علف هرز

Various methods have been performed to control weeds in the world and the use of herbicides is one of them, but public concerns about human health have changed interest in alternative methods. Thermal methods based on flame-weeder, hot air, steam, and hot water have the potential to control weeds, but due to the high cost are not economical. Electromagnetic waves transfer energy into weeds and finally destroy them. The effect of radiation on plant mutation, high consumption of energy, and human health are problems for this approach. Unlike other methods, electrical energy is an ideal and non-chemical method for weeds. This method applies high voltage to weeds, their roots, and soil so that electric currents pass through them, and the vaporization of the liquid content of weeds kills the weeds. To increase the severity of damage to weeds, the development of a feedback mechanism is required. The ultrasonic sensor measuring physical parameters like plant height is a simple method. Some complex sensing systems include optical sensors such as infrared, and machine vision that require high-speed processors and expensive equipment. In this project, as a simple method, the monitoring of the electrical current passing through weeds was used for developing the feedback mechanism and increasing electric damage to weeds.

In this study, the system consisted of a high-voltage device that generated a 15 kV AC voltage to kill weeds, as well as a feedback mechanism that included a sensor to measure the electric current on the input of the weed killer and identify the presence of weeds and their annihilation. All parts were installed on a robotic platform, and an application on a laptop was connected to it via an access point for navigation and data reception. The system was tested in a greenhouse lab with various weeds. Initially, a test was performed to investigate the effect of high voltage on the weeds and establish relationships between the electric currents passing through weeds and their presence (before and after annihilation). During the test, the system was guided along a path and applied high voltage to kill the weeds. The feedback mechanism was then calibrated based on the extracted data on electric current relations. This allowed the system to detect weeds and their annihilation, enabling it to move to the next target once a weed had been eliminated. After calibration, a comparative test was conducted to evaluate the weed-killing efficiency of the two methods (with and without the feedback mechanism), and the results were analyzed using a t-test with p ≤ 0.01.

The observations indicated that the input electric current on the weed killer was dependent on the electric current passing through weeds. When the high-voltage electrode touched a weed, the electric current passed through it increased, and simultaneously, the high electrical energy destroyed the weed. After the removal of the weed, the electric current rapidly decreased. The average energy consumption per weed plant was estimated to be 250 joules, which can be compared with other methods. The final test comparing the use and non-use of the feedback mechanism revealed significant differences (P < 0.01) between the results obtained with and without the mechanism, demonstrating that the feedback mechanism increased the efficiency of weed annihilation. The sensing system used in the developed feedback mechanism is a simple method that is affected by the electrical resistivity of weeds. As such, it did not mistakenly detect other objects as weeds, unlike an ultrasonic mechanism. Based on these results, monitoring the electrical current passing through weeds proved to be a suitable method for developing a feedback mechanism for the weed killer to identify the presence of weeds and their annihilation.

The use of high voltage as a non-chemical and alternative method for weed control has shown promising results. The study revealed that measuring the electric current applied to the weed killer was an effective and straightforward approach to developing a feedback mechanism. This mechanism aids in identifying the presence of weeds and ensuring their elimination by intensifying the damage inflicted on them through the application of high electrical energy. To further enhance the efficiency and speed of weed control, future research should consider integrating an automatic guidance mechanism with the weed killer.

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.

 Weeds are any plant growing at wrong time in the wrong place and also harmful for crops”.Weeds compete with the crop for nutrient and light therefore reduce crop yield. Weed management is a strategy that makes a desired plant population successful in a particular agro-ecosystem using knowledge of the ecology of the undesired plants that is the weed. Recently several ways of controlling weeds are using by farmers, either by using manual, chemical, mechanical or biological means. The earliest and the simplest weed control method is manual weed control. The most effective method of weed management is by making physical contact with the weeds themselves, which are using mechanical weed control machine to reduce the chemical pesticides in mechanized paddy field. This method is among the most important goals in achieving sustainable agriculture development. The present weeding machines have been some demerit which includes of inefficiencies in different farm conditions and the lack of cost-effective purchasing for Sub-ownership land. Therefore to increase of weeding operation quality, the new weeding machine was designed as a single row with rotor propeller by solid work software and manufactured. The weeding unit has been ability to mount on the portable weed cutting machine useable in upland.

