جستجوی مقالات مرتبط با کلیدواژه « کشاورزی دقیق » در نشریات گروه « مکانیزاسیون کشاورزی »

Measuring the seed and fertilizer mass flow rate in drill delivery tubes is an essential tool for real-time monitoring of the application rates. In the present study, a non-contact capacitance measuring system was implemented to monitor the granular discharge rate in seed and fertilizer applicators. The set-up consisted of a fluted-roller feeding mechanism and the capacitance measuring system incorporated in the discharge tube. The set-up was mounted on a custom-built vibratory stand to simulate the field condition during the measurement. Then, triple super phosphate, wheat, and alfalfa granular discharge rates were measured over 30 s intervals in both static and dynamic conditions from 3.42±1.22 to 19.3±0.95 g/s in 5 rates, from 2.55±0.23 to 19.02±0.86 g/s in 8 rates, and from 0.42±0.02 to 1.6±0.08 g/s in 7 rates, respectively. The results revealed that the mass flow was effectively correlated with sensor signals for all examined materials. However, the capacitance sensor failed to measure the sudden change in mass flow rate synchronously with the digital scale. This phenomenon was predictable because it is attributed to the feature of a digital balance, which is always accompanied by an initial lag. Also, the results revealed that the vibrating stand had no effect on the sensor accuracy.

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

Considering that wheat and barley are important and strategic products in Iran, the Sunn pest is one of the most important pests of these products. Every year, the country's plant protection organization monitors this dangerous pest so that an order to fight it can be issued at the right time. Farm monitoring is a very difficult, time-consuming, and destructive task; it also requires a large number of specialized human resources. One of the goals of this research is to create a non-destructive method for such an operation in the identification of Sunn pests. In this research, the counting of Sunn pests in cereal farms was done by two methods using aerial image processing by recording 954 images and framing method and eye counting in the farm by an expert in two farms with different numbers of this pest. The number of pests in each image was obtained after processing the images using the Python programming language in the PyCharm learning library. The statistical study results showed no significant difference at the 1% probability level between the average data of the two methods. This method can be used confidently to monitor the Sunn pest in cereal farms. In this research, by using UAV image processing, a fast, accurate, and non-destructive method was proposed and evaluated for counting the pests and issuing spraying orders in cereal farms.

The precision agriculture is one of the best ways to carry out mechanized operations to conserve natural and economic resources. By achieving to this important issue, utilizing a variety of sensors, various hydraulic, electronic and mechanical technologies in agricultural machinery. Control and planning to apply the variable rate application (VRA) 1, especially by solving the problem of required power supply will be provided, on the other hand, the expansion of supplying electricity energy due to easier transmission, high efficiency and intelligent planning capabilities in various industries, especially automotive industry, during a few recent decades show the necessity of its entrance to tractors and agricultural machineries, but the type and amount of electrical power avaliable in tractors is the main limiting factor. In this research, with an overview of the methods of supplying electricity to agricultural tools agricultural equipment, a new method was tested to supply the vibration of an electric vibrating subsoiler. which is based on the design of a DC inverter is with a power input from the tractor battery, which according to it, a pulse width modulation and a potentiometer are responsible for applying the appropriate speed for electromotor and variable vibration frequency, and the subsoiler vibration system frequencies (zero frequency: F1 frequency +18: F2, -18: F3, +36: F4 and -36: F5) was provided with required power up to 1 kW. The results of tillage indicators of a rotary vibrating subsoiler with electricity energy showed that the effect of vibration frequency levels on the draft force and the reduction of the clod mean weight diameter has significant at the level of 5%, so electrifying the agricultural equipment can improve their performance and power supply with inverter design in addition to simplicity and cost reduction, the power supply feature independent of tractor engine speed is very important and efficient for precision agriculture.

