control citrus orchard pests. 

Abstract Fish fillet is a corruption-sensitive product that requires several quality and hygiene reviews
before consumption. Many methods of fish quality and freshness determined based on
destructive and time-consuming tests require manpower and sample sizes. This study
evaluated the ability of a non-destructive fusion method of ultrasound, electrical resistance
measurement, and colorimetry to monitor the freshness of rainbow trout fillets at refrigerator
temperature. For this purpose, the samples of fish fillets were prepared and stored in a
refrigerator for 12 days, and physical tests such as ultrasound wave attenuation, electrical
resistance, and colorimetry to determine the color component of the fillets in both color space
of RGB and Lab, and sensory evaluation (color, taste, odor, and texture) were performed.
Also, a chemical test was conducted to determine the fillets ash, protein, and fat during
storage. Extracted data from physical tests were subjected to two classification models of
neural network and decision tree for determining the best classification method according to
the fillet freshness. Various single hidden layer neural networks with zero to ten neurons and
decision trees with five algorithms of LMT, REP, J48, RANDOM FOREST, and RANDOM
TREE were evaluated. In each model, physical test data were considered as input and storage 
days as output. Based on the results, the highest classification accuracy (100%) and the lowest
mean error value (0.02) were obtained for the neural network with 6 neurons in the hidden
layer, and correspondingly 100 percent and nearly zero for the decision tree with J48
algorithm The confusion table of both models also confirmed the classifiers' accuracy. The
integrated non-destructive system presented in this study does not require sample preparation. 
It is accurate, relatively inexpensive, and can be used during the food and fishery process to
determine their freshness.

Abstract Today, horticultural crops are often harvested mechanized in developed countries, so to 
design devices such as shakers used for harvesting, and we must have the necessary 
information about the wood of the branches and trunks of trees so that we can harvest with 
minimal damage. Therefore, the mechanical properties of almond and walnut wood, including 
shear modulus, yield stress, fracture stress, toughness, and germination modulus, were 
investigated to distinguish these values between healthy and unhealthy wood and the effects 
of factors such as storage time, moisture, diameter, cutting speed and condition of wood of 
two different wood products (almond and walnut) were investigated, and the results were 
compared. The condition of the wood was in two types, healthy, which did not have any 
worm corrosion, and unhealthy, which the Zeozera Pyrina damaged. The results of the 
analysis of variance of the main factors and their interaction in almond wood showed that the 
main factors of cutting speed and healthy and unhealthy and the interaction effects of two 
factors of cutting speed and year (shelf life), cutting speed and healthy and unhealthy, cutting 
speed and moisture. They had a significant effect on the 5% probability level. In walnut 
wood, the effect of the main factor of moisture and the interaction of healthy and unhealthy 
and humidity were significant at 5% probability. With increasing cutting speed in almond 
wood, the shear modulus increased from 28 to 32 and then to 39 MPa, respectively, the stress 
at the yield point increased from 26 to 28 and then to 45 MPa, and the stress at the breaking 
point increased from 20 to 32 MPa. It had more moisture and was more flexible. It was seen 
more than wood, which had less moisture. Also, these values in walnut wood with higher 
moisture decreased more than in wood with less moisture. 

Abstract Artificial seed production is a developing technology in plant biotechnology that provides considerable potential for conserving rare, endangered, and threatened species that are difficult to propagate through conventional propagation methods. In the present investigation, Image J software was used to process the digital image of the capsules and determine the volume, sphericity, and artificial seed centrality index. Response surface methodology (RSM) and artificial neural network (MLP and RBF) were utilized to model, predict and optimize the physical properties of the artificial seed of Phalaenopsis orchid. The training and validation data sets of the studied models were compared using regression  coefficients (R) and sums of squared errors (SSE). Artificial seeds were obtained through encapsulation of Phalaenopsis
orchid protocorm using various levels of sodium alginate concentration (3, 4, and 5%), calcium chloride concentration (100, 125, and 150%), and drop height (1, 1.5, and 2 cm) as input variables. Physical properties of artificial orchid seeds, including (capsule volume, sphericity index (SI), and concentric index (CI)) were considered as output variables. The
results showed that image processing is an effective method for determining the physical properties of artificial seeds. According to the regression models, Results showed that interaction effects of the main factors are positively correlated with volume (p< 0.05). The effects of sodium alginate× calcium chloride concentration were significantly correlated with
the CI index (P< 0.05). In addition, the sphericity value was positively correlated with sodium alginate concentration. In general, based on R2 and SSE values, the MLP model showed a much more accurate prediction than RSM and RBF models in terms of the values R2 and SSE. The optimum condition for volume, sphericity and CI index values was obtained using MLP 3-10-1 (R= 0.79, SSE= 0.0014), MLP 3-14-1 (R= 0.57, SSE= 0.0031) and MLP 3-14-1 (R=0.67, SSE= 0.0042), respectively. It is concluded that the ANN (MLP) is a desirable tool for the prediction and optimization of the physical properties of Phalaenopsis orchid synthetic seed.

Abstract One way to increase crop production and productivity is to properly feed crop plants, which
results in increased production per unit area. Chemical fertilizers and compost alone cannot
provide for plants' needs. Therefore, mixing these two types of fertilizers in a specific ratio is
necessary. The main problem with mixing compost and chemical fertilizers is controlling their
mix ratio. In this study, a twin-shaft paddle mixer was designed, fabricated, and evaluated to
solve the problem of adequately mixing compost and fertilizer urea. The constructed device
includes a 1.5 kW electromotor, chassis, paddle, and chain-sprocket mechanism. Experiments
at three levels of the axis rotational speed of 20, 40, and 60 rpm and three levels of mixing
path length of 72, 108, and 144 cm, and two levels of paddle angle of 45 and 60-degree with a
rotating axis in three replications under factorial test based on completely randomized block
design. The analysis of variance of a distance effect on the measured urea-compost ratio
showed that the rotational speed of the axes, the paddles angle, and mixing length of 1 and 5
% level were significant. Also, the results of the comparison of mean effects of treatments
showed that the paddle angle of 45 degrees, the rotational speed of 40 rpm, and the mixing
length of 108 cm had the best performance.