فهرست مطالب nikrooz bagheri

Remote sensing is a unique and cost-effective tool that provides information about the nitrogen status of plants in a non-destructive way. The objective of this study is to evaluate the effectiveness of aerial multispectral imagery captured by UAV for estimating corn nitrogen (N) and chlorophyll (Chl) content at different growth stages. The study used a fully randomized experimental design with four treatments of nitrogen fertilizer (0, 50%, 100%, and 150%). Ten plants were randomly selected in each plot at the phenological stages of 8 leaves (V8) and tasseling growth stages (VT) for sampling. Leaf samples were taken to measure total nitrogen (N) and chlorophyll (Chl) content. Mathematical models were created using vegetation indices extracted from aerial multispectral images to estimate the amount of nitrogen and chlorophyll. The models were evaluated using the leave-one-out cross-validation method. The results showed that there is a significant positive relationship between the leaf dry weight (LDW), the Chl and N content with the amount of nitrogen fertilizer used. So, the results indicated that the REIP index is suitable for estimating chlorophyll content in both the V8 (R2 of 0.997) and VT (R2 of 0.980) growth stages. Additionally, the REIP index was found to be an appropriate index for estimating N content in both growth stages (R2 of 0.980). It can be concluded that aerial multispectral remote sensing technology is a reliable method for estimating corn nitrogen and chlorophyll content.

One of the decision-making methods using quantitative data is multi-criteria decision-making, which helps the manager make rational decisions by considering different conflicting criteria. Planning for the optimal use of water and soil resources, in addition to their conservation, involves increasing production, income growth for farmers, and rural economic prosperity. Given the limited resources, the optimal cropping pattern should be appropriate and effective for each region. The selection of an appropriate cropping pattern and, if necessary, adjusting the cultivation based on regional and national needs and the relative advantage of products in different regions is of great importance. So far, many studies have been conducted to optimize cropping patterns. The main reasons for the low productivity in agricultural production are the inappropriate allocation of resources and production factors. Although farmers are faced with various options for agricultural activities and crop selection, their production factors are limited. By formulating and implementing an optimal cropping pattern in a region, it is possible to familiarize farmers with the available potentials while considering the constraints of production resources, reducing risk, ensuring system stability, improving income in production, and creating the groundwork for the growth and prosperity of agricultural regions. This leads to agricultural development, increased profitability, and profitability. Decision-making is one of the most important and fundamental tasks of management, and the quality of decision-making determines the achievement of organizational goals. However, the main focus of research has been on improving the productivity of crops based on water and soil resources, with little attention given to the subject of agricultural mechanization and the criteria of regional operators. Considering the criteria of farmers in decision-making processes increases the acceptance of programs and better collaboration in implementing them. Additionally, most studies have only used one decision-making method, but each decision-support method provides a unique outcome and may differ from the results of other methods. Therefore, in this study, multiple decision-making methods were evaluated and compared to determine the best method to be used. Based on the given explanation, the objective of this research is to prioritize the factors influencing the determination of suitable cropping patterns for agricultural products using four different decision-making methods and introduce the best method.

The research area is sited in Silakhour Plain, in Lorestan Province, between the cities of Doroud and Borujerd, at geographical coordinates 38 degrees 36 minutes north and 48 degrees 31 minutes east, in coordinate zone 39. In this study, Analytic Hierarchy Process (AHP), TOPSIS, VIKOR, and Simple Weighted Average methods were used for decision-making. The parameters investigated in this study included technical-agricultural, economic, macro-governmental, soil and climate, social, and production support factors. These factors included sub-factors such as the presence of mechanized planting and harvesting equipment, farming unit larger than one hectare, water requirements of plants, distance from the place of consumption, farming unit smaller than one hectare, crop profitability, required capital, suitable market for the product, customs and farmer habits, farmer education, crop cultivation experience, guaranteed purchase of the product, government incentive policies, physical characteristics of the soil in the region, chemical characteristics of the soil in the region, average temperature during the growing season, elevation of the region, average rainfall in the region, insurability of the product, sustainability of crop production, availability of seeds compatible with regional conditions, and prevalent pests in the region. To validate all judgments made in the analytic hierarchy process method, the inconsistency ratio was calculated using Expert Choice11 software. Based on this, the inconsistency ratio was calculated to be less than 0.1 in all steps of this method. If the consistency ratio is 0.1 or less, it indicates consistency in the comparisons and confirms the validity of the judgments. To reach a general consensus on the ranking of parameters, the method of merging average ranks was used.

