H. Balanian; S. H. Karparvarfard; A. Mousavi Khanghah; M. H. Raoufat; H. Azimi-Nejadian
Abstract
In this study, a model was developed for predicting the seeding rate of corn seeds of a typical row-crop planter equipped with a multi-slot feeding device. To this, nine multi-slot rotors (with 4, 5 and 6 slots in three angles of mouth including 23°, 25° and 27°) were designed and manufactured. ...
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In this study, a model was developed for predicting the seeding rate of corn seeds of a typical row-crop planter equipped with a multi-slot feeding device. To this, nine multi-slot rotors (with 4, 5 and 6 slots in three angles of mouth including 23°, 25° and 27°) were designed and manufactured. Tests were carried out at four levels of angular velocity of 40, 52, 62 and 78 rpm on grease belt moving at constant speed of 3.5 km h-1. Tests were completed in three replications. Discharge flow rate was measured and recorded for each treatment. The data were used to develop a model which can be used for predicting the seeding rate under various numbers of slot, mouth angle and rotor angular velocity. According to the results, angle mouth of slots, number of slots, angular velocity and the dual interaction between them showed increasing effects on weight flow rate of seeds (P-value<0.01). In the next step, raw data were used to develop the two desired models: based on the dimensional analysis technique and response surface methodology (RSM). The models outputs were compared to experimental data. The standard error of estimate for flow rate for dimensional analysis and response surface methodology (RSM) were 68.13 mm3 s-1 and 475.59 mm3 s-1, respectively. The dimensional analysis model was closer to experimental data rather than the RSM method. Thus, to predict the volume flow rate of seed, the dimensional analysis model is recommended.
Z. Kavoosi; M. H. Raoufat
Abstract
In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was evaluated ...
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In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was evaluated by ground and air observations. The purpose of this study was to investigate the capability of aerial images captured by an unmanned aerial vehicle (UAV) in identifying the distances between corn seedlings and as a result, assessing the quality of planter performance. Collected data from ground and aerial imagery were used to calculate seed establishment indices including multiple index, miss index, quality of feed index, precision index and also emergence rate index (ERI), for each plot. Images captured from10 m altitude (4.5 mm pixel-1) could give satisfactory results in relation to our objectives. Our results show that acceptable correlations existed between terrestrial and aerial seedlings spacing data sets (0.94<R<0.98) suggesting the aerial imagery is a good choice for evaluating the seed establishment and estimating ERI. Aerial imagery data source underestimated quality of feed and precision indices, overestimated miss index and could not provide processed data range needed for computing multiple index due to low image resolution, weeds presence within crop rows and overlapping of leaves.
H. Izadi; S. Kamgar; M. H. Raoufat
Abstract
Introduction: The quality of agricultural products is associated with their color, size and health, grading of fruits is regarded as an important step in post-harvest processing. In most cases, manual sorting inspections depends on available manpower, time consuming and their accuracy could not be guaranteed. ...
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Introduction: The quality of agricultural products is associated with their color, size and health, grading of fruits is regarded as an important step in post-harvest processing. In most cases, manual sorting inspections depends on available manpower, time consuming and their accuracy could not be guaranteed. Machine Vision is known to be a useful tool for external features measurement (e.g. size, shape, color and defects) and in recent century, Machine Vision technology has been used for shape sorting.
The main purpose of this study was to develop new method for tomato grading and sorting using Neuro-fuzzy system (ANFIS) and to compare the accuracies of the ANFIS predicted results with those suggested by a human expert.
Materials and Methods: In this study, a total of 300 image of tomatoes (Rev ground) was randomly harvested, classified in 3 ripeness stage, 3 sizes and 2 health.
The grading and sorting mechanism consisted of a lighting chamber (cloudy sky), lighting source and a digital camera connected to a computer.
The images were recorded in a special chamber with an indirect radiation (cloudy sky) with four florescent lampson each sides and camera lens was entire to lighting chamber by a hole which was only entranced to outer and covered by a camera lens.
Three types of features were extracted from final images; Shap, color and texture. To receive these features, we need to have images both in color and binary format in procedure shown in Figure 1.
For the first group; characteristics of the images were analysis that could offer information an surface area (S.A.), maximum diameter (Dmax), minimum diameter (Dmin) and average diameters. Considering to the importance of the color in acceptance of food quality by consumers, the following classification was conducted to estimate the apparent color of the tomato;
1. Classified as red (red > 90%)
2. Classified as red light (red or bold pink 60-90%)
3. Classified as pink (red 30-60%)
4. Classified as Turning (red 10-30%, It showed the color green change to pink)
5. Classified as Breakers (red < 10%, It showed the color green change to yellow)
6. Classified as green (The whole fruit area was green)
To estimate the quality of tomato, we need to estimate background of the images. For this purpose we should follow the preocedure as shown in Fig.2.
According to flowcharts shown in Fig.1, our samples will be in the following stages: (Fig.3.)
Fig.4 shows that during the ripening of tomato red color is increased and green color is decreased. Indicating chlorophyll degradation while lycopen started to be produced.
According to figure 6 we utilize the R and G value of tomato for ripening decision. As ripening data we utilize the mean of red and green values of pixels that are used for this goal. For correct processing of last group, edge of images were removed that had incompletely understood of the fruit color and determine color coefficients, the system with slight error could detect all parts of the damage. Quantity of damage area reported in the proportion of the total area of tomato.
In the present work, 5 factors were considered and the linguistic variables corresponding to the values were created in 4 levels: size, color or ripening, a healthy and final level that classified tomatoes in 8 classes. In size level input values were minimum diameter and surface area. These values classified the tomatoes into 3 groups. In color level input values were Red and Green component values. These values were used to classify the tomatoes in 3 group, too. In healthy and unhealthy level, input value was proportion of damage area to tomato total area. This value were used to classify the tomatoes in 2 group. In final level, outputs of previous levels are our inputs now. This values going to classify the tomatoes into 8 final groups.
