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

Reference evapotranspiration (ETo) is considered one of the important variables in hydrology and agricultural science and is a determining factor in water resources management. This study investigates a hybrid model of an artificial neural network with an artificial rabbit optimization algorithm (ANN-ARO) for daily modeling of ETo with limited meteorological parameters. It compares it with other hybrid methods, i.e. ANN with a particle optimization algorithm (ANN-PSO). ANN with genetic algorithm (ANN-GA) and five different data mining models. These models were evaluated using long-term daily climate data from 2000 to 2023 in two climates. The investigated stations included Birjand (with a desert climate) and Mashhad (with a cold semi-arid climate). The statistical comparison showed that considering all climatic parameters, the hybrid ANN-ARO model in Mashhad city with R2=0.9986 and MSE=0.0001 and in Birjand city with R2=0.9986 and MSE=0.0001 gave better estimates than other methods. In addition, the ANN_ARO optimization algorithm has the best estimation with "temperature" and "relative humidity" by considering the minimum meteorological parameter, and also by considering two and three input parameters, it performs better than other methods. In general, nature-inspired optimization algorithms are powerful tools to enhance the performance of ANN in ETo simulation. According to the results, the ANN-ARO model is highly recommended for estimating ETo in similar climate regions with limited climate data. This study proposes powerful models for accurate estimation of ETo with limited inputs in arid and semi-arid climates, which provide practical implications for the development of precision agriculture.

Today, using advanced technologies such as the global positioning system (GPS), agricultural drones, satellite mapping, remote sensors, and precision agriculture machinery provides farmers with a lot of big data during production. According to the reports, this can be considered a part of the digital economy in precision agriculture and be economically exploited. The analysis of this data cannot be processed by traditional processing systems due to its complexity. Given the size and complexity of big data, artificial intelligence can transform this data into valuable information through machine learning algorithms. This technology is being used to performance prediction algorithms, reducing agricultural inputs such as fertilizers and poisons, monitoring the growing conditions, pest management, breeding, molecular studies and finally value chain management. Developing programs using artificial intelligence technology will soon be able to manage the time of agricultural products entering the market, in addition to determining the planting time in order to increase productivity. The production of bio fertilizer from agricultural waste can be another achievement of the development of algorithms based on artificial intelligence to reduce the negative environmental effects and increase the economic productivity of the remaining waste from agricultural products. This study discusses the important applications of artificial intelligence in agriculture and its impact on Precision agriculture.

In recent decades, the deployment of technologies such as robotics, unmanned aerial vehicles, and satellite operations has become increasingly evident in all farming activities. The revolution known as smart agriculture, employed to enhance the efficiency of agricultural products, allows farmers to achieve higher yields while minimizing the use of fertilizers and other chemicals, thereby contributing to a reduction in greenhouse gas emissions and mitigating climate change. This paper investigates and analyzes the challenges and constraints of eco-friendly innovative technologies in the field of smart agriculture globally and in Iran. It also proposes indigenous solutions for the localization of eco-friendly innovative technologies within the country. The localized management of nutrients in domestic farms, despite their small size, is expected to provide relatively positive outcomes. Agriculture presents an excellent opportunity for the introduction of various types of robots. Countries such as China, Japan, and Australia have made significant strides in the development of automated tractors capable of performing various tasks on the farm. The use of drones in domestic farms has demonstrated an increase of approximately 30% in pesticide consumption efficiency. A national type of drone developed within the country has significant advantages over its foreign counterparts in terms of electronic engines, operational types and services, cameras and sensors, flight types, and monitoring functions. It is anticipated that the mentioned technologies, as part of smart agriculture, will significantly reduce the human labor intensity in farms to a much lower level than before.

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.

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.