In this study, 22 indicators were identified, including the presence of mechanized planting and harvesting equipment (0.061), operational unit larger than one hectare (0.021), crop water requirement (0.014), distance from consumption site (0.008), operational unit smaller than one hectare (0.006), product profitability (0.125), required cash capital (0.116), suitable market for the product (0.020), agricultural customs and habits (0.028), education of the farmer (0.006), crop cultivation experience (0.130), guaranteed purchase of the product (0.3), government incentive policy (0.122), physical characteristics of the soil in the region (0.075), chemical characteristics of the soil in the region (0.022), average temperature during the growth season (0.022), elevation of the region (0.008), average precipitation in the region (0.006), insurability of the product (0.013), stability of crop production (0.007), availability of seeds compatible with the regional conditions (.0004), and common pests in the region (0.0002). Among the mentioned parameters, cash capital (0.236), water requirement for cultivation (0.233), product profitability (0.098), and operational unit larger than one hectare (0.039) are considered the most important factors. Certain purchase of the product (0.3), product profitability (0.0125), government incentive policy for products (0.122), and required cash capital for cultivation (0.116) were identified as the most important factors influencing the cropping pattern, respectively, using the hierarchical weighted method. product profitability and required cash capital are among the influential factors in the design of cropping patterns for agricultural products. The results showed that the ranking of agricultural products for inclusion in the regional cropping pattern differs in each decision-making method. Although grains and sugar beets have high rankings in all groups, it is necessary to validate and finalize these methods with integrated approaches to reach a general conclusion. In a multi-criteria decision-making problem, multiple decision-making methods may be used because decision-makers do not limit themselves to one decision-making method, and they may obtain different results using different methods. In fact, in such situations where the results of different methods of multi-criteria decision-making are not the same, the question is which option should be chosen. To reach a general conclusion, it is necessary to validate and finalize these methods with integrated validation and finalization approaches. Among the integration methods,

Today, intelligent agriculture is developing with the advancement of electronic and information technologies. The key to access intelligent agriculture is data gathering and analysis. Therefore, data gathering is one of the fundamental actions to implement precision and intelligent agriculture. This paper aims to introduce the audience to the various data acquisition platforms such as satellite, aerial (manned and unmanned), and ground vehicle platforms and to present the features and limitations of each method based on technical and economic parameters. Technical parameters that influence decision-making include spatial resolution, spectral and temporal resolution, maneuverability, sensor variety, the extent of the covered area, and accuracy. Based on the results, all types of data acquisition technologies can be used in agriculture, and selecting the appropriate system should be based on technical and budget considerations, the availability of technology, the size of the region, and the object. By reading this paper, the audience can choose the right data acquisition system for their executive or research activities in the field of agriculture

Tomato fruitworm (Heliotis) is the most destructive pests of the crops of Solanaceae family. In favorable environmental conditions, Heliotis spread quickly. In recent years, drone prayers have been used to control pests but the performance of these sprayers has not been evaluated in tomato fields yet. This research was carried out in a tomato field infected by the the Heliotis pest and the drone sprayer performance was compared with the performance of tractor lancer sprayer and back knapsack sprayer (atomizer) in the form of Randomized Complete Block Design (RCBD) and in three replications. The results showed that in tractor lancer, atomizer and drone sprayers the solution consumption was 1200, 211 and 32 liters per hectare, drift was 45.6, 13.06, 20.68 percent and field efficiency was 60.66, 69 and 90.2 percent respectively. In 14 days after spraying, the effectiveness was calculated 59.75%, 40.06% and 41.75% in drone method, atomizer and lancer methods respectively. In terms of effectiveness coefficient, the spraying with the drone was found better than that with atomizer and lancer methods on the 4th and 8th days after spraying. The spraying quality coefficient in atomizer and drone sprayer was 2.92 and 1.22, respectively. Economically, profit to cost ratio was calculated 4.15, 4.88 and 2.33 in lancer, atomizer and drone method respectively. Finally, according to the results, the use of drone sprayer to control tomato pests is recommended technically but in terms of economical aspects more studies should be done.

