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1 |
Is the Internet of Things a helpful employee? An exploratory study of discourses of Canadian farmers
آیا اینترنت اشیا یک کارمند مفید است؟ بررسی اکتشافی گفتمان های کشاورزان کانادایی-2022 The increasing global population and the growing demand for high-quality products have called
for the modernization of agriculture. “Internet of Things” is one of the technologies that is pre-
dicted to offer many solutions. We conducted a discourse analysis of 19 interviews with farmers in
Ontario, Canada, asking them to describe their experience of working with IoT and related
technologies. One main discourse with two opposing tendencies was identified: farmers recognize
their relationship with IoT and related technology and view technology as a kind of “employee”,
but some tend to emphasize (1) an optimistic view which is discourse of technology is a “Helpful
Employee”; while others tend to emphasize (2) a pessimistic view which is a discourse of tech-
nology is an “Untrustworthy Employee”. We examine these tendencies in the light of the literature
on organizational behavior and identify potential outcomes of these beliefs. The results suggest
that a farmer’s style of approaching technology can be assessed on a similar scale as managers’
view of their employees and provide a framework for further research. keywords: فناوری اینترنت اشیا | کشاورزی | تحلیل گفتمان | سبک استفاده از تکنولوژی | Internet of things technology | Agriculture | Discourse analysis | Style of use of technology |
مقاله انگلیسی |
2 |
Monitoring crop phenology with street-level imagery using computer vision
پایش فنولوژی محصول با تصاویر سطح خیابان با استفاده از بینایی ماشین-2022 Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining
the use of cheap high-quality cameras with recent advances in deep learning compute solutions to derive relevant
thematic information. We present a framework to collect and extract crop type and phenological information
from street level imagery using computer vision. Monitoring crop phenology is critical to assess gross primary
productivity and crop yield. During the 2018 growing season, high-definition pictures were captured with side-
looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed
200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures.
At 220 specific parcel locations, detailed on the spot crop phenology observations were recorded for 17 crop
types (including bare soil, green manure, and tulips): bare soil, carrots, green manure, grassland, grass seeds,
maize, onion, potato, summer barley, sugar beet, spring cereals, spring wheat, tulips, vegetables, winter barley,
winter cereals and winter wheat. Furthermore, the time span included specific pre-emergence parcel stages, such
as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g.
green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition
model, based on transfer learning with convolutional neural network (MobileNet). A hypertuning methodology
was developed to obtain the best performing model among 160 models. This best model was applied on an
independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage
at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for
implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data
collection and suggests avenues for massive data collection via automated classification using computer vision. keywords: Phenology | Plant recognition | Agriculture | Computer vision | Deep learning | Remote sensing | CNN | BBCH | Crop type | Street view imagery | Survey | In-situ | Earth observation | Parcel | In situ |
مقاله انگلیسی |
3 |
Deep learning based computer vision approaches for smart agricultural applications
رویکردهای بینایی کامپیوتری مبتنی بر یادگیری عمیق برای کاربردهای کشاورزی هوشمند-2022 The agriculture industry is undergoing a rapid digital transformation and is growing powerful by the pillars of cutting-edge approaches like artificial intelligence and allied technologies. At the core of artificial intelligence, deep learning-based computer vision enables various agriculture activities to be performed automatically with utmost precision enabling smart agriculture into reality. Computer vision techniques, in conjunction with high-quality image acquisition using remote cameras, enable non-contact and efficient technology-driven solutions in agriculture. This review contributes to providing state-of-the-art computer vision technologies based on deep learning that can assist farmers in operations starting from land preparation to harvesting. Recent works in the area of computer vision were analyzed in this paper and categorized into (a) seed quality analysis, (b) soil analysis, (c) irrigation water management, (d) plant health analysis, (e) weed management (f) livestock management and (g) yield estimation. The paper also discusses recent trends in computer vision such as generative adversarial networks (GAN), vision transformers (ViT) and other popular deep learning architectures. Additionally, this study pinpoints the challenges in implementing the solutions in the farmer’s field in real-time. The overall finding indicates that convolutional neural networks are the corner stone of modern computer vision approaches and their various architectures provide high-quality solutions across various agriculture activities in terms of precision and accuracy. However, the success of the computer vision approach lies in building the model on a quality dataset and providing real-time solutions.
keywords: Agriculture automation | Computer vision | Deep learning | Machine learning | Smart agriculture | Vision transformers |
مقاله انگلیسی |
4 |
Plant leaf disease detection using computer vision and machine learning algorithms
تشخیص بیماری برگ گیاه با استفاده از بینایی کامپیوتری و الگوریتم های یادگیری ماشین-2022 Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recom-
mended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to
the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind
the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in
plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing
with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the
samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farm-
ers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to
256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means
clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted
using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis
and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally,
the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM),
Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is
tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples. keywords: شبکه های عصبی کانولوشنال | تبدیل موجک گسسته | تجزیه و تحلیل مؤلفه های اصلی | نزدیکترین همسایه | بیماری برگ | Convolutional Neural Networks | Discrete Wavelet Transform | Principal Component Analysis | Nearest Neighbor | Leaf disease |
مقاله انگلیسی |
5 |
Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review
کاربردهای یادگیری ماشین کوانتومی در حوزه زیست پزشکی: مرور سیستماتیک-2022 Quantum technologies have become powerful tools for a wide range of application disciplines,
which tend to range from chemistry to agriculture, natural language processing, and healthcare due to
exponentially growing computational power and advancement in machine learning algorithms. Furthermore,
the processing of classical data and machine learning algorithms in the quantum domain has given rise to
an emerging field like quantum machine learning. Recently, quantum machine learning has become quite a
challenging field in the case of healthcare applications. As a result, quantum machine learning has become
a common and effective technique for data processing and classification across a wide range of domains.
