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ردیف | عنوان | نوع |
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1 |
A deep learning-based cow behavior recognition scheme for improving cattle behavior modeling in smart farming
طرح شناخت رفتار گاو مبتنی بر یادگیری عمیق برای بهبود مدلسازی رفتار گاو در کشاورزی هوشمند-2022 Farming and animal husbandry applications are improvised with the implication of machine
learning and artificial intelligence in recent years. The precise estimation, recommendations, and
performances are the prime reason for the technology implication. Owing to the modern agri-
cultural and animal cultures, this article introduces an innovative Behavior Recognition and
Computation Scheme (BRCS) for predicting cow behaviors. The information from the swallowed
microchip is processed based on the observed animal action that is used for the forecast.
Considering the information to be rectilinear, the distractions and distribution patterns (data) are
augmented in identifying and forecasting its behavior. The proposed scheme identifies the pat-
terns using a deep recurrent learning paradigm recurrently. This pattern is distinguished for idle
and non-idle observations for improving the prediction accuracy. Distinguished data patterns are
mapped for the consecutive time and observation data in classifying abnormalities. The proposed
scheme’s performance is validated using the metrics accuracy, precision, computing time, and
mean error. keywords: رفتار گاو | تحلیل داده ها | یادگیری عمیق | تشخیص الگو | Cow behavior | Data analysis | Deep learning | Pattern recognition |
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 |
AI and IoT Based Monitoring System for Increasing the Yield in Crop Production
سیستم مانیتورینگ مبتنی بر هوش مصنوعی و اینترنت اشیا برای افزایش عملکرد در محصولات زراعی-2020 Artificial Intelligence (AI) and Internet of things
(IoT) based monitoring systems are in great demand and gives
a precise extraction and analysis of data. In this paper, the
research is performed on a marigold plant to detect the most
suitable conditions for plant growth. The philosophy behinds
this work is to reduce the risks in agriculture and to promote
smart farming practices. The effect of physical conditions like
humidity, temperature, soil temperature and moisture and
light intensity on the plant growth, is monitored using IoT
based monitoring system. The data responsible for the plant
growth is obtained using different sensors units like DHT11,
LDR, DS18B20, Soil Moisture sensors, Noir camera, singleboard
microcontrollers and Application Programming
Interfaces (APIs). The variation of plant growth rate w.r.t. the
intensity of sunlight was observed within the range of 1000 lx-
1200 lx, category-2 (best). The further analysis of the extracted
parameters is done using different Machine Learning (ML)
algorithms. Logistic Regression, Gradient Boosting Classifier
and Linear Support Vector Classifier (SVC) algorithms are
found best for analysis of physical parameters responsible for
the marigold plant growth. Keywords: Machine Learning | Internet of Things | Smart farming | Agriculture | Artificial Intelligence | OpenCV | Python | Thingspeak |
مقاله انگلیسی |
4 |
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 |
5 |
Decision support systems for agriculture 4.0: Survey and challenges
سیستم های پشتیبانی تصمیم گیری برای کشاورزی 4.0: بررسی و چالش ها-2020 Undoubtedly, high demands for food from the world-wide growing population are impacting the environment
and putting many pressures on agricultural productivity. Agriculture 4.0, as the fourth evolution in the farming
technology, puts forward four essential requirements: increasing productivity, allocating resources reasonably,
adapting to climate change, and avoiding food waste. As advanced information systems and Internet technologies
are adopted in Agriculture 4.0, enormous farming data, such as meteorological information, soil conditions,
marketing demands, and land uses, can be collected, analyzed, and processed for assisting farmers in
making appropriate decisions and obtaining higher profits. Therefore, agricultural decision support systems for
Agriculture 4.0 has become a very attractive topic for the research community. The objective of this paper aims
at exploring the upcoming challenges of employing agricultural decision support systems in Agriculture 4.0.
Future researchers may improve the decision support systems by overcoming these detected challenges. In this
paper, the systematic literature review technique is used to survey thirteen representative decision support
systems, including their applications for agricultural mission planning, water resources management, climate
change adaptation, and food waste control. Each decision support system is analyzed under a systematic manner.
A comprehensive evaluation is conducted from the aspects of interoperability, scalability, accessibility, usability,
etc. Based on the evaluation result, upcoming challenges are detected and summarized, suggesting the development
trends and demonstrating potential improvements for future research. Keywords: Agriculture | Smart farming | Decision-making | Decision support systems |
مقاله انگلیسی |
6 |
Big data in agriculture: Does the new oil lead to sustainability?