It is difficult to accurately measure the flow rate of seeds and fertilizers in grain drills, because the granules move in a continuous dense state. The purpose of this research was to monitor the mass flow of granular materials in the fall tube of the drill using a piezoelectric sensor. The laboratory system consisted of a repository box, a fluted roller metering device, a fall tube, and a piezoelectric sensor. To simulate the drill movement in the field, a vibrating stand was designed to oscillate the set-up in two perpendicular directions. The maximum amplitude stand was 8.99 cm according to the peaks and depressions available in a field after plowing. The sensor was evaluated in two static and dynamic modes for wheat seed, alfalfa seed, and triple superphosphate fertilizer with the usual drilling rate. The results showed the output signal of the sensor was proportional to all different mass flow rates in both static and dynamic states. The correlation coefficient was 0.95, 0.99, and 0.98 in static mode and 0.93, 0.86, and 0.98 in dynamic mode for wheat, alfalfa seed, and triple superphosphate fertilizer, respectively. In addition, the piezoelectric sensor could instantaneously monitor the sudden changes in the mass flow according to the reading of the digital scale. The results showed that the developed sensor can be used to monitor the mass flow rate of seeds and fertilizers in drills for calculating the drilling rate in real time.

Mankind is facing great problems and challenges in agriculture to provide food: the limitation of land at the same time as the population grows up, the increasing destructive force of agriculture on the world's ecosystems, climate change that has turned agriculture into a risky activity, chemical fertilizers and agricultural toxins that are polluting the crop, water and soil have caused serious risks to human health and the environment. Vertical smart agriculture approach is a human solution to solve existing challenges and future crises. This type of agriculture focuses on the use of data and automation in managing farm activities to save water and soil resources and time while increasing farm productivity. Cultivation throughout the year, protection of crops against atmospheric conditions by creating a safe environment, increasing nutritional value and production of organic products due to the lack of use of poisons and pesticides, remote monitoring and reduction of operating costs are the other its advantages. Smart agriculture has presented the solution of vertical (layered) agriculture for the serious problem of land (space) limitation especially in our country which is facing drought and reduction of water resources.

Site-specific agriculture is a subset of precision agriculture that aims to increase productivity as well as the quantity and quality of crops based on measuring spatial variation, soil and plant characteristics, and consumption of inputs as variables in time and space. The first step to fulfill this goal is to locate the agricultural machine in the relevant place so that the desired operation can be performed accordingly. In the current research, a ground positioning device with sensors for measuring the steering angle and the number of rotations of the moving wheel of the tractor has been developed. The system was field-tested with local positioning without needing external servers, and the data were compared with the output values of a GNSS receiver. The experiments were performed on asphalt, plowed and uncultivated fields as well as in sunny and cloudy conditions. The statistical design was based on randomized completeblocks as a factorial test in 2 × 2 × 3 with four replications. According to the obtained results, the X and Y axes data of the GNSS receiver and self-tracking device showed a significant difference at the 1% and 5% levels, respectively. Changes in environmental factors, including sunny/cloudy and the ground surface conditions on both the X and Y axes, did notsignificantly affect positioning by the GNSS receiver and the self-tracker. The accuracy of the tracker system in the X direction was found to be 25 cm and for the GNSS receiver 250 cm, while in the Y direction, this was 50 cm and 140 cm for the GNSS receiver. As a result, the tracker system was more accurate on both the X and Y axes. In addition, path deviation distances obtained with the GNSS receiver and the self-tracking device were significantly different at the 1% probability level. The accuracy of determining the distance in the selftracking system was 60 cm, while with the GNSS receiver, it was 400 cm. In general, the performance of the self-tracking system in finding the optimal trajectory was more accurate than the conventional GNSS receiver (without DC correction).

Sugarcane is a strategic agricultural product and increasing productivity and self-sufficiency in its production is of special importance. The most important product of sugarcane is sugar. Various factors like climatic and management conditions affect the yield of sugarcane and recoverable sugar. Crop yield forecasting is one of the most important topics in precision agriculture, which is used to estimate yield, match product supply with demand and manage product to increase productivity. The purpose of this study is to predict and model the factors affecting sugar extracted from sugarcane (recoverable sugar) in the farms of Amir-Kabir sugarcane agro-industry Company of Khuzestan province using machine learning methods.

