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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 |