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

About 30% of the annual losses of agricultural products are caused by pests, diseases, and weeds. Spraying is currently the most common method of their control. At present, various manual and tractor-mounted sprayers are used for spraying. Manual spraying has very low work efficiency and is damaging as the spray might be applied irregularly and consumed by the labor or the product at poisonous levels. Tractor-mounted sprayers are more efficient than manual sprayers and require less labor. However, their use is associated with issues such as compacting the soil or crushing the product. In recent years, Unmanned Aerial Vehicle (UAV) sprayers have been used to spray farms and orchards. UAV spraying can increase the spraying efficiency by more than 60% and reduce the volume of spray used by 20-30%. Based on the capabilities of the UAV sprayer and the limitations of other current spraying methods, the purpose of this research is to evaluate the performance of the UAV sprayer in controlling Brevicoryne brassicae (L.) and compare the results with a turbo liner sprayer.

In the present research, the UAV sprayer is studied as a new method of spraying to fight Brevicoryne brassicae (L.). The results were technically and economically evaluated and compared with the control group and that of the turbo liner sprayer (the conventional method of spraying canola in Iran). The experiment was triplicated with a completely randomized design and three treatments of UAV sprayer, turbo liner sprayer, and control (no spraying). Field tests were conducted on the canola crop at the stemming stage where at least 20% of the plants were infected. The measured parameters included drift, spraying quality, field capacity, field efficiency, energy consumption, and spraying efficiency.

Based on the results, the spray volume consumed by UAV and turbo liner sprayers was equal to 11.1 and 187.6 liters per hectare, respectively. The particle drift in spraying with UAV sprayer and turbo liner sprayer were 53.3% and 80%, respectively. Moreover, the quality coefficient of UAV and turbo liner sprayers were 1.15 and 1.21, respectively. Therefore, the farm efficiency of the UAV sprayer and turbo liner sprayer was equal to 51.4% and 32.3%, respectively. Based on the results of the analysis of variance, immediately after spraying, there was no statistically significant difference between the average density of pests of the three treatments. However, three, seven, and 14 days after spraying, there was a significant difference between the control treatment and the spraying treatments. The density of pests in the plots sprayed with UAV and turbo liner sprayers was lowered to less than 100 pests per stem, whereas in the control treatment, the density varied between 250-700 pests per stem. A comparison of the average efficiency of the UAV sprayer and turbo liner sprayer with the t-test showed that both sprayers had managed to control the population of pests and 14 days after the spraying, the efficiency of the UAV sprayer was higher than that of the turbo liner sprayer.

The spray volume consumed by the turbo liner sprayer was 17 times the UAV sprayer. The spray drift was about 34% more in spraying with the turbo liner sprayer than the UAV sprayer. The field efficiency of the UAV sprayer was 59.1% more than the turbo liner sprayer. The energy consumption per hectare of the turbo liner sprayer was 7 times the energy consumption of the UAV sprayer. UAV sprayer’s efficiency reached 92.7 % 14 days after spraying.UAV sprayer is recommended for controlling Brevicoryne brassicae (L.) due to its high efficiency, low drift, low spray volume and energy consumption, and superior spraying quality. To improve the performance of the UAV sprayer for controlling Brevicoryne brassicae (L.), a flight height of 1-1.5 meters from the top of the crop, a flight speed of less than 7 m s-1, and a maximum spraying speed of 4 m s-1 are recommended. Additionally, it is possible to prevent the spread of the pest in the stemming stage by spraying the field in an earlier stage.

Accurate detection of apple leaf diseases is essential to prevent the reduction in both the quantity and quality of crop yield. With advancements in deep learning methods, the diagnosis of these diseases is improving, but data limitations remain a significant barrier. This research evaluates pretrained deep learning models with precise settings and demonstrates that high accuracy in disease detection is possible even with limited data. The selected models perform better than conventional methods and introduce transfer learning as an effective strategy to combat limited data and the diversity of diseases. These models aid farmers and horticulture specialists in identifying and managing apple leaf diseases more efficiently and rapidly. Additionally, these models are not only beneficial for farmers but also for agricultural consultants, students of agricultural sciences, and researchers in this field. Moreover, these methods are adaptable to other diseases and plants, promising advancements in plant disease detection systems in the future. The present study has the potential to revolutionize horticultural processes through optimization and automation, leading to a transformation in plant disease management.

The agricultural industry must pay attention to the issues of population increase, climate change and natural resource destruction in a way that is responsive to food and environmental needs. Paying attention to the Internet of Things can be one of them. Internet of things means a set of objects that are connected through the network. These objects can be sensors monitoring environmental factors such as temperature, humidity, light, soil moisture, soil acidity and carbon dioxide concentration, or types of agricultural equipment. In this article, the types of sensors and their capacity to intelligentize different aspects of agriculture (such as irrigation, nutrition, fertilization, disease diagnosis, monitoring and climate control) according to the facilities and capacities of the Internet of Things are introduced, so that they can be used to increase agricultural productivity, preserving resources and reduce human errors.

