با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
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
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
Modelling fuzzy combination of remote sensing vegetation index for durum wheat crop analysis
مدل سازی ترکیب فازی از شاخص پوشش گیاهی سنجش از دور برای تجزیه و تحلیل محصول گندم دوروم-2019
The application of new technologies (e.g. Internet of Things, mechatronics, remote sensing) to the primary sector will reduce the production costs, limit the waste of primary materials, and reduce the release of polluting compounds into the environment. Precision agriculture (PA) has been growing in the last years thanks to industry efforts and development of applications for diagnostic purposes. Many applications in PA use vegetation indices to measure phenology parameters in terms of Leaf Area Index (LAI). In this context, the correlation of some vegetation indices were analyzed with respect to the durum wheat canopy, evaluating two different phenological stages (elongation and maturity). The results show that for the first stage of growth, the Enhanced Vegetation Index (EVI) was the best-correlated vegetation index with LAI, while the Land Surface Water Index (LSWI) was more reliable for the following stage of growth. Considering trials findings, a fuzzy expert system was developed to combine EVI and LSWI, obtaining a new combined index (Case-specific Fuzzy Vegetation Index) that better represents the LAI in comparison with the single indices. Thus, this approach could give place to a better representative vegetation index of a different biological condition of the plant. It may also serve as a reliable method for wheat yield forecasting and stress monitoring.
Keywords: Precision agriculture | LAI | Remote sensing | Crop management | Landsat images | Ecosystem services
An IoT-based cognitive monitoring system for early plant disease forecast
یک سیستم نظارت شناختی مبتنی بر اینترنت اشیا برای پیش بینی بیماری اولیه گیاه-2019
In this paper, we develop an IoT-based monitoring system for precision agriculture applications such as epidemic disease control. Such an agricultural monitoring system provides environmental monitoring services that maintain the crop growing environment in an optimal status and early predicts the conditions that lead to epidemic disease outbreak. The agricultural monitoring system provides a service to store the environmental and soil information collected from a wireless sensor network installed in the planted area in a database. Furthermore, it allows users to monitor the environmental information about the planted crops in real-time through any Internet-enabled devices. We develop artificial intelligence and prediction algorithms to realize an expert system that allows the system to emulate the decision-making ability of a human expert regarding the diseases and issue warning messages to the users before the outbreak of the disease. Field experiments showed that the proposed system reduces the number of chemical applications, and hence, promotes agriculture products with no (or minimal) chemicals residues and high-quality crops. This platform is designed to be generic enough to be used with multiple plant diseases where the software architecture can handle different plant disease models or other precision agriculture applications.
Keywords: Internet of Things (IoT) | Wireless sensor network (WSN) | Precision agriculture (PA) | Epidemic disease control Expert systems | Cognitive architectures
A secure fish farm platform based on blockchain for agriculture data integrity
یک پلت فرم امن مزرعه ماهی مبتنی بر بلاکچین برای یکپارچگی داده های کشاورزی-2019
Internet of Things (IoT) has opened up a new dimension for smart farming and agriculture because of the natural feature that makes it possible to assign tasks made by a user or that transfers agriculture data obtained through sensors to producers for analysis on various terminal devices. In recent years, heightened interest in agriculture data has arisen since the commercialization of precision agriculture technology. Agriculture data are known to be messy, especially from combine yield monitors, and analysts are concerned with the validity of data, especially given that other people may have impacted data quality at various steps along the data path. The blockchain can be a possible solution to the analyst’s problem of uncertain data quality from prior data manipulation since it ensures data have not been inappropriately manipulated or at the very least documents what changes have been made by specific individuals. This paper proposes a blockchain-based fish farm platform to ensure agriculture data integrity. The designed platform aims to provide fish farmers with secure storage for preserving the large amounts of agriculture data that cannot be tampered with. Diverse processes of the fish farm are executed automatically by using the smart contract to reduce the risk of error or manipulation. A proof of concept that integrates a legacy fish farm system with the Hyperledger Fabric blockchain is implemented on top of the proposed architecture. The efficiency and usability of the proposed platform are demonstrated through a series of experiments using various metrics.
Keywords: Internet of Things | Agriculture data integrity | Blockchain | Permissioned network | Fish farm
Automatic detection of cereal rows by means of pattern recognition techniques
تشخیص خودکار ردیف های غلات با استفاده از تکنیک های تشخیص الگو-2019
Automatic locating of weeds from fields is an active research topic in precision agriculture. A reliable and practical plant identification technique would enable the reduction of herbicide amounts and lowering of production costs, along with reducing the damage to the ecosystem. When the seeds have been sown row-wise, most weeds may be located between the sowing rows. The present work describes a clustering-based method for recognition of plantlet rows from a set of aerial photographs, taken by a drone flying at approximately ten meters. The algorithm includes three phases: segmentation of green objects in the view, feature extraction, and clustering of plants into individual rows. Segmentation separates the plants from the background. The main feature to be extracted is the center of gravity of each plant segment. A tentative clustering is obtained piecewise by applying the 2D Fourier transform to image blocks to get information about the direction and the distance between the rows. The precise sowing line position is finally derived by principal component analysis. The method was able to find the rows from a set of photographs of size 1452 × 969 pixels approximately in 0.11 s, with the accuracy of 94 per cent.
Keywords: Computer vision | Pattern recognition | Principal component analysis | Fourier transform | Precision agriculture
How data analytics is transforming agriculture
چگونه تحلیل داده ها کشاورزی را تغییر داده است؟-2018
Two discussions about the interaction between data analytics and competitive analysis have been taking place in the past decade: one focusing on microlevel firm capabilities and the other on macro-level industry competitiveness. We seek to integrate the micro- and macro-level analyses via the lenses of firms in agricultural input markets. Agriculture is undergoing a tremendous transformation in the collection and use of data to inform smarter farming decisions. Precision agriculture has brought a heightened degree of competition for input supply firms, forcing greater interactions among friends and foes.
KEYWORDS :Precision agriculture ; Data analytics ; Competitive analysis ; Big data ; Internet of Things
Monitoring system using web of things in precision agriculture
سیستم نظارت با استفاده از وب اشیاء در کشاورزی دقیق -2017
As water supplies become scarce because of climatically change, there is an urgent need to irrigate more efficiently in order to optimize water use. In this context, farmers use of a decision-support system is unavoidable. Indeed, the real-time supervision of microclimatic conditions are the only way to know the water needs of a culture. Wireless sensor networks are playing an important role with the advent of the Internet of things and the generalization of the use of web in the community of the farmers. It will be judicious to make supervision possible via web services. The IOT cloud represents platforms that allow to create web services suitable for the objects integrated on the Internet. In this paper we propose an application prototype for precision farming using a wireless sensor network with an IOT cloud.
Keywords: wireless sensor networks | Web of things | monotoring | precision agriculture