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نتیجه جستجو - Precision agriculture

تعداد مقالات یافته شده: 16
ردیف عنوان نوع
1 The Interplay between the Internet of Things and agriculture: A bibliometric analysis and research agenda
تعامل بین اینترنت اشیا و کشاورزی: ​​تجزیه و تحلیل کتاب سنجی و دستور کار تحقیق-2022
The proliferation of the Internet of Things (IoT) has fundamentally reshaped the agricultural sector. In recent years, academic research on the IoT has grown at an unprecedented pace. However, the broad picture of how this technology can benefit the agricultural sector is still missing. To close this research gap, we conduct a bibliometric study to investigate the current state of the IoT and agriculture in academic literature. Using a resource-based view (RBV), we also identify those agricultural resources that are mostly impacted by the introduction of the IoT (i.e., seeds, soil, water, fertilizers, pesticides, energy, livestock, human resources, technology infrastructure, business relations) and propose numerous themes for future research.
keywords: اینترنت اشیا | کشاورزی | کتاب سنجی | پایداری | چالش ها | دیدگاه مبتنی بر منابع | کشاورزی دقیق | Internet of Things | Agriculture | Bibliometrics | Sustainability | Challenges | Resource-based view | Precision agriculture
مقاله انگلیسی
2 FANETs in Agriculture - A routing protocol survey
FANETs در کشاورزی - مرور پروتکل مسیریابی-2022
Breakthrough advances on communication technology, electronics and sensors have led to integrated commercialized products ready to be deployed in several domains. Agriculture is and has always been a domain that adopts state of the art technologies in time, in order to optimize productivity, cost, convenience, and environmental protection. The deployment of Unmanned Aerial Vehicles (UAVs) in agriculture constitutes a recent example. A timely topic in UAV deployment is the transition from a single UAV system to a multi-UAV system. Collaboration and coordination of multiple UAVs can build a system that far exceeds the capabilities of a single UAV. However, one of the most important design problems multi- UAV systems face is choosing the right routing protocol which is prerequisite for the co- operation and collaboration among UAVs. In this study, an extensive review of Flying Ad- hoc network (FANET) routing protocols is performed, where their different strategies and routing techniques are thoroughly described. A classification of UAV deployment in agri- culture is conducted resulting in six (6) different applications: Crop Scouting, Crop Survey- ing and Mapping, Crop Insurance, Cultivation Planning and Management, Application of Chemicals,and Geofencing. Finally, a theoretical analysis is performed that suggests which routing protocol can serve better each agriculture application, depending on the mobility models and the agricultural-specific application requirements.
keywords: کشاورزی هوشمند | کشاورزی دقیق | وسایل نقلیه هوایی بدون سرنشین (UAV) | شبکه های ادوک پرنده (FANET) | پروتکل های مسیریابی | مدل های تحرک | smart farming | precision agriculture | unmanned aerial vehicles (UAVs) | flying adhoc networks (FANETs) | routing protocols | mobility models
مقاله انگلیسی
3 Genetic Algorithm based Internet of Precision Agricultural Things (IopaT) for Agriculture 4:0
اینترنت اشیاء دقیق کشاورزی مبتنی بر الگوریتم ژنتیک (IopaT) برای کشاورزی 4:0-2022
The development of IoT is increasing in our daily life. Its applications are now becoming famous in rural areas also, such as Agriculture 4.0. Cheap sensors, climate data, soil in- formation, and drones are now used to solve many real-time problems. One of the most emerging topics in the IoT in the Agriculture field is IoT based precision agriculture. The range of IoT applications can range between water spraying from drone, soil recommenda- tion for different crops, weather prediction and recommendation for water supply, etc. In this paper we propose a system that will recommend whether water is needed or not by predicting the rain fall using Genetic Algorithm. In this article, we proposed a unique de- cision making method to predict Rainfall using Genetic Algorithm (GA) to identify the ne- cessity of manual water supply is needed or not. The sensor based system will be activated to check wheather the GA based system completes its prediction correctly or not by sens- ing moisture level from the soil. If the moisture level of the soil crosses the pre-defined threshold value then plant watering is performed by quadrotor UAV. A terrace gardening system is also implemented in this article, which uses a pump for water spraying. Various atmospheric parameters help to develop a rainfall prediction system to enhance efficiancy more than 80% in the proposed IopaT system to make the system more interoperable.