Consequently, quantum machine learning is the most commonly used application of quantum computing.
The main objective of this work is to present a brief overview of current state-of-the-art published articles
between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the
biomedical field. Furthermore, the approach adheres to the requirements for conducting systematic literature
review techniques such as research questions and quality metrics of the articles. Initially, we discovered
3149 articles, excluded the 2847 papers, and read the 121 full papers. Therefore, this research compiled
30 articles that comply with the quantum machine learning models and quantum circuits using biomedical
data. Eventually, this article provides a broad overview of quantum machine learning limitations and future
prospects.
INDEX TERMS: Quantum computing | quantum machine learning | biomedical and healthcare. |
مقاله انگلیسی |
6 |
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 |
7 |
A proactive role of IoT devices in building smart cities
نقش فعال دستگاه های اینترنت اشیا در ساخت شهرهای هوشمند-2022 Due to rapid advancement in technology the world is rapidly changed to face the upcoming challenges and going
towards automation. The use of various IoT devices is making a vast approach and every happening becomes part
of the network and due to that towns are converting into smart cities. The IoT devices collect the data of every
happening smartly and send it for further processing. An imperative part of these devices is containing the
wireless sensors used for building smart cities. A giant set of data is collected in the sensors and is stored in the
data center. Subsequently, the huge data becomes an exorbitant mountain that must be managed smartly if a
smooth operation is required. In this study, how such big data can be managed shrewdly is going to explore and
the proactive role of IoT sensors are investigated which helps in building the future smart cities more independently. The impact of services such as Smart transport, smart energy, smart infrastructure, smart health, smart
agriculture, and smart recreation in respect of smart cities and the old traditional city has been analyzed through
an Analytic Hierarchy Process (AHP). The obtained results showed a satisfactory level of local communities about
98% of people living in smart cities are satisfied in contrast to people living in old traditional cities and others
having a neutral opinion.
keywords: اتوماسیون | دستگاه های اینترنت اشیا | شهر هوشمند | سنسورهای بی سیم | داده های بزرگ | مجموعه غول پیکر | گزاف | Automation | IoT devices | Smart city | Wireless sensors | Big data | Giant set | Exorbitant |
مقاله انگلیسی |
8 |
AgroLens: A low-cost and green-friendly Smart Farm Architecture to support real-time leaf disease diagnostics
AgroLens: یک معماری مزرعه هوشمند کمهزینه و سبز پسند برای پشتیبانی از تشخیص بیماریهای برگ در زمان واقعی-2022 Agriculture is one of the most significant global economic activities responsible for feeding the
world population of 7.75 billion. However, weather conditions and diseases impact production
efficiency, reducing economic activity and the food sovereignty of economies worldwide. Thus,
computational methods can support disease classification based on an image. This classification
requires training Artificial Intelligence (AI) models on high-performance computing resources,
usually far from the user domain. State of the art has proposed the concept of Edge Computing
(EC), which aims to bring computational resources closer to the domain problem to decrease
application latency and improve computational power closer to the client. In addition, EC has
become an enabling technology for Smart Farms, and the literature has appropriated EC to
support these applications. However, predominantly state-of-the-art architectures are dependent
on Internet connectivity and do not allow diverse real-time classification of diseases based on
crop leaf on mobile devices. This paper sheds light on a new architecture, AgroLens, built with
low-cost and green-friendly devices to support a mobile Smart Farm application, operational
even in areas lacking Internet connectivity. Among our main contributions, we highlight the
functional evaluation of AgroLens for AI-based real-time classification of diseases based on leaf
images, achieving high classification performance using a smartphone. Our results indicate that
AgroLens supports the connectivity of thousands of sensors from a smart farm without imposing
computational overhead on edge-compute. The AgroLens architecture opens up opportunities
and research avenues for deployment and evaluation for large-scale Smart Farm applications
with low-cost devices.
keywords: بیماری گیاهی | مزرعه هوشمند | اینترنت اشیا | یادگیری عمیق | سبز پسند| Plant disease | Smart Farm | Internet of Things | Deep learning | Green-friendly |
مقاله انگلیسی |
9 |
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 |
10 |
Farmland monitoring and livestock management based on internet of things
نظارت بر زمین های کشاورزی و مدیریت دام بر اساس اینترنت اشیا-2022 In order to improve the development of modern agriculture, this paper uses the Internet of Things
technology to conduct research on farmland monitoring and livestock management, and build an
intelligent system. Moreover, this paper combines farmland monitoring and livestock manage-
ment, and designs a sensor network based on microkernel operating system. Based on the idea of
modular design, the functional modules and control modules are organically combined, and an
API interface is provided to manage and control nodes and networks, so as to realize the moni-
toring and collection of farmland information and livestock management. The experimental
evaluation shows that the farmland monitoring and livestock management system based on the
Internet of Things proposed in this paper meets the needs of intelligent agricultural production. keywords: اینترنت اشیا | زمین کشاورزی | نظارت بر | مدیریت دام | روش PCB | سیستم های ALOHA | Internet of things | farmland | monitoring | livestock management | PCB method | ALOHA systems |
مقاله انگلیسی |