Every year, frost causes the loss of many agricultural products. There is numerous equipment to protect plants against frost. Late turning on these equipment causes inefficiency in raising the air temperature, and early turning them on will increases energy consumption and costs. Therefore, accurately forecasting frost is crucial for turning on the equipment on time. In this research, an intelligent radiation frost forecasting and warning system (IFFS) based on the Internet of Things (IoT) technology was designed and constructed. This system comprises a wireless sensor, computing, and intelligent forecasting based on deep learning methods and warning announcements to the farmer by a message. Intelligent forecasting based on forecasting dew point temperature for the next three hours according to the in-situ measurement of temperature and relative humidity of the air. The meteorological data of the studied regain from 2011-2021 were used to train the network. The IFFS Performance was evaluated. Based on the obtained results, the system accuracy in measuring temperature and relative humidity of the air was 99% and 98%, respectively. The F-score of the IFFS obtained 96%, and the system accuracy in the warning announcement obtained 100%. Finally, applying the IFFS for better protection of plants is recommended.

Tomato moth is one of the most destructive pests in tomato farms. One of the methods to control this pest is the chemical spraying using different sprayers. In recent years, spraying drones have also been used for chemical control of pests, but the performance of these sprayers in tomato farms has not been evaluated yet. In this research, three different spraying methods were evaluated in controling the Tota pest in a tomato farm in Safadasht Karaj region in a completely randomized design (RCD) and in three replications. Experimental treatments included:  1- spraying with the tractor lancer sprayer, 2- spraying with a back knapsack sprayer (atomizer), and 3- spraying with Sprayer drone. The results obtained with Duncan's multi-range test at the level of significant of 1% showed that the consumption of solution of pesticide was 373.3, 207.1 and 8.4 liters per hectare, the rate of drift was 46.5%, 32.6% and 9.9%, the effective capacity of farm was 0.5, 0.4 and 4.9 hectares per hour, and the farm efficiency was 44.6%, 35.7% and 68.0% in tractor lancer sprayer, knapsck sprayer and sprayer drone, respectively. In terms of effectiveness, based on the number of tunnels made by larvae on fruits and the number of live larvae found after 3, 7 and 10 days of spraying, it has been found that the tractor lancer sprayer had the highest effectiveness with 82% in 10 days after spraying. The spraying quality coefficient in knapsack sprayer and drone sprayer was 2.92 and 1.22, respectively. From the economical point of view, the benefit - cost ratio was 1.93, 1.88 and 4.80, in tractor lancer sprayer, knapsck sprayer and sprayer drone respectively. which shows that the sprayer drone works better than the other two sprayers. Based on technical and economical results, our recommendation is to use sprayer drone to control the Tota pest, despite its lower effectiveness.

In order to present a new, non-destructive, accurate, and fast method for estimating the nitrogen content of corn, Unmanned Aerial Vehicle (UAV) multispectral sensing technology was used.

The experiments were performed based on a randomized complete block design in four levels of nitrogen fertilizer (zero, 50, 100, and 150%) in Varamin in 2018. Sampling was carried out in two stages of fertilization (8-leaf Stage and Tasseling Stage). Multispectral aerial imaging and ground sampling was performed one week after each fertilizer application. After processing aerial imagery, vegetation indices were calculated and their correlation with the results of ground sampling was determined.

Based on the results obtained from the correlation coefficients (r) and best subsets regression, among the spectral vegetation indices, Normalized Difference Vegetation Index (NDVI), Nitrogen Reflectance Index (NIR), and Modified Triangular Vegetation Index2 (MTVI2) indices in both eight leaf collar (V8) and tasseling (VT) of maize growth stage was identified as the best indicator to estimate the nitrogen content of forage maize. At VT, a positive and significant relationship was obtained between NDVI (R2= 0.86, P≤0.001), NRI (R2= 0.70, P≤0.001) and MTVI2 (R2= 0.46, P≤0.01) indices with maize nitrogen content.