To conduct this study, data from the agro-industrial company Amir-Kabir in the province of Khuzestan from 2010 to 2017 were used. This data has 3223 records which include four sets of data: climate, soil, crop and farm management. This data includes continuous and discrete variables. Discrete variables include production management, soil type, farm, variety, age (cane class), the month of harvest and times irrigation. Continuous variables include area, chemical fertilizer consumption, water consumption per hectare, total water consumption, drain, crop season duration, yield (cane yield) soil EC, purity, time interval drying off to crop harvest, precipitation, min and max temperature, min and max relative humidity, wind speed and evaporation. The recoverable sugar variable is considered as the target variable and is divided into two classes, values greater than or equal to 9 are in the optimal class and less than 9 are in the undesirable class. The other variables are considered as predictor variables. For modeling using the Holdout method the data were randomly divided into two independent sets, a training set and a test set. 70% of the data which includes 2256 records were used for training and 30% of the data which includes 967 records were used for testing. The modeling of this study was performed with the Python programming language version 3.8.6 in the Jupyter notebook environment. Random Forest, Adaboost, XGBoost and SVM (support vector machine) algorithms were used for modeling.

To evaluate the models, metrics of accuracy, precision, recall, f1 score and k-fold cross validation were used. The XGBoost model with 94.8% accuracy on the training set and the Adaboost model with 92.4% accuracy on the test set, are the best models. Based on precision and recall metrics Adaboost model with 87% precision and SVM model with 87% recall have better performance than the other models. Based on Repeated 10-fold stratified cross validation using two repeats the SVM model with 92.3% accuracy is the best model. The variables of purity, time interval drying off to crop harvest and crop season duration are the most important variables in predicting the recoverable sugar.

In this study a new approach based on machine learning methods for predicting recoverable sugar from sugarcane was presented. The most important innovation of this study is the simultaneous consideration of management and climatic factors, along with other factors such as soil and crop characteristics for modeling and classification the recoverable sugar percentage from sugarcane. The results show that the performance of all models is acceptable and machine learning methods and ensemble learning algorithms can be used to predict crop yield. The results of this study and the analysis of the rules obtained from the set of decision trees made in the random forest model can be used for managers of different agro-industries in determining appropriate strategies and preparing the conditions to achieve optimal production.For future research as well as policy making and decision making Amir-Kabir sugarcane agro-industry Company the following suggestions are offered: more samples can be used to obtain more reliable results. Also can be used Deep learning methods, time series analysis and image processing. Use of IOT equipment to collect and real-time processing data on Amir-Kabir sugarcane agro-industry farms.

Considering the growing population of the world, it can be said that food security is one of the most important challenges of this century. Due to the limitation of natural resources and inputs, and problems such as climate change, environmental pollution, and increasing migration of villagers to cities, it is difficult to increase the quantity and quality of agricultural products. In such a situation, ensuring food security depends on changing production methods and approaches. New solutions and methods should be sought for production. Increasing productivity in production means increasing the application of knowledge and technology in agricultural production systems. Therefore, new technologies should be used to increase productivity. In other words, the future of sustainable agricultural production depends on the application of new technologies. Despite the consideration of importance of Information and communication technologies in the agricultural fields, new terms such as smart agriculture, e-commerce and precision agriculture are discussed in this article.

In the present study, an electro-hydraulic system for repositioning the soil shield of a rotary tiller was designed, developed, and evaluated to make it automated for performing variable rate tilling operation. The reposition of the soil shield was studied in the opening and closing stages from 0° to 80° and vice versa, with a 10° and 20° increment under laboratory and field conditions, respectively. Under laboratory conditions, the speed average of soil shield reposition in the opening and closing stages were 102.0±9.2 and 79.0±8.7 deg/s, with the time average in opening and closing stages of 0.11±0.02 and 0.15±0.01 s, respectively. The correlation coefficients between the requested position for the soil shield and the position created by the hydraulic actuator under laboratory conditions in both opening and closing states were R2 = 0.99 (RMSE = 0.7 deg). Under field conditions, the speed average of the soil shield reposition at three travel speeds of 2.6, 3.5, and 4.8 km/h in the opening stage were 97.7, 99.1, and 99.1 deg/s and in the closing stage were 77.3, 78.3, and 78.7 deg/s, respectively. The correlation between the requested position for the soil shield and the position created by the hydraulic actuator in both opening and closing states were R2 = 0.99 (RMSE = 1.2 deg) and R2=0.99 (RMES=1.3 deg), respectively. The soil shield repositioning system can be performed in less than 0.3 s in-field, making it an effective and practical tool for performing variable rate tillage.