Agricultural development is one of the most important priorities in the national development programs of developing countries. This cannot be achieved without applying and using new technologies in the agricultural sector through precision agriculture. Since the acceptance and application of this type of technology is facing challenges and obstacles; The aim of this quantitative study is to "investigate the factors influencing the adoption of UAV technology in two retrospective and prospective perspectives". The required information was collected in the form of questionnaires made by the researcher among the potato farmers of Kermanshah city. In order to analyze the data, the statistical method of logistic regression was used. The findings indicated that only three variables of perceived risks, the intention to use drones and the perceived need for technology were effective on the acceptance of drone technology, so that by reducing each of these variables, the possibility of increasing the adoption of drone technology among farmers. Potatoes work more. Therefore, holding promotional courses and granting financial facilities to potato farmers can be effective in facilitating the adoption of drone technology.

During the past decades, with the indiscriminate use of some chemical inputs conventional agriculture has caused serious environmental and human health problems. In response to these problems, precision agriculture, as one of the sustainable agriculture approaches, has received more attention in recent years. Mazandaran province, which is one of the important agricultural producers in the country, has an un-favorable situation in terms of the use of pesticides and chemical fertilizers; that is while, the efficient utilization of resource on a per-unit area basis is necessary due to limited land. In this regard, the aim of this research was to analyze the factors affecting the application of precision agriculture in Mazandaran province. This applied research was conducted qualitatively using the Interpretive Structural Modeling (ISM) method. This study applied a systematic review of internal and external research sources to identify the factors affecting the application of precision agriculture. Then, the required data were collected in the form of a questionnaire from the respondents. The statistical population of this research consisted of experts in the field of precision agriculture in Mazandaran province, 23 of whom were selected by a purposeful and judgmental sampling method. The analysis of previous research and sources showed that generally, 10 factors are effective in the application of precision agriculture. The results of the ISM analysis indicated that technical and professional, social, and individual factors are the most influential factors in the application of precision agriculture. Furthermore, economic factors, land condition, system of operation and management, and policy-making and macro planning factors were the other most influential factors. Paying special attention to these variables in the field of applying precision agriculture can help the growth and development of sustainable agriculture in Mazandaran province and increase productivity.

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.

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.

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).

Compared to other agricultural sciences, weed science is a new science, but it has been considered as an old tradition and method. Weeding was one of the first method in weed control, but with the over of time and the invention of agricultural tools, new methods for weed control were introduced and have been more or less used until. However, Today, challenges have been created in this science, the most important of which are the emergence of herbicide-resistant biotypes, reduction of the effectiveness of old herbicides, the lack of weed management methods and the reduction of the introduction of herbicides with new modes of action. In the future, according to the need, it is important to introduce natural compounds as a suitable option to discover a new site of action and produce bio-herbicides. Also, genetic engineering will be considered as another tool for adjusting the selective properties of herbicides and creating new management methods. Computer science and robotic, which include electronic eyes, sensors for distinguishing crops from weeds, and other tools related to engineering sciences, are also important and needed in weed management that are being developed and processed. Also, combining old methods with new tools can be considered as a suitable option for sustainable management of weeds.

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 precision agriculture has become an undeniable necessity in the agricultural sector today. Paying attention to this issue in student cooperatives is doubly important in terms of skills development and student education. The present article tries to examine the educational needs of the members of 4802 Student Cooperatives of Razi University of Agriculture and Natural Resources regarding precision agriculture. The study population was active student cooperative members in 1400 (N = 121), 98 of whom were studied using stratified sampling method with proportional assignment. Data collection tool was a researcher-made questionnaire whose validity and reliability were confirmed by experts and Cronbach's alpha. Data analysis using Burich model showed that students' knowledge about precision agriculture is at a low level (mean 1.80 and standard deviation 0.85). The results also showed that the subjects studied GPS global positioning system, technologies related to product performance maps, point-to-point measurement of the product, equipping existing machines with automatic control systems and technologies related to weed maps. They have stated as their most important educational need. The results of the regression model also showed that about 71% of the changes in the educational needs of cooperative members are explained by the variables: participation in training courses and access to information resources. Therefore, it is necessary to help strengthen this skill in agricultural students through strategies such as: holding courses and workshops, forming telegram and WhatsApp groups, inviting specialists, and so on..