It can be concluded that UAV multispectral imaging provides acceptable accuracy in determining the nitrogen content of maize. This technology can help farmers to determine the appropriate time of fertilization.

Tthis research was conducted with the aim of evaluating of two common spraying methods for Brevicoryne brassicae control during stemming stage and to introduce a more suitable method according to the technical parameters affecting the performance and efficiacy of spraying. The test was conducted in the form of a completely randomized design in three treatments and three replications based on the obtained results, for the tractor-mounted boom sprayer and tractor-mounted lance sprayer, the amount of consumed volume was 179 and 279 L/ha, respectively, the effective field capacity was 5.0 and 0.8 ha/hr, respectively, the field efficiency was 56.8% and 55.5%, respectively, the crop damage area obtained 3.85% and 4.45%, respectively, the spray quality coefficient was 1.96% and 3.31%, respectively, and the average drift was 21.0% and 35.7%. Also, there was no significant difference between the two sprayers in 3 and 7 days after spraying. However, 14 days after spraying, there was a significant difference between the treatments, and the spraying efficacy of the boom sprayer reached 83% and 41% for the lance sprayer. Considering the better results of the tractor-mounted boom sprayer and its greater spraying efficiency than the tractor-mounted lance sprayer, this method is recommended to control pests during the canola stemming stage.

The recent development of UAVs is due to progress in electronics, control, and computer science combined with mechanics and flight science. What is evident in society is more mention of the advantages and capabilities of this technology, and less attention is paid to the technical parameters and basic challenges of the technology. The UAVs capability has only been expressed in various ways without expressing the challenges to society and the advertisement on the import of this technology continues. Yet, some decision-makers consider this advanced and expensive device only for import and flashy records. On one hand, the present article aims to express the important parameters and challenges of this advanced technology by stating the background, types, and applications, more awareness, and a detailed understanding of the challenges for the wise choice of decision-makers and users. On the other hand, future research topics for researchers, designers, and manufacturers would be another output of the article.

In the present century, the development of information technology (IT) is bringing another revolution to achieve the maximum increase in crop production and efficiency of using agricultural inputs. The greatest role of IT in advanced agriculture (precision, smart and digital) is through the IT-based crop management cycle. So, this paper aims to introduce the components of IT-based crop management cycle and their role in advanced agriculture. IT-based crop management cycle consists of five main components including (1) crop, (2) remote sensing platforms supporting sensors such as satellites, drones and proximal equipment (3) data, (4) decision-making through algorithms such as artificial intelligence and (5) the actuation through variable rate technology. The main purpose of this cycle is to obtain accurate information from every pixels of a farm for smart decision making. This cycle enables the collection of objective data for the implementation of advanced agriculture. In fact, advanced agriculture is based on the principle of monitoring and preparing maps and spatio-temporal information, and this principle arises from the existence of heterogeneity in the field. The role of IT-based crop management cycle components is identifying these heterogeneities (differences in land slope, altitude, soil nutrients, pests and weeds, moisture, salinity (EC), pH, organic matter or even crop yield) for helping to perform precision and smart field management in different parts of the production field

Today, improving productivity in agricultural production due to limited production resources and population growth will be possible only with the use of technology. To improve the level of technology in the production of agricultural products, in the first step, it is necessary to monitor the current state of technologies used in agriculture. Therefore, to achieve this goal, the level of technology used in the production of strategic agricultural products in the fields of agriculture, horticulture, livestock, poultry, and aquaculture was determined and evaluated using the opinions of experts and researchers of the Ministry of Jihad Agriculture. Based on the obtained results, the level of technology in the production of strategic crops was 2 and 3. The level of technology was achieved in the production of strategic horticultural products, especially in the harvest was 1. Also, the level of technology in the production of strategic livestock products was 2 and 3. The level 3 technologies used were mostly imported. The level of technology in aquaculture production was also obtained 1. Despite the progress made in the development of technology in agriculture, however, the level of technology in the production of agricultural products in the country, especially in the production of aquatic and horticultural products is still very low. Also, most Level 3 technologies are imported. Therefore, planning is necessary to upgrade level 1 technologies to level 2 and level 2 technologies to level 3, as well as to enable the production of level 3 technological products in the country.