The weeding machine consists of various parts, including of gasoline engine, final drive gearbox, a power transmission shaft and a weeding unit. The weeding unit consisted of chassis, power transfer shaft, propeller, plastic skateboard and protective frame. The diameter of weeding propeller is 22 cm with two blades, which the rotary shaft passes through it. For determination of maximum torque, a total of 9 samples were selected from different areas of paddy field with high weed density and then measured. The maximum propeller torque was determined by torque meter (TQ-8801) through the shaft .The power requirement was calculated about 0.6 hp. So, the gasoline engine with 1.1 kW and 3000rpm was used in designed weeding machine. The gearbox reducer is mounted with a ratio of 1 to 10 after the clutch system was used to provide the rotor rotation at about 200-300 rpm and connected to the engine clutch. The machine field capacity was obtained about 5000 to 7500 m2 per day (10 hours). The actual field operation of weeding machine was evaluated on a 200 m2. The fields was separated into 4 plots and 4 treatments was used in each plots as follow; control (T1), manual weeding (T2), once weeding by machine (T3) and twice weeding at the same time (T4). Data was collected 35 days after transplant (15 days after weeding operation) for each treatment. The height of plant, hill number and efficiency of machine was measured in a completely randomized design with three replications and analyzed by SAS software.

 Based on the requirements of paddy field, the single rotary weeding machine was designed and manufactured. The technical data was obtained based on the computation. The technical results show that the maximum static torque was measured 0.47 N.m. The value of the shaft angle curve designed to be around 0.11 degrees which is very small. Therefore, by consideration of some factors such as; physical ability, fatigue caused by operator's work and unpredicted losses time, the field capacity was obtained about 0.5 to 0.75 ha/day. The results of evaluation experiment showed that there was a significant difference between the treatments at the probability level of 1%. The height of plant was increased in twice weeding treatment (T4) about 19%, manual weeding (T2) about 12%, and once weeding by machine (T3) about 4% in compare of control treatment (T1). The hill numbers was increased in twice weeding treatment about 44%, manual weeding about 40%, and once time weeding by machine about 15% in compared with witness treatment.

 The product performance is highly correlated with the hill number. There is a meaningful difference between mechanical weeding and manual weeding. In comparison of mechanical weeding methods, the hill number increased about 4% in twice weeding method in compare of once time weeding which shows no meaningful effect. Therefore, once time mechanical weeding (T3) is economically better due to the coexistence of yield with twice weeding (T4). Also, the hill number is increased about 29% in once mechanical weeding in compare of manual weeding. Eventually, once time weeding by machine was recommend.

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.

IntroductionWith increase in world population, one of the approaches to provide food is using site-specific management system or so-called precision farming. In this management system, management of crop production inputs such as fertilizers, lime, herbicides, seed, etc. is done based on farm location features, with the aim of reducing waste, increasing revenues and maintaining environmental quality. Precision farming involves various aspects and is applicable on farm fields at all stages of tillage, planting, and harvesting. Today, in line with precision farming purposes, and to control weeds, pests, and diseases, all the efforts of specialists in precision farming is to reduce the amount of chemical substances in products. Although herbicides improve the quality and quantity of agricultural production, the possibility of applying inappropriately and unreasonably is very high. If the dose is too low, weed control is not performed correctly. Otherwise, If the dosage is too high, herbicides can be toxic for crops, can be transferred to soil and stay in it for a long time, and can penetrate to groundwater. By applying herbicides to variable rate, the potential for significant cost savings and reduced environmental damage to the products and environment will be possible. It is evident that in large-scale modern agriculture, individual management of each plant without using some advanced technologies is not possible. using machine vision systems is one of precision farming techniques to identify weeds. This study aimed to detect three plant such as Centaurea depressa M.B, Malvaneglecta and Potato plant using machine vision system.
Materials and MethodsIn order to train algorithm of designed machine vision system, a platform that moved with the speed of 10.34 was used for shooting of Marfona potato fields. This platform was consisted of a chassis, camera (DFK23GM021,CMOS, 120 f/s, Made in Germany), and a processor system equipped with Matlab 2015 version. The video camera was installed in 60-centimeter height above the ground level. Therefore, all plants in the camera field of view (whether on the crops row or between the rows) were analyzed. This study conducted on 4 hectares of potato fields in Kermanshah–Iran (longitude: 7.03 E; latitude: 4.22 N). The most suitable color space for segmentation plants was HSV color space and most suitable channel of applying threshold was the H channel. In this study, features in two areas of color features, texture features based on gray co-occurrence matrix were extracted. Ultimately, 126 color features and 80 texture features were extracted from each object. In final six features among 206 features were selected.
Results and DiscussionAmong 206 extracted features, six effective features including the additional second component of the YCbCr color space, green index minus blue in RGB color space, sum entropy in the neighborhood of 45 degree, diagonal moment in the neighborhood of 0 degree, entropy in the neighborhood of 45 degree, additional third component index in CMY color space were selected using hybrid ANN-PSO. This means that, two set features have the same effect over plants. The result shows that hybrid ANN-SAGA classified Centaurea depressa M.B, Malvaneglecta and Potato plant with 99.61% accuracy. This accuracy is high and this meant that 1. These plants have different 6 selected features, 2. The classifier is very powerful to classify.
Conclusions1. Plants with similar features make the classification process complicated and less accurate.
2. The presence of shadow on the plants’ leaves reduces the accuracy of the classification.

