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ردیف | عنوان | نوع |
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1 |
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 |
2 |
Computer vision for anatomical analysis of equipment in civil infrastructure projects: Theorizing the development of regression-based deep neural networks
چشم انداز کامپیوتری برای تجزیه و تحلیل آناتومیکی تجهیزات در پروژه های زیرساختی عمرانی: نظریه پردازی توسعه شبکه های عصبی عمیق مبتنی بر رگرسیون-2022 There is high demand for heavy equipment in civil infrastructure projects and their performance is a determinant
of the successful delivery of site operations. Although manufacturers provide equipment performance hand-
books, additional monitoring mechanisms are required to depart from measuring performance on the sole basis
of unit cost for moved materials. Vision-based tracking and pose estimation can facilitate site performance
monitoring. This research develops several regression-based deep neural networks (DNNs) to monitor equipment
with the aim of ensuring safety, productivity, sustainability and quality of equipment operations. Annotated
image libraries are used to train and test several backbone architectures. Experimental results reveal the pre-
cision of DNNs with depthwise separable convolutions and computational efficiency of DNNs with channel
shuffle. This research provides scientific utility by developing a method for equipment pose estimation with the
ability to detect anatomical angles and critical keypoints. The practical utility of this study is the provision of
potentials to influence current practice of articulated machinery monitoring in projects. keywords: هوش مصنوعی (AI) | سیستم های فیزیکی سایبری | معیارهای ارزیابی خطا | طراحی و آزمایش تجربی | تخمین ژست کامل بدن | صنعت و ساخت 4.0 | الگوریتم های یادگیری ماشین | معماری های ستون فقرات شبکه | Artificial intelligence (AI) | Cyber physical systems | Error evaluation metrics | Experimental design and testing | Full body pose estimation | Industry and construction 4.0 | Machine learning algorithms | Network backbone architectures |
مقاله انگلیسی |
3 |
Animal biometric assessment using non-invasive computer vision and machine learning are good predictors of dairy cows age and welfare: The future of automated veterinary support systems
ارزیابی بیومتریک حیوانات با استفاده از بینایی کامپیوتری غیرتهاجمی و یادگیری ماشینی پیشبینیکننده خوبی برای سن و رفاه گاوهای شیری هستند: آینده سیستمهای پشتیبانی خودکار دامپزشکی-2022 Digitally extracted biometrics from visible videos of farm animals could be used to automatically assess animal
welfare, contributing to the future of automated veterinary support systems. This study proposed using non-
invasive video acquisition and biometric analysis of dairy cows in a robotic dairy farm (RDF) located at the
Dookie campus, The University of Melbourne, Australia. Data extracted from dairy cows were used to develop
two machine learning models: a biometrics regression model (Model 1) targeting (i) somatic cell count, (ii)
weight, (iii) rumination, and (iv) feed intake and a classification model (Model 2) mapping features from dairy
cow’s face to predict animal age. Results showed that Model 1 achieved a high correlation coefficient (R = 0.96),
slope (b = 0.96), and performance, and Model 2 had high accuracy (98%), low error (2%), and high performance
without signs of under or overfitting. Models developed in this study can be used in parallel with other models to
assess milk productivity, quality traits, and welfare for RDF and conventional dairy farms. keywords: هوش مصنوعی | فیزیولوژی گاو | ماستیت | بیومتریک حیوانات | سنجش از راه دور برد کوتاه | Artificial intelligence | Cows physiology | Mastitis | Animal biometrics | Short range remote sensing |
مقاله انگلیسی |
4 |
الگوریتم ژنتیک چند هدفه و طرح معماری یادگیری عمیق مبتنی بر CNN برای تشخیص موثر spam
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 18 معمولا ایمیل به عنوان قدرتمندترین رسانه در شبکههای اجتماعی آنلاین در نظر گرفته میشود که امکان گفتگو و ارتباط آنلاین کاربران رسانههای اجتماعی آنلاین را با یکدیگر فراهم می کند، همچنین امکان اشتراک گذاری لینک هم وجود دارد. به ویژه، توییتر به عنوان محبوب ترین شبکه اجتماعی شناخته شده است که بهترین کانال ارتباطی برای به اشتراک گذاشتن اخبار، ایده ها، افکار، نظرات و عقاید فعلی کاربران خود با سایر کاربران رسانه های اجتماعی آنلاین است. علیرغم تلاشهایی که برای مبارزه با عملیات اسپم در شبکه اجتماعی آنلاین انجام شده است، اسپم توییتر دارای عملکرد جدیدی محدود به 140 کاراکتر است. این نه تنها علت اصلی آزار کاربران روزمره است، بلکه اکثر مسائل امنیتی رایانه نیز ناشی از آن است که میلیاردها دلار کاهش بهره وری هزینه را در پی دارد. در این مقاله، یک الگوریتم ژنتیک چندهدفه و یک طرح معماری یادگیری عمیق مبتنی بر CNN (MOGA-CNN-DLAS) برای فرآیند تشخیص اسپم غالب در توییتر پیشنهاد میکنیم. جزئیات تجربی و نتایج و بحث حاصل از MOGA-CNN-DLAS پیشنهادی از نظر دقت ، صحت، فراخوان، FScore، RMSE و MAE مورد ارزیابی قرار گرفتند. این ارزیابی با تغییر نسبت دادههای آموزشی کاربردی از سه مجموعه داده واقعی، مانند مجموعه داده توییتر k100 و ASU انجام شد.