In the present century, the need to provide food security for the growing population of the country, the importance of maintaining human health and the environment, as well as the need to continuously increase the productivity of inputs, has made the use of new technologies in the production of agricultural products more necessary. One of the new technologies is Intelligent agriculture. This paper has tried to present the importance of remote sensing technology as one of the most important parts of intelligent agriculture, namely data collection and recording. Also, the types of platforms and sensors that have been introduced in remote sensing studies and their applications in agriculture, as well as the advantages and limitations of each method are described. In the present century, the need to provide food security for the growing population of the country, the importance of maintaining human health and the environment, as well as the need to continuously increase the productivity of inputs, has made the use of new technologies in the production of agricultural products more necessary. One of the new technologies is Intelligent agriculture. This paper has tried to present the importance of remote sensing technology as one of the most important parts of intelligent agriculture, namely data collection and recording. Also, the types of platforms and sensors that have been introduced in remote sensing studies and their applications in agriculture, as well as the advantages and limitations of each method are described.

 One of the most important challenges that agriculture has encountered with, is the overusing of nitrogen fertilizers. Excessive use of these fertilizers, not only proliferate the costs of production and harm human health but also expand environmental pollution. Improving the efficiency of the nitrogen fertilizers depends on monitoring the plant nitrogen status at the different stages of plant growth and applying a sufficient amount of fertilizer at the right time and right place. Considering the necessity for diminishing the harm of nitrogen fertilizer overuse on human health, research was conducted to identify nitrogen stress in the maize field as a new approach for optimization of the use of those fertilizers by means of a new, fast and non-destructive aerial multispectral remote sensing technology via UAVs. The experiment was carried out on a maize field in a randomized complete block design in 4 replications of urea fertilizer in 4 treatments including 0% (witness), 50% (critical range), 100% (adequate range), and 150% (maximum or toxic range). Urea fertilizer was utilized with irrigation water in two growth stages, including the 8-leaf stage (V8) and tasseling appearance stage (VT). Multispectral aerial imaging was accomplished in both maize growth stages. In order to ground Sampling, 10 specimens of maize plants were randomly selected from each treatment in those plant growth stages. At first, the amount of chlorophyll was measured for each sample, next, the amount of nitrogen was delineated by the Kjeldahl method. After taking the images, they were processed. The vegetation indices including NDVI, NRI, MTVI2, CI, GM, which were related to plant chlorophyll amount and nitrogen content, were calculated. Data analysis for nitrogen concentration stress indicators, with leaf chlorophyll content, considering their variable relationships, was performed by the fitting regression models. Based on our research, CI (R^2=0.88) and NRI (R^2=0.90) index were the most appropriate indicators for detecting nitrogen stress in the V8 and VT growth stage of maize, respectively. According to our results, aerial multi-spectral remote sensing is capable of detecting the variability and stress of nitrogen fertilizers in the maize field.

The selection of cropping patterns is one of the main factors in increasing agricultural productivity. Optimal allocation of land and determination of suitable crop production in each region to prevent unwarranted consumption of inputs and reduce the disaster of lack of food supply after scientific study of factors affecting cropping pattern and taking into account different indices. Therefore, the present study determines the factors affecting the cropping pattern and their prioritization. This research was conducted in Silakhor agricultural area in Dorud Lorestan. The research method used in this study was a library, skillful interviews, and inspection (collecting questionnaires from farmers). The variables affecting the cropping pattern were extracted using exploratory factor analysis and then Shannon entropy-based TOPSIS method was used to prioritize crop cultivation. Factor analysis results indicated that the variables affecting sustainable crop pattern selection in the region can be classified into six factors, mechanization-farming, soil and climate, macro-government management, production support, social and production margin. These factors explained 61.41% of the variance of the effective variables in selecting the cropping pattern. Also, in the four-criterion entropy method, Access to cash capital of cultivation needed (0.236), Crop water requirement (0.233), crop profit (0.098), and cultivation area more than 1 ha (0.039). They had the highest priority in choosing the pattern of cultivation. Finally, the results of TOPSIS Multi-Criteria Decision Making showed that autumn sugar beet (0.598), Wheat (0.589), Barley (0.558), Canola (0.556), autumn peas (0.515). ), Rice (0.499), Quinoa (0.471), and saffron (0.390), respectively, have the highest priority for being included in the region's cultivation pattern.