داده های بزرگ در کشاورزی: آیا سوخت جدید منجر به پایداری می شود؟-2020 Big data represent a new productive factor (the “new oil” for advocates) that generates new realities in agriculture.
By adding an extra “cyber” dimension to current farming systems, big data lead to the emergence of
new, complex cyber-physical-social systems. However, our understanding of the sustainability of such systems is
still at a rudimental stage. In this critical review we attempt to shed some light on this topic, by identifying and
presenting some issues that put in doubt the sustainability of big data agriculture. By using a punctuated
equilibria lens, we argue that despite their contribution to the economic and environmental performance of
farming, big data act as a speciation mechanism. Hence, they lead to new forms of intraspecific, interspecific and
intergeneric competition, thus putting at risk the most vulnerable players of the game. We conclude by pointing
out that to holistically address the interrelation between big data and agricultural sustainability we need a
hybrid research line, which will combine the qualities of both technology-oriented research and critical social
science. Keywords: Big data | Smart farming | Digital farming | Cyber-physical-social systems | Sustainability | Agriculture |
مقاله انگلیسی |
7 |
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 |
8 |
Blockchain and Internet of Things: A bibliometric study
بلاکچین و اینترنت اشیا : یک مطالعه کتابشناختی-2019 Blockchain is a distributed, decentralized and immutable digital ledger which records transactions across a global network of computers where the information is highly secure. Since its merger with other domains has solved numerous relevant problems, it has poten- tial to resolve the issues of privacy and security in the domain of Internet of Things (IoT). IoT is reshaping the world into smart cities, smart farming, smart grids, smart transport, smart home, and smart healthcare systems. Thus, application of Blockchain in the domain of IoT gives birth to a new domain of Blockchain in IoT (BIoT). This research presents a bibliometric analysis of articles in BIoT domain, covering papers published in top jour- nals and conferences, and finds research trends. It also explores diverse research areas, the most influential publications, top publication venues, top funding agencies and future re- search direction. This research can be a good learning point for young researchers to find attractive relevant research insights within BIoT. Keywords: Blockchain | Internet of things | Bibliometric study | BIoT | Web of science | Scientometrics |
مقاله انگلیسی |
9 |
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 |
مقاله انگلیسی |
10 |
Big Data in Smart Farming - A review
36/5000 داده های بزرگ در کشاورزی هوشمند - مرور-2017 Smart Farming is a development that emphasizes the use of information and communication technology in the
cyber-physical farm management cycle. New technologies such as the Internet of Things and Cloud Computing
are expected to leverage this development and introduce more robots and artificial intelligence in farming.
This is encompassed by the phenomenon of Big Data, massive volumes of data with a wide variety that can be
captured, analysed and used for decision-making. This review aims to gain insight into the state-of-the-art of
Big Data applications in Smart Farming and identify the related socio-economic challenges to be addressed. Fol
lowing a structured approach, a conceptual framework for analysis was developed that can also be used for future
studies on this topic. The review shows that the scope of Big Data applications in Smart Farming goes beyond
primary production; it is influencing the entire food supply chain. Big data are being used to provide predictive
insights in farming operations, drive real-time operational decisions, and redesign business processes for
game-changing business models. Several authors therefore suggest that Big Data will cause major shifts in
roles and power relations among different players in current food supply chain networks. The landscape of stake
holders exhibits an interesting game between powerful tech companies, venture capitalists and often small start
ups and new entrants. At the same time there are several public institutions that publish open data, under the
condition that the privacy of persons must be guaranteed. The future of Smart Farming may unravel in a contin
uum of two extreme scenarios: 1) closed, proprietary systems in which the farmer is part of a highly integrated
food supply chain or 2) open, collaborative systems in which the farmer and every other stakeholder in the chain
network is flexible in choosing business partners as well for the technology as for the food production side. The
further development of data and application infrastructures (platforms and standards) and their institutional
embedment will play a crucial role in the battle between these scenarios. From a socio-economic perspective,
the authors propose to give research priority to organizational issues concerning governance issues and suitable
business models for data sharing in different supply chain scenarios.
Keywords:Agriculture|Data|Information and communication technology|Data infrastructure|Governance|Business modelling |
مقاله انگلیسی |