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

The use of optical sensors in assessing crop health, plant fertilizer demand, and yield prediction is the new method in the agricultural sector. Grennseeker optical sensor is one of the latest tools that has recently gotten much attention from worldwide farmers and researchers. Detailed examination of the variables mentioned above and measuring canopy coverage can improve production management and minimize input consumption. Therefore, this study aimed to investigate the GS sensor's ability to assess the nutritional status and surface area of the canopy and predict the performance of the garlic crop. In this research, five N-fertilizer treatments of N0, N50, N100, N150, and N200 were applied to garlic in a randomized complete block design with three replications. The image processing results in Labview software showed that the best separation performance for the elimination of background from the canopy could be achieved with the function of G-R. The results revealed that the best correlation coefficient between NDVI and LAI was 0.69, but no significant relationship was found between N and NDVI. Finally, it can be concluded that the GS could not estimate the N level of garlic. However, it could perform well in estimating tuber yield and canopy performance  satisfactorily.

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.

Precision agriculture is rapidly growing worldwide, which is mainly based on positioning. While with the evolution of technology the accuracy of GPS data is continuously improved, the use of these sensors in the less developed countries is still challenging. The direct price link with the accuracy of the GPSs has led to attempts to use low-cost GPSs. The use of low-cost GPSs comes with various sources of error. In this study, a new method has been proposed to overcome the challenge of improving the accuracy of positioning while reducing equipment prices. The proposed method is based on the nature of the GPS error (random noise and bias) and is divided into two parts. First, the random noise was eliminated by employing the simple classic filter. In this regard, a comparison was made between the Kalman filter, Low-pass filter and the Moving Average filter. The settings of the filters were made in such a way that in addition to reducing the power of random error, it reaches the desired error range in the shortest possible time. The results showed that the Kalman filter was more efficient than the other filters. Bias removal is not possible except by using a DGPS receiver that can receive corrections sent from base stations. To solve this problem, a base station was designed that could send the corrections needed by low-cost receivers. The proposed low-cost differential positioning method leads to a 89% reduction in the horizontal error index, which is the accuracy obtained is suitable for various operations in precision agriculture. Second, the bias was removed by inspiring from DGPS technique, via providing a base station, incorporating three low-cost GPSs. in noise reduction and the proposed low-cost differential positioning at best resulted in 89% reduction in the error index. Such an enhanced accuracy can be used for many applications in precision agriculture.

According to reports on trends in the agricultural industry, demand for more precise and affordable machinery is rising and precision farming methods used by farmers are expanding. Damping vibration of the boom sprayer is one of the challenges for researchers when crossing the surface roughness. The lack of uniformity of the nozzle spray pattern and the reduction of the precision and quality is the result of an uncontrolled vibration of the boom sprayer. So far, many efforts have been made to decrease the vibration of the long sprayer boom. Using active and passive methods, researchers have achieved great success in reducing the spray boom vibration. Many of these methods are based on the use of external force as a controlling force. Expensive equipment, the use of tractor hydraulic power, and high energy costs are the main disadvantages of these methods. In the present work, a new system called "variable support" was designed and built. The boom rests on a round bar at its midpoint; as the boom tends to oscillate, a minute amount of rotation of the bar activated by a servomotor in an appropriate direction, alters the position of the resting point of the boom on the supporting bar, bringing the boom back to its initial balanced position as a result.