This paper presents the introduction of IoT and some of its applications in agriculture and also the technical challenges of developing IoT-based systems.The IoT depicts a technological world in which many objects are interconnected. An IoT-based object consists of main layers, including the perception layer, the network layer, and the application layer. The perception layer is used for data collection, the network layer for storing and analyzing information, and the application layer for sending a message to the operator and setting it up. Some of the technical challenges of IoT development are the limitations of the data gathering, saving data, networks, appropriate hardware for the agricultural area, and ensuring data integrity and security.The application of this technology in agriculture causes cost reduction, better management ,and increasing productivity. The IoT is developing rapidly. The high economic benefit of this technology is the main incentive for investors to invest in this field.

Food security along with sustainable agriculture development is one of the most important challenges of the present century. Food security, despite resource constraints, climate change and environmental problems, requires changing methods and approaches for producing agricultural products. Increasing productivity in the production of agricultural products requires the application of new methods and technologies. One of these technologies is intelligence in agriculture. Smart Agriculture is a good way to solve agricultural challenges and provide food security. Intelligent (Smart) agriculture provides the opportunity to farmers and operators to increase their productivity and manage better their production resources. Considering the importance of using intelligent technologies in the field of agriculture, this paper attempts to introduce this technology and present some of its application in agriculture. So, the beds for development of this technology are introduced. Also, in this article, the requirements for the proper development of this technology in the agricultural sector with regard to the current situation have been addressed.

Canola is a source of edible oil and its cultivation in Iran and the world is growing. Only few studies have been carried out by remote sensing for canola yield estimation,. In 2017-2018, in order to predict the canola yield by Landsat satellite, OLI sensor, three farms were evaluated. The satellite images were processed in five stages: before flowering, early flowering, peak of flowering, green and dry maturing, and some of vegetation indices were extracted based on the ratio of the bands. The pixel network of each farm was determined and the Real Time Kinematic Global Positioning System (RTKGPS) was used to increase the precision of pixels location in the farms. Sampling was done inside farms pixels during harvesting time and canola yield was measured. Totally, 28 pixels from three studied farms were used to develop and validate the predictive models. Simple and multivariate linear regression models were used to assess the relationship between canola yield and vegetation indices. The results showed that, on the basis of simple linear regression models, among the growth stages, the highest coefficient of determination (R2) in each of the vegetation indices belonged to one of the two stages: the peak of flowering and green maturing. The coefficient of determination in all vegetation indices was low in the before flowering stage (less than 10 percent) and relatively medium (24- 52 percent) in the early flowering and dry maturing stages. According to this model, the NDYI with 67 percent in the peak of flowering stage, and the RVI with 64 percent in the green maturing stage had the highest coefficient of determination compared to other vegetation indices. The stepwise multivariate linear regression models, with four visible and near infrared bands, resulted to the best yield predictive model in the peak of flowering stage, with 78 and 74 percent of coefficient of determination, for its implementation and validation, respectively.

Nowadays, the need for food security is one of the most important global issues. It needs more careful consideration, understanding, and planning in developing countries. Thus, the food security in developing countries such as in Iran requires new strategies and methods in food production and supply. One of the ways to increase production, quality and to reduce waste and losses and perform different operations timely and correctly is the use of new technologies and updated machines. In addition to these, one of the most key and updated domain is mechatronics engineering and intelligent systems. Therefore, taking the advantages of this new opportunity in the field of engineering to pass and change from the usual mechanization (purely mechanical) into biosystems can lead to the improvement of the production systems and accelerating the processes. Before this, an advanced knowledge and critical understanding of the essential requirements such as proper recognition, applications, and challenges, are needed to use and continue the application of emerging technologies or develop the new methods. Thus, this article aims to introduce modern technologies and understanding of these domains, the importance, application and challenges associated with mechatronics engineering in agro-industrial development and other linked areas.