Results and Discussion: This system can classify the tomatoes in 8 groups just with rules. For this reason we measured the accuracy of the system before training. This values were 70.7, 82.0, 95.7 and 75.5% for size, color, health and final system respectively. For achieving all ability of ANFIS in classifing we done the above measuring after training of machine. The results were 80.9, 89.5, 95.7 and 81% for size, color, health and final system respectively, that indicate the accuracy of the system is raised by 10%. A validation step is done in this study. The accuracy of the system is measured versus a human expert. The classification was done with 60 samples. The accuracies of machine were 75.9, 83.8, 94.2 and 76.5%. Analysis of results with qui-square test indicated that there is no significant difference between machine results and human expert choices.The validation process proved that system is useful in this purpose.
Conclusions: This research was about evaluating of using machine vision and ANFIS in grading machines and done in off-line mode. The research was redirected to the following general conclusions:
1. To obtain an estimate of tomatoes, sample sizes were measured by using calipers and machine vision, the results showed that this system can be used to obtain dimensions.
2. For the purpose of size grading, the small diameter and the surface area of the image was used whichyielded 67% and 62% accuracy for determining the mass, in comparison the ANFIS system performance was precisely 81%.
3. For the purpose of color grading, red and green were used which is a better description of quality. For this the ANFIS system was used for color grading and it performed at 89.5%.
4. For the purpose of sample selection grading (dividing the rotten from the good), optical robot was used. The outcome of system ANFIS and the optical robots had the same results of selection at 95%.
5. In an aggregate or globally, the criteria from the above was used as an input for the grading and classification. Based on these inputs, the ultimate output was consequently categorized into 8 groups. The precision of the division or the selection was determined to be 81.5%.
6. With respect to the testing based on chi-square, it can be determined that this system can replace human workers. In addition, based on the performance and necessary adjustments to the system and its grading criteria better system can be built to replace human workers.
Design and Construction
E. Chaligar; M. H. Raoufat; S. M. R. Khadem; E. Chaligar
Abstract
In conventional cultivation of sugar beet the weeding and crust breaking are mostly performed manually. The objectives of this research were to design, fabricate and evaluate a soil crust breaker and weeding implement equipped with a detecting sensor. Each unit consisted of a distance detecting sensor ...
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In conventional cultivation of sugar beet the weeding and crust breaking are mostly performed manually. The objectives of this research were to design, fabricate and evaluate a soil crust breaker and weeding implement equipped with a detecting sensor. Each unit consisted of a distance detecting sensor and a hydro-motor for driving the blades and pneumatic valves for moving the blades. The hydro-motor was activated by the sensors. To avoid damaging the plants, a command signal was sent to the pneumatic valves to move the blades up and down and pass safely. Three configurations of cutting blades were considered which could be mounted to the crust breaker. The field evaluation was conducted with two tractor forward speeds (0.4 and 1 km h-1) and four plant-to-plant spacings within rows (20, 25, 30 and 40 cm) all with three different blade shapes. For field evaluation split plot experiments arranged in a completely randomized block design with three replications. The number of plants damaged (evaluated only for four-lobe blades) and size of broken crusts (evaluated for all blade shapes) for various treatments were recorded and compared. The results of analysis indicated that the higher the inter-row spacing the lower the injury to the plant. The highest and the least damage to the plants occurred for within-row plant spacings of 20 and 40 cm, respectively. The forward speed was also significantly affected the percent of plant damaged. The forward speed of 1 km h-1 at 20 cm spacing had the most (59%) and the speed of 0.40 km h-1 at 40 cm spacing had the least effect (3.3%) on the percent of plant damage. The two-lobe blade could result in the maximum surface area broken.
M. A. Rostami; M. H. Raoufat; A. A. Jafari; M. Loghavi; M. Kasraei; S. M. J. Nazemsadat
Abstract
Local information about tillage intensity and ground residue coverage is useful for policies in agricultural extension, tillage implement design and upgrading management methods. The current methods for assessing crop residue coverage and tillage intensity such as residue weighing methods, line-transect ...
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Local information about tillage intensity and ground residue coverage is useful for policies in agricultural extension, tillage implement design and upgrading management methods. The current methods for assessing crop residue coverage and tillage intensity such as residue weighing methods, line-transect and photo comparison methods are tedious and time-consuming. The present study was devoted to investigate accurate methods for monitoring residue management and tillage practices. The satellite imagery technique was used as a rapid and spatially explicit method for delineating crop residue coverage and as an estimator of conservation tillage adoption and intensity. The potential of multispectral high-spatial resolution WorldView-2 local data was evaluated using the total of eleven satellite spectral indices and Linear Spectral Unmixing Analysis (LSUA). The total of ninety locations was selected for this study and for each location the residue coverage was measured by the image processing method and recorded as ground control. The output of indices and LSUA method were individually correlated to the control and the relevant R2 was calculated. Results indicated that crop residue cover was related to IPVI, RVI1, RVI2 and GNDVI spectral indices and satisfactory correlations were established (0.74 - 0.81). The crop residue coverage estimated from the LSUA approach was found to be correlated with the ground residue data (0.75). Two effective indices named as Infrared Percentage Vegetation Index (IPVI) and Ratio Vegetation Index (RVI) with maximum R2 were considered for classification of tillage intensity. Results indicated that the classification accuracy with IPVI and RVI indices in different conditions varied from 78-100 percent and therefore in good agreement with ground measurement, observations and field records.