To carry out experiments, the universal tractor U650 and a mounted tractor sprayer model tms500 with 8 m boom and a 500-liter tank, was used in this research by making changes to the design of the simple tractor sprayer a new active sprayer designed and built with intelligent online balancing system that in addition to balancing the boom angle, it reduced the fluctuations that occur during work. Electronic control was used to control the position of the boom. The microcontroller programming codes were developed and uploaded in the microprocessor to execute instantaneous commands to the mounted activator due to the need for boom positional data for analyzing experiments, a data logger was also designed in conjunction with the controller circuit and the algorithm was loaded after coding with C++. To compare the new sprayer with the conventional, a field test was conducted. Tests were carried out according to manufacturer's instructions at three-speed levels of 3 (low), 5 (medium), and 8 km h-1 (high), with three bumps heights of 10, 15, and 20 cm with three replications. For the acquisition of vertical acceleration as well as axial rotation data in the conventional sprayer, a data logger with an accelerometer and gyroscope was used. The data logging rate and the accuracy of the accelerometer and gyroscope measurement were set to 50 Hz, 0.1 m s-2 and 0.1°, respectively.

To compare the behavior of the active and the conventional spray booms in terms of vibrations, the univariate analysis was used. The results showed that there was a significant difference between the performances of two sprayers at 5% probability level with the sig. number of 0.000.To compare the behavior of the active and the conventional spray booms in terms of axial rotation, also the Univariate analysis was used. The results showed that there was significant difference between the performances of two sprayers at 5% probability level with the sig. number of 0.000. Also, comparing the marginal mean values of the data obtained from the data logger showed that the active boom compared with the conventional boom had less axial rotation and less vertical acceleration.

Results of experiments showed that the new sprayer with variable support system, relative to the conventional sprayer in terms of controlling boom acceleration and the angular balance of the boom with a significant difference had relative superiority and can be considered as an appropriate alternative to increase the accuracy of spraying, although more research on large scale booms are necessary before coming to final conclusion.

In recent years, the drying blossom of date palm caused extensive damage to a considerable part of date palm orchards of Bam and Baravat, south of Iran. Drying blossom is responsible for a large amount of product loss and has been expanding in recent years. In this study, the technology of GIS was used as a quick and easy solution for monitoring of the drying blossom of date palm. It was carried out in three stages: collecting field information from damaged orchards,  feeding GIS with all collected data, and extracting  required information and the maps from the system which showed the severity and distribution of drying blossom in Bam and the suburb by three methods nearest neighbor, inverse distance weighted (IDW), and kriging. Results, upon data normalization, and the trend analysis showed that the severity of the damage in the southern coastal orchards was much higher than that in central and northern regions; the finding led us to focus on certain geographical coordinates (58 degrees and 20 minutes east longitude and 29 degrees and 4 minutes northern latitude to 58 degrees and and 26 minutes east longitude and 29 degrees and 2 minutes north latitude). It has been found  that the rate of drying blossom of date palm in 57.2% of the total damaged orchards were between 20-30%, and in 5.9% of the damaged gardens higher than 40%. Interpreting the results of the distribution of drying blossom on maps showed a relation between the severity of the disorder and the elevation of the area. It could be seen that the palm trees grown at altitudes above 1000 m suffered less damage; in other words, the higher the altitude, the less severity of drying blossom. The pictures received from Sentinel satellite showed that 56.2% or 2049 ha out of 3647 ha total area of palm orchards of Bam and Baravat was affected by drying blossom.

Precision Agriculture by managing in-farm variables and acquiring soil and product variable properties, mapping, analyzing variables, and accepting appropriate management practices lead to optimal use of inputs and increase performance with minimizing environmental effects. Therefore, in order to implement precision Agriculture, the first step is sufficient knowledge about the quantity of the yield. so, in this research, the pistachio yield monitoring system was designed and constructed and the machine performance was investigated in the laboratory tests. in this study the number of Connecting chains and height of the reservoir were selected as Variable factors. The number of chains at 3 levels (2, 3 and 4) and reservoir height at 3 levels (10, 20 and 30 cm) were investigated. The results showed that optimum values for the weighing of the product in the reservoir were not significant for the number of chains, so, in order to achieve better equilibrium in this study, four chains were used. Also, with the 20 cm height of the reservoir, the best results were obtained. The highest error value was 3.6%. After the laboratory tests, the system was transferred to the pistachio orchard and weight data were recorded associated with the location of each pistachio tree (by GPS). Among the methods of IDW interpolation, ordinary kriging, simple kriging, and general kriging, the ordinary Kriging method was introduced as the best method due to lower RMSE (1160.89). At the end, the yield map of the product was created by the usual Kriging method in the GIS software, which can be used along with other data as a prescriptive map for better management of the orchards. Among the methods of IDW interpolation, ordinary kriging, simple kriging, and general kriging, the ordinary Kriging method was introduced as the best method due to lower RMSE (1160.89). At the end, the yield map of the product was created by the usual Kriging method in the GIS software, which can be used along with other data as a prescriptive map for better management of the orchards.