Rice is one of the most important main food sources in Iran and the world. The correct identification of the type of pest in the early stages of preventive action has a significant role in reducing the damage to the crop. Traditional methods are not only time-consuming but also provide inaccurate results, As a result, precision agriculture and its associated technology systems have emerged. Precision agriculture utilizes information technology such as GPS, GIS, remote sensing, and machine learning to implement agricultural inter-farm technical measures to achieve better marginal benefits for the economy and environment. Machine learning is a division of artificial intelligence that can automatically progress based on experience gained. Deep learning is a subfield of machine learning that models the concepts of using deep neural networks with several high-level abstract layers. This capability has led to careful consideration in agricultural management. The diagnosis of disease and predicting the time of destruction, with a focus on artificial intelligence, has been the subject of much research in precision agriculture. This article presents, in the first step, a trained model of the Chilo suppressalis pest using data received from the smartphone, validated with the opinion of experts. In the second step, we introduce the developed system based on the smartphone. By using this system, farmers can share their pest images through the Internet and learn about the type of pest on their farm, and finally, take the necessary measures to combat it. This operation is done quickly and efficiently using the developed artificial intelligence. In the continuation of the article, the second part introduces the materials and methods, and the third part presents the results. The fourth section also discusses and concludes the research.

Chilo suppressalis is one of the most important pests of rice in temperate and subtropical regions of Asia. The conventional approach employed by villagers to gather the Chilo suppressalis pest entails setting up a light source above a pan filled with water infused with a pesticide. At sunset, these insects are attracted to the light and fall into the water in the pan. This method is known as optical trapping. After catching the pest using optical traps, they are collected from the water surface, and their photo is taken with a mobile phone based on the location of the optical trap.The proposed method in this research consists of three main steps. Firstly, the farmer utilizes the software provided by the extended version known as Smart Farm. The farmer captures an image of the Chilo suppressalis pest and sends it along with its location to the system. The Smart Farm software program carries out image processing and pest range detection operations. The user then verifies the accuracy of the pest detection. In the second step, the images sent by the farmer are processed by the pre-trained model within the system. The model analyzes the images and determines the presence of the pest. Finally, after identifying the type of pest, the results, along with recommended methods for pest control, are sent back to the farmer.In summary, In this method, farmers employ the Smart Farm software to capture and transmit images of the Chilo suppressalis pest. The captured images then undergo image processing and pest range detection as the next steps in the process. The results, including pest identification and control methods, are then returned to the farmer.

The model has been designed with 400 artificial neural network processing units (APCs), achieving accuracy percentages of 88% and 92%. To conduct a more detailed study of the proposed model, the statistical criteria of recall and F-score were used. Based on the calculations, the trained model demonstrated a recall score of 91%. This criterion shows that the model was able to identify a large percentage of what was expected to be identified by the model. Additionally, the F-score, with an acceptable percentage of 88%, confirmed the accuracy of the trained model.

Researchers have always been highly interested in the valuable data freely provided by farmers for their studies and analyses. In this study, an intelligent system was designed for identifying types of pests such as worms and stalk eaters, which can automatically determine the pest type from the image sent by the farmer using artificial intelligence and deep learning. By utilizing the developed system, farmers can be informed of the type of pest present on their farm in the shortest possible time, with minimal required software training.

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.

A smart greenhouse is a greenhouse that is often remotely and automatically managed with a variety of interconnected hardware and software. Infact, tools and processes are knowledge based and this is the biggest feature of smart greenhouses. Various data are collected through using a variety of tools and methods, and sent to the system or platform via the communication tools and channel. The function of these softwares or platforms is data analysis and decision making. Therefore, like all information systems, it includes hardware, software, and communication tools. Other tools in smart greenhouses are also mainly automated with various information and communication technologies, including a variety of sensors. The main duty of sensors is to collect data from the environment. Tools such as cameras, satellites and drones are also used in different environments. Sensors and cameras are also installed on a variety of vehicle, agricultural machines, etc., to collect data and information necessary for analysis, decision-making, and implementation.The point that is often overlooked is that, smart systems requires the development of its infrastructure through out the country; especially educational and research infrastructure. Reforming social and economic structures is also one of the infrastructures. The importance of paying attention to inter-departmental and intra-departmental relations during smarting is high because all scattered and distributed departments are supposed to be connected and even integrated with the help of information and communication technology. In smarting systems, not only the weaknesses and needs are highlighted, but they must become integrated and related scientific and technical solutions. In this way, both the function of each part and its components are improved, as well as the function and efficiency of the whole system

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.

Smart farming is an emerging concept and is often referred to as the management of various agricultural processes, tools and services that operate automatically or intelligently in combination with information and communication technologies. The goal is to intelligently improve the quality of products and services, and reduce financial, human and other costs. Optimal management of water, soil, air, energy, fertilizer, toxins and other things is also important in smart farming. Remote control, production and greater access to data to improve decision making and increase functionality and efficiency are also among the goals of smart farming. The use of smart tools in the agricultural industry is increasing so much that it can be considered as a kind of digital transformation.This article tries to describe the functions and applications of concepts, tools and methods in the field of smart agriculture in a simple and easy way, so that the audience is familiar with the border between modern and smart agriculture.

The agricultural sector will not be able to provide the food needed by the population in 2050. On the other hand, theThe agricultural sector will not be able to provide the food needed by the population in 2050. On the other hand, the waste of manufactured products double this challenge. Therefore, one of the most important strategies for food security and prevention of waste of agricultural resources and inputs is smart farming. Smart farming is based on data collection, data transfer and storage in remote storage systems, and by analyzing and combining information, enables decision-making in the shortest possible time. Examples of technologies used in the paddy system include the use of smart machinery in paddy fields, smart management of plant diseases and weeds in paddy fields, and smart paddy irrigation. Smart farming increases the quantity and quality of products while optimizing the production and optimal use of manpower. Farm management uses smart technologies to boost agriculture and help reverse migration to rural areas. It also preserves environmental resources. Despite the many advantages of smart systems in farm management, there are two major challenges, including lack of access to a suitable Internet network in rural areas and the need for high skill and accuracy of the operators in smart farming. Necessary solutions have been provided too.

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.

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.