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.

Sugar beet (Beta vulgaris L.) as the second most important world’s sugar source after sugarcane is one of the major industrial crops. The presence of weeds in sugar beet fields, especially at early growth stages, results in a substantial decrease in the crop yield. It is very important to efficiently eliminate weeds at early growing stages. The first step of precision weed control is accurate detection of weeds location in the field. This operation can be performed by machine vision techniques.
Hough transform is one of the shape feature extraction methods for object tracking in image processing which is basically used to identify lines or other geometrical shapes in an image. Generalized Hough transform (GHT) is a modified version of the Hough transform used not only for geometrical forms, but also for detecting any arbitrary shape. This method is based on a pattern matching principle that uses a set of vectors of feature points (usually object edge points) to a reference point to construct a pattern. By comparing this pattern with a set pattern, the desired shape is detected. The aim of this study was to identify the sugar beet plant from some common weeds in a field using the GHT.
Images required for this study were taken at the four-leaf stage of sugar beet as the beginning of the critical period of weed control. A shelter was used to avoid direct sunlight and prevent leaf shadows on each other. The obtained images were then introduced to the Image Processing Toolbox of MATLAB programming software for further processing.
Green and Red color components were extracted from primary RGB images. In the first step, binary images were obtained by applying the optimal threshold on the G-R images.
A comprehensive study of several sugar beet images revealed that there is a unique feature in sugar beet leaves which makes them differentiable from the weeds. The feature observed in all sugar beet plants at the four-leaf stage was a stretched S-shaped curve at the junction of the leaf and petiole. This unique shape characteristic was used as the pattern for sugar beet detection using GHT. To implement the Hough transform in the images, a 50-member group of samples was prepared from S-shaped curve to build appropriate patterns. Desired features for the Hough transformation were extracted from the patterns. In the next step, the attempts were made to find the images for the shapes similar to each of the patterns.
Plants were thoroughly separated from soil and residues. The accuracy of segmentation algorithm was achieved by almost 100%.
The accuracy of the generalized Hough algorithm was evaluated in two stages. In the first stage, the algorithm accuracy was assessed in detecting patterns in the images. Results showed that the accuracy of the algorithm was 96.21%. In the second stage, the algorithm was evaluated for some other test images, whereas the algorithm achieved an overall accuracy of 91.65%. In some cases, the presence of a large overlap between objects in the image reduced the detection accuracy. This was because of two main reasons; 1) high interference and ambiguity in the object edges, so that Hough transform was not able to detect the predefined patterns in the objects and, 2) weeds highly overlapped with sugar beet plants and thereby they were wrongly detected as sugar beet. However, since there is no or little interference between plants at the four-leaf stage, this interference can be eliminated by morphological operations. Due to this fact, it can be said that the results of GHT algorithm are acceptable for the detection of sugar beet in the plants close to four-leaf stage.
A special feature in the shape of sugar beet leaves was used as a criterion to distinguish between sugar beet and weeds. The results showed that by quantifying this special feature, which is an S-shaped curve near the petioles connection of beet leaves, sugar beet can be discriminated from weeds with an accuracy of 91.65 %. Recalled that this feature is a shape characteristic, therefore, the generalized Hough algorithm must be applied prior to plant canopy development, which is consistent with the critical period of weed control in sugar beet fields.