Fire Blight (FB) is the most destructive bacterial disease of pome fruit trees around the world. In recent years, spectrometry has been shown to be an accurate and real-time sensing technology for plant disease detection. So, the main objective of this research is early detecting FB of pear trees by using Visible-Near-infrared spectrometry. To get this goal, the reflectance spectra of healthy leaves (ND), non-symptomatic (NS), and symptomatic diseased leaves (SY) were captured in the visible–NIR spectral regions. In order to keep the important information of spectra and reduce the dimension of data, three linear and non-linear manifold-based learning techniques were applied such as, Principal Component Analysis (PCA), Sammon mapping and Multilayer auto-encoder (MAE). The output of manifold-based learning techniques was used as an input of the SIMCA (Soft independent modeling by class analogy) classification model to discriminate NS and ND leaves. Based on the results, the best classification accuracy obtained by using PCA on the 1st derivative spectra, with accuracy of 95.8%, 89.3%, and 91.6% for ND, NS, and SY samples, respectively. These results support the capability of manifold-based learning techniques for early detection of FB via spectrometry method.

IntroductionThe color of soil depends on its composition and this feature is easily available and rather stable. Fast and accurate determination of soil organic matter distribution in the agricultural fields is required, especially in precision farming. General laboratory methods for determining the soil organic carbon are expensive, time-consuming with many repetitions, and high consumption of chemicals. Soil scientists use the Munsell soil color diagrams to define the soil color. Due to the nature of Munsell color diagrams; this system is less suitable for recognizing exact color of the soil because of weak relationship and limited number of chips. Fast methods like image processing, colorimetric and spectroscopy provide a description of most physical characteristics of the soil color. Some of the advantages of using digital cameras was used in this study, are simple sampling of screened soil, being free from chemicals and toxic materials and being fast, inexpensive and precise.
Materials and MethodsIn this research, 80 A-horizon (0-10 cm) soil samples were collected from various agricultural soils in west Azerbaijan, in the North West of Iran. Soil texture of these fields was loam clay and clay. The amount of organic carbon in samples was determined. The camera was installed at the distance of 0.5 m from the Petri dish on the lighting compartment. Captured images with the digital camera were saved in RGB color space. Processing operations were done by MATLAB 2012 software. The features extracted from the color images are used to model the soil organic carbon including the color features in different spaces. Four-color spaces including RGB, HSI, LAB and LUV were studied to find the relation between the color and the soil organic carbon.
Results and DiscussionThe correlation of R component in the RGB model shows a strong single-parameter relation with the organic carbon as R2=0.83. This good relationship can be due to the compound information of the red color component on both brightness and chromaticity dimension. The results also show that the organic carbon has a relatively strong correlation with the light parameters, intensity and lightness in the HSI, Lab and LUV color spaces respectively. It also has a weak correlation with other parameters, since they cannot have a proper linear correlation with organic carbon due to their structural nature. Results show that the highest correlation is obtained when the R and G components participate in modeling and the component B is omitted. One explanation of this high correlation could be the high sensitivity of component B to the intensity and the angle of light. Even a small change in light changes this component. Thus, in order to reduce the effect of this component, it is better to omit it from the models and make models independent of it. In next section, 51 data were used to train neural network, 14 data were used to test the network and 12 data for network validating. The amount of soil organic carbon was output of the neural networks that was estimated after training using the color component values of each segment. To assess the accuracy of the network, estimated values and actual values of each sample were plotted in a graph. The minimum MSE values were 7.28e-6 with 16 neurons, 3.77e-6 with 14 neurons, 4.8e-3 with 10 neurons and 3.77e-6 with 12 neurons for RGB, HSI, Lab and LUV color spaces respectively.
ConclusionsThe availability of digital cameras, possibility to use it in different situations, being inexpensive and providing many samples are the advantages of this method to estimate the soil organic carbon amount. Therefore, digital photography can be used as an analytical method to evaluate the soil fertility. It also requires a low cost of sample testing, and can provide a good possibility of time and place classification for studying a vast area. However to develop more reliable models, more effort is needed, such as collecting more soil samples of different areas that include a wide range of soil features.