Todays, the application of new methods for monitoring and online management of agricultural farms is necessary to increase quality and quantity of agricultural products. Remote sensing is one of the technologies that can be used to monitor agricultural farms and natural resources. This technology is capable of detection and prediction of farm changes by application of satellite and aerial images. Satellite images have some limitations for application in agriculture, namely, high cost, low revisit time and low spatial and spectral resolutions. Thus, recently design of low altitude remote sensing system is considered as a useful tool for ground observations with high spatial resolutions. This system is used to monitor and collect online images for agricultural farms of small spatial farms. So, by the importance of aerial imaging in agricultural farm management and limitations of satellite imagery, the main objective of this research is to develop an unmanned aerial remote sensing system for imaging agricultural farms with high spectral and spatial resolutions. The constructed unmanned aerial vehicle (UAV) is composed of aerial part and ground station. Aerial part is composed of carbon fiber body and arms, 8 brushless DC electric motors, 8 control speeds, control board, PID controller, AHRS system for roll, pitch and yaw angles measurements (with 3 gyroscopes, 3 accelerometers, 3 Magnetometers, one barometer), GPS, camera mount with 2 servo motor for compensation of camera vibration, 3-cell chargeable Li-Po battery (5000mA, 11.1 V) and voltage measurement unit. A multispectral camera (ADC-Micro, Tetracam Company) with 520-920 nm wavelengths and 3.2 Megapixel CMOS sensor in Green, Red and Near Infra Red bands (6-12 V, 2 Gb memory). Ground station is constructed for conduction and control of aerial part and, indeed, it is connected with operator to aerial part. Ground station is composed of 8 frequency radio control (2.4 GH frequencies), flight monitor software and control software. Total system weight is 2 kg with 78 cm length and width and 29 cm height. The main duty of ground station is following current position and coordination of the UAV, visiting flight parameters and adjustment of primary flight parameters. Sending online imagery of regain to the ground station is carried out by a CCD camera. To connect aerial and ground parts together, a telemetry system is used. To evaluate the performance of the system, collecting data is carried out in a wheat farm in the Mohammad Shahr, Karaj, Iran. Some factors such as flight endurance, flight maximum preload, maximum and minimum UAV speed, flight altitude, camera mount performance and spatial resolution are investigated. So, imagery are processed, NDVI and supervised classification map is extracted. To evaluate the results of classification, error matrix and overall accuracy is calculated. Based on the results, manuaribilty and stability of the UAV during the flight and also in takeoff and landing positions were satisfactory. The results of experiments showed that the average time endurance of the UAV with installation of GPS, CCD camera, Multispectral camera is 10 minutes. Thus, the results of UAV preload experiments indicated that the maximum preload of the developed UAV is 1 kg. Also, the maximum wind speed for the UAV flight based on the result of this research was 15km/hr. The performance of the system is decreased by increasing wind speed. The optimum UAV speed for collecting qualified imagery is 0-40 km/hr. In higher speed, the quality of imagery will be decreased. The obtained results from this study are: - Spatial resolution: based on the results of experiments in the height of 250 m by image dimension (2048*1536), the total covered area in imagery is 2.8 ha. So, time resolution based on the UAV speed and imagery saving file format is 2-5s. So, spatial resolution of imagery in the height of 10m was 3.6 mm/pixel and in the height of 250m the obtained image is 95mm/pixel. - NDVI map showed higher value in denser regains. - Supervised classification map (maximum likelihood): the results of experiments showed that the overall accuracy of classification map was 93.99% and Kappa coefficient obtained 0.9. Classification results showed that different classes such as soil, crop canopy, shadow and etc. were separated and recognized completely. High value of the overall accuracy and Kappa coefficient indicates that the UAV is capable of taking good imagery without nose which shows the satisfied performance of control system of the UAV and control system of camera mount in order to keep the UAV in the right situation and to fix camera in the desired position.