Saeed AgaAzizi; Mansor Rasekh; Yousef Abbaspour Gilandeh; Mohamadhosein Kianmehr
Abstract
Wild oat (Avena fatua) is the most common weed of wheat fields. Given that the presence of even small amounts of wild oat in the wheat grain mass may lead to a sharp drop in the quality of flour produced, separation of wild oat from wheat will increase the purity of the seed and enhance the economic ...
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Wild oat (Avena fatua) is the most common weed of wheat fields. Given that the presence of even small amounts of wild oat in the wheat grain mass may lead to a sharp drop in the quality of flour produced, separation of wild oat from wheat will increase the purity of the seed and enhance the economic value of the crop. In present study, some physical properties of wheat and wild oat, including geometric properties, gravity properties, frictional properties and initial moisture content were determened. A gravity separator - equipped with some tools to adjust five parameters: air flow rate, frequency of oscillation, amplitude of oscillation, longitudinal slope and latitudinal slope of the table - was used to separate wild oats from wheat grain mass. The effects of these parameters were studied to achieve maximum separation of wild oat from wheat mass. Statistical analyses were performed in two factorial experiments in a completely randomized design. Also, using dimensional analysis, a dimensionless dimensional parameter V/aω was obtained (where v is air speed, a is oscillation and ω is oscillation frequency), which was effective in evaluating the effect and reducing the number of parameters. The results showed significant differences (1%) in moisture content, mass and particle density, boundary velocity and interaction between grain type and friction surface between two grain types. Also, the results indicated that the maximum separation of wild oats from wheat (70.47%) was obtained when the air flow rate was 6 m/s, the oscillation was 5 mm, the oscillation frequency was 395 cycl/min,, and the longitudinal slope and latitudinal slope were 2.5° and 1.5°, respectively.
H. Orak; S. Abdanan-Mehdizadeh
Abstract
A large amount of herbicide is being used for controlling weeds in agricultural, lawns, sport fields on yearly basis. This causes environmental pollution and economic concerns. To reduce the use of herbicides, hand labor may be the best way of removing weeds. It is, however, costly and time consuming. ...
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A large amount of herbicide is being used for controlling weeds in agricultural, lawns, sport fields on yearly basis. This causes environmental pollution and economic concerns. To reduce the use of herbicides, hand labor may be the best way of removing weeds. It is, however, costly and time consuming. In this paper, two methods of distinguishing weeds from lawns using computer vision techniques are proposed. Due to the fact that the weeds have different colors and identification of them from grass is not possible; therefore, an algorithm was developed based on the assumption that the grass area should contain more edges while the weed area is smoother than the grass area. For identification of weed/grass two methods were used, namely: Bayesian Classifier (BO) and morphology (MO. Results indicated that correct weed identification rates for MO and BO methods were 89.58% and 80.42% respectively. Furthermore, from results obtained it can be concluded that herbicide usage was reduced more than 70%, which from economical point of view as well as reduction of environmental pollution is of great importance.
Amir Hossein Afkari Sayyah; Hamid Reza Mohammad Doust Chaman Abad; Mansour Rasekh; Mahsa Sadat Razavi
Abstract
Machine vision technology can be used to detect the location of weeds around the main crop in a field as the machine moves, in order to decrease the losses of using herbicides. The purpose of this research is to determine the accuracy of image processing method in discriminating weeds from potato crop ...
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Machine vision technology can be used to detect the location of weeds around the main crop in a field as the machine moves, in order to decrease the losses of using herbicides. The purpose of this research is to determine the accuracy of image processing method in discriminating weeds from potato crop according to color features. Therefore, by performing a research which conducted in university of Mohaghegh Ardabili research field, three factors including: environmental light condition, type of weed (Shalambig, Pichack and Wheat), and level of crop growth, were investigated on discrimination accuracy. The results of this research were showed that there are no significant differences between two types of environmental light conditions. However, the main and interactive effects of two factors of type of weeds and level of crop growth was significant on discriminating system performance. According to this study, the first stage of crop growth is the best time for the visual tests (middle of the June) and among the three types of popular weed in the region, it is possible to discriminate the wheat from potato leaves with a reasonable accuracy according to RGB color model. By this method, it is possible to determine the location of weeds around the main crop by maximum accuracy of 95% dependent to different condition of treatments.