In order to study the effects of weeding and plant density on forage yield of two sorghum cultivars، an experiment was conducted in 2011 at Bahonar University of Kerman Research field station. The experiment was designed as a randomized complete block with a factorial-split treatment arrangement and with three replications. Treatments were included four levels of weeding (whole season wedding، whole season interference، twice and three times weeding) and three levels of sorghum density (20، 25 and 30 plants per m2) and two forage sorghum cultivars (Pegah and Speedfeed). The results showed that the competition of weeds with forage sorghum significantly influenced all of the measured parameters. The maximum reduction in yield was observed in weed-infested treatment (full season competition). Increasing in plant density led to the reduction of stem diameter، and increased plant height. The highest fresh fodder، dry matter and leaf to stem ratio was obtained in the density of 30 plant per m2. The maximum dry matter of 6. 352 ton per hectare was obtained in Speedfeed which was a superior hybrid over Pegah cultivar. Increasing plant density enhanced yield and yield components and competition power of crop plant which led to decreased weed dry matter production.

Farmers are now more interested in application of weed control methods and tools with less environmental side effects. Flame weeding using propane gas is an approach with almost no any chemical residue on the soil and plant surfaces or underground water. In this research، a flame weeding machine with the ability of uniform and also discrete flaming was developed and evaluated in laboratory and field scales. In this apparatus، machine vision technology successfully discriminates between soil and weeds (plants grown in between the corn rows are considered as weeds) under natural illumination. In the laboratory tests، the effect of three forward speeds (0. 5، 0. 7 and 0. 9 m s-1) on flam leading or lagging was investigated. The feasibility of using this technology for site-specific weed control of a corn field in comparison with conventional continuous flaming was investigated. The field trials were conducted with both continuous and discrete flaming approaches. The system performance and weed response to flaming treatments were evaluated by measuring the fuel consumption، counting the number of and weighting the survived and dead weeds one and three days after each flaming treatment. The results of laboratory tests showed that the effect of forward speed on system accuracy was significant and the system performance was more accurate at forward speeds of 0. 5 and 0. 7 m s-1 than 0. 9 m s-1. According to the field experiments، continuous and discrete flaming methods exhibited similar results in eradication of weeds (both number and weight-based)، while the fuel consumption of the discrete flaming was lower than the continuous one. The results also showed that discrete flaming by employing machine vision technology could be an efficient substitute for continuous flaming due to its lower fuel consumption and potential reduction of air pollution as well as other benefits of flame weeding.

A 1-year field experiment was conducted in Agricultural college of the University of Lorestan, during the 1385-86 cropping year to study the influence of mono-and inter-cropping of barley (Hordeum vulgare L.) with narbon vetch (Vicia narbonensis L.) or narbon bean (Vicia narbonensis L.) on barley grain production and weeds growth. Treatments including barley mono-cropping, different seed ratios (barley: vetch) of 0:100, 25:75, 50:50, 75:25 and 100:0 and weed interference were tested. The experiment was a factorial on the basis of randomized complete block design in three replications. The interactions results showed that number of fruitful tillers was significantly affected by intercropping system. However, the number of grain into spike, 1000-seed weight of barley and barley grain yield were not significantly affected. The maximum seed yield of barley (2.88 t.ha-1) was obtained in barley mono-cropping and the minimum seed yield (2.41 t.ha-1) was obtained in 50:50 seed ratio. Also, the lowest dry weight (6.78 gr.m-2) and number of weeds (14.3 numbers per m2) were resulted by treatments 75:25 ratio indicating the important role of this seed ratio in more effective control of weeds.