IntroductionCurrent study tries to find a new simple and practical real-time technique to estimate forage crop nutritional quality indices at field conditions. Estimating these indices help producers to have field quality variation layer to reach the goals of Precision Agriculture. Previous studies have shown that standardized shear characteristics of crop stem would be a good indicator for some nutritional quality indices. In previous studies, laboratory tests were conducted at controlled conditions of crop moisture content, stem diameter and employing standard shear test procedure.
Materials and MethodsIn order to simulate field conditions, a two-stage study was conducted in Iran and United States. In the first stage fresh and naturally sun dried alfalfa stems were used in evaluating four levels of crop growth stage and eight loading conditions (four loading rates and two stem conditions). In order to evaluate the effectiveness of shear technique with respect to the conventional harvest method in Iran, shear tests were conducted using fixed and moving knives of a standard square hay baler (John Deere-348). Special fixtures were constructed to attach these knives to a universal testing machine (SANTAM, STM-20). Since evaluation of the suggested method with regard to other quality related factor indices such as different varieties and seeding rates, was not practically feasible in Iran in the second stage of this research, five different varieties and three seeding rates were tested in United States. In this part of the study, shear tests were conducted using modified Varner-Bratzler shear test with universal testing machine (TESTRESOURCES-311). Based on the results of loading rate and stem condition in the first stage, shear tests were carried out using loading rate of 500 mm/min and multiple stem condition. In both stages Specific Shear Energy (shear energy per stem diameter, J mm-1) were calculated using trapezoidal method. In order to compare the shear energy results with crude fiber nutritional quality indices such as Acid Detergent Fiber (ADF), Neutral Detergent Fiber (NDF) and Relative Feed Value (RFV), all alfalfa samples were analyzed using (Association of Official Agricultural Chemists) AOAC standard analytical laboratory methods. Statistical analyses were consisted of ANOVA mean comparison test at each level of factors and regression analysis to find the correlation between specific shear energy and nutritional quality indices.
Results and DiscussionsResults of ANOVA analysis and mean comparisons showed a significant difference in specific shear energy at different levels of loading rates. The higher loading rates showed lower energy which was related to lower ability of knives to cut alfalfa stem thoroughly and shredding the stems at lower speed levels. Significant differences were found in different levels of annual growing cycle, harvest time and seeding rates. Alfalfa stem in fifth harvest year showed the highest shear energy due to higher lignification in plant stems. In the first year, harvested alfalfa stem did not have the lowest shear energy which might be due to existence of weeds in first year field. Results showed higher values of shear energy in fifth harvest of the season in comparison with the third harvest which was acceptable because of differences in plant received Degree Day in these harvest times. The lowest seeding rate (5 kg h-1) showed the highest shear energy respect to the other seeding rates. The reason for this significant difference could be due to lower competition to receive water and nutritions, also lower plant density helps the canopy to receive more sun light which causes higher lignification. Comparing the shear energy means in different varieties didn’t show significant differences which can be explained because of varieties adoptability to the region specific weather condition. The regression analysis showed good correlations between specific shear energy and crude fiber nutritional indices (ADF, NDF and RFV). The negative trends which were found in regression analyses were also reported in similar studies.
ConclusionsTwo stage laboratory tests were conducted in order to evaluate the effect of alfalfa nutritional feed quality indices related factors on unitized shear energy. Results showed a significant difference of standardized shear energy mean at different levels of harvest time, annual growing cycle and seeding rates. The specific shear energy was not significantly different in different varieties because of varieties environmental adoptability. The unitized shear energy showed a good correlation with crude fiber related indices with similar trends in both stages of research and good agreements with previous studies.