keywords: اینترنت اشیا | تصمیم گیری | کشاورزی دقیق | الگوریتم ژنتیک | کشاورزی 4.0 | کوادکوپتر پهپاد | سنسور رطوبت خاک | Internet of Things | Decision Making | Precision Agriculture | Genetic Algorithm | Agriculture 4.0 | Quadrotor UAV | Soil Moisture Sensor
مقاله انگلیسی
4 Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks
پدیده های گیاهی در زمان واقعی تحت راه اندازی رباتیک کشاورزی: یک پلت فرم مبتنی بر دید برای کارهای پیچیده فنوتیپ سازی گیاهان-2021
Plant phenotyping in general refers to quantitative estimation of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Analyzing big data is challenging, and non-trivial given the different complexities involved. Efficient processing and analysis pipelines are the need of the hour with the increasing popularity of phenotyping technologies and sensors. Through this work, we largely address the overlapping object segmentation & localization problem. Further, we dwell upon multi-plant pipelines that pose challenges as detection and multi-object tracking becomes critical for single frame/set of frames aimed towards uniform tagging & visual features extraction. A plant phenotyping tool named RTPP (Real-Time Plant Phenotyping) is presented that can aid in the detection of single/multi plant traits, modeling, and visualization for agricultural settings. We compare our system with the plantCV platform. The relationship of the digital estimations, and the measured plant traits are discussed that plays a vital roadmap towards precision farming and/or plant breeding.
Keywords: Phenotype | Image processing | Spectral | Robotics | Object localization | Precision agriculture | Plant science | Pattern recognition | Computer vision | Automation | Perception
مقاله انگلیسی
5 How data-driven, privately ordered sustainability governance shapes US food supply chains: The case of field to market
چگونه حاکمیت پایداری مبتنی بر داده و با نظم خصوصی ، زنجیره های تأمین مواد غذایی ایالات متحده را شکل می دهد: نمونه موردی برای بازار-2021
Multi-stakeholder initiatives (MSIs) establish metrics and collect farm-level data to measure sustainability in the food system. Rooted in the private sector, MSIs advance goals that were once the responsibility of the state. To make sense of this trend, we distinguish three ideal types of accountability systems in the United States agrifood system: community-based, state-led, and private-ordering systems. We explore the implications of data-driven private-ordering for the distribution of power and accountability along a food supply chain by analyzing Field to Market, a prominent US-based MSI. A central feature of Field to Market are metrics that commodity producers can use to assess their performance and which provide data for food manufacturers and retailers to support sustainability claims. Compared to state-led environmental sustainability efforts from the 1940s until the 1980s, which depended on farmers voluntarily adhering to regulations, metrics rely upon the generation and circulation of data that create a nascent, privately ordered bureaucracy. This change in governance has purported and undeclared consequences for food supply chains. Field to Market’s metrics promise continuous improvements IN agricultural sustainability and accountability in the food system, but they also help food manufacturers and retailers coordinate their supply chains, facilitate the commodification of farm management data, and reframe the meaning of sustainability.
Keywords: Sustainable agriculture | Precision agriculture | Metrics | Governance | Multi-stakeholder initiatives | Accountability | Bureaucracy
مقاله انگلیسی
6 Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study
تجزیه و تحلیل رنگ خاک بر اساس یک دوربین RGB و یک شبکه عصبی مصنوعی برای آبیاری هوشمند: یک مطالعه آزمایشی-2021
Irrigation operations in agriculture are one of the largest water consumers in the world, and it has been increasing due to rising population and consequent increased demand for food. The development of advanced irrigation technologies based on modern techniques is of utmost necessity to ensure efficient use of water. Smart irrigation based on computer vision could help in achieving optimum water-utilization in agriculture using a highly available digital technology. This paper presents a non-contact vision system based on a standard video camera to predict the irrigation requirements for loam soils using a feed-forward back propagation neural network. The study relies on analyzing the differences in soil color captured by a video camera at different distances, times and illumination levels obtained from loam soil over four weeks of data acquisition. The proposed system used this color information as input to an artificial neural network (ANN) system to make a decision as to whether to irrigate the soil or not. The proposed system was very accurate, achieving a mean square error (MSE) of 1.616 × 10—6 (training), 1.004 × 10—5 (testing) and 1.809 × 10—5 (validation). The proposed system is simple, robust and affordable making it promising technology to support precision agriculture.
Keywords: Smart irrigation | Computer vision system | RGB color analysis | Artificial neural network | Feed-forward back propagation neural network
مقاله انگلیسی
7 AI Down on the Farm
هوش مصنوعی کوچک در مزرعه-2020
Agriculture has become an information-intensive industry. In the production of crops and animals, precision agriculture approaches have resulted in the collection of spatially and temporally dense datasets by farmers and agricultural researchers. These big datasets, often characterized by extensive nonlinearities and interactions, are often best analyzed using machine learning (ML) or other artificial intelligence (AI) approaches. In this article, we review several case studies where ML has been used to model aspects of agricultural production systems and provide information useful for farm-level management decisions. These studies include modeling animal feeding behavior as a predictor of stress or disease, providing information important for developing precise and efficient irrigation systems, and enhancing tools used to recommend optimum levels of nitrogen fertilization for corn. Taken together, these examples represent the current abilities and future potential for AI applications in agricultural production systems.
مقاله انگلیسی
8 Paradigm change in Indian agricultural practices using Big Data: Challenges and opportunities from field to plate
تغییر پارادایم در شیوه های کشاورزی هند با استفاده از داده های بزرگ: چالش ها و فرصت ها از زمینه ای به صفحه دیگر-2020
Agriculture is the backbone of the Indian Economy. However, statistics show that the rural population and arable land per person is declining. This is an ominous development for a country with a population of more than one billion, with over sixty-six percent living in rural areas. This paper aims to review current studies and research in agriculture, employing the recent practice of Big Data analysis, to address various problems in this sector. To execute this review, this article outline a framework for Big Data analytics in agriculture and present ways in which they can be applied to solve problems in the present agricultural domain. Another goal of this review is to gain insight into state-of-the-art Big Data appli- cations in agriculture and to use a structural approach to identify challenges to be addressed in this area. This review of Big Data applications in the agricultural sector has also revealed several collection and analytics tools that may have implications for the power relationships between farmers and large corporations.© 2020 China Agricultural University. Production and hosting by Elsevier B.V. on behalf of KeAi. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Contents
Keywords: Agriculture | Data | Governance | Precision agriculture | Smart farming
مقاله انگلیسی
9 CYBELE –Fostering precision agriculture & livestock farming through secure access to large-scale HPC enabled virtual industrial experimentation environments fostering scalable big data analytics
کوبله -Fostering کشاورزی دقیق و دام کشاورزی از طریق دسترسی امن به HPC در مقیاس بزرگ فعال محیط آزمایش صنعتی مجازی پرورش تجزیه و تحلیل داده های بزرگ مقیاس پذیر-2020
According to McKinsey & Company, about a third of food produced is lost or wasted every year, amount- ing to a $940 billion economic hit. Inefficiencies in planting, harvesting, water use, reduced animal contri- butions, as well as uncertainty about weather, pests, consumer demand and other intangibles contribute to the loss. Precision Agriculture (PA) and Precision Livestock Farming (PLF) come to assist in optimiz- ing agricultural and livestock production and minimizing the wastes and costs aforementioned. PA is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. PLF is also a technology-enabled, data-driven approach to live- stock production management, which exploits technology to quantitatively measure the behavior, health and performance of animals. Big data delivered by a plethora of data sources related to these domains, has a multitude of payoffs including precision monitoring of fertilizer and fungicide levels to optimize crop yields, risk mitigation that results from monitoring when temperature and humidity levels reach dangerous levels for crops, increasing livestock production while minimizing the environmental footprint of livestock farming, ensuring high levels of welfare and health for animals, and more. By adding ana- lytics to these sensor and image data, opportunities also exist to further optimize PA and PLF by having continuous data on how a field or the livestock is responding to a protocol. For these domains, two main challenges exist: 1) to exploit this multitude of data facilitating dedicated improvements in performance, and 2) to make available advanced infrastructure so as to harness the power of this information in order to benefit from the new insights, practices and products, efficiently time-wise, lowering responsiveness down to seconds so as to cater for time-critical decisions. The current paper aims to introduce CYBELE, a platform aspiring to safeguard that the stakeholders involved in the agri-food value chain (research community, SMEs, entrepreneurs, etc.) have integrated, unmediated access to a vast amount of very large scale datasets of diverse types and coming from a variety of sources, and that they are capable of actually generating value and extracting insights out of these data, by providing secure and unmediated access to large-scale High Performance Computing (HPC) infrastructures supporting advanced data discovery, pro- cessing, combination and visualization services, solving computationally-intensive challenges modelled as mathematical algorithms requiring very high computing power and capability.
Keywords: Precision agriculture | Precision livestock farming | High performance computing | Big data analytics
مقاله انگلیسی
10 Paradigm change in Indian agricultural practices using Big Data: Challenges and opportunities from field to plate
تغییر پارادایم در شیوه های کشاورزی هند با استفاده از داده های بزرگ: چالش ها و فرصت ها از زمینه به صفحه دیگر-2020
Agriculture is the backbone of the Indian Economy. However, statistics show that the rural population and arable land per person is declining. This is an ominous development for a country with a population of more than one billion, with over sixty-six percent living in rural areas. This paper aims to review current studies and research in agriculture, employing the recent practice of Big Data analysis, to address various problems in this sector. To execute this review, this article outline a framework for Big Data analytics in agriculture and present ways in which they can be applied to solve problems in the present agricultural domain. Another goal of this review is to gain insight into state-of-the-art Big Data applications in agriculture and to use a structural approach to identify challenges to be addressed in this area. This review of Big Data applications in the agricultural sector has also revealed several collection and analytics tools that may have implications for the power relationships between farmers and large corporations.
Keywords: Agriculture | Data | Governance | Precision Agriculture | Smart Farming
مقاله انگلیسی
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