کلمات کلیدی: اسپم توییتر | یادگیری عمیق | شبکه عصبی پیچشی یا همگشتی (CNN) | الگوریتم ژنتیک | آنالیز رسانه های اجتماعی | تشخیص موثر اسپم |
مقاله ترجمه شده |
5 |
Resource Allocation in Time Slotted Channel Hopping (TSCH) Networks Based on Phasic Policy Gradient Reinforcement Learning
تخصیص منابع در شبکه های گام کانال با شکاف زمانی (TSCH) بر اساس یادگیری تقویت گرادیان خط مشی فازی-2022 The concept of the Industrial Internet of Things (IIoT) is gaining prominence due to its lowcost solutions and improved productivity of manufacturing processes. To address the ultra-high
reliability and ultra-low power communication requirements of IIoT networks, Time Slotted
Channel Hopping (TSCH) behavioral mode has been introduced in IEEE 802.15.4e standard.
Scheduling the packet transmissions in IIoT networks is a difficult task owing to the limited
resources and dynamic topology. In IEEE 802.15.4e TSCH, the design of the schedule is open
to implementation. In this paper, we propose a phasic policy gradient (PPG) based TSCH
schedule learning algorithm. We construct the utility function that accounts for the throughput,
and energy efficiency of the TSCH network. The proposed PPG based scheduling algorithm
overcomes the drawbacks of totally distributed and totally centralized deep reinforcement
learning-based scheduling algorithms by employing the actor–critic policy gradient method that
learns the scheduling algorithm in two phases, namely policy phase and auxiliary phase. In
this method, we show that the schedule converges quickly compared to any other actor–critic
method and also improves the system throughput performance by 58% compared to the minimal
scheduling function, a default TSCH schedule.
Keywords: Industrial internet of things | IEEE 802.15.4e | Time slotted channel hopping | Deep reinforcement learning | Actor–critic policy gradient methods | Phasic policy gradient |
مقاله انگلیسی |
6 |
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 |
7 |
The politics behind scientific knowledge: Sustainable forest management in Latin America
سیاست پشت دانش علمی: مدیریت جنگل پایدار در آمریکای لاتین-2021 Sustainable Forest Management (SFM) seeks to achieve an equilibrium in the economic, social and environ-
mental value of all types of forests. This practice contrasts with the conventional view of managing forests, in
which the focus is productivity. Thus, discussions about conventional forest management versus sustainable
forest management play a central role in the political and scientific agendas. However, knowledge production
and its direction can be biased by different contextual factors such as the way funding is assigned by each
country, institutional priorities, and constraints on international cooperation. With this paper, we aim to analyze
the contribution of scientific knowledge produced in Latin America within the sustainable forest management
research landscape by applying a literature review method (Scopus database for 2015–2018 period). Our results
show a similar contribution of national and foreign funds and institutions supporting scientific knowledge about
SFM in Latin America. Foreign funding comes mainly from United States of America, and Europe. Latin American
authors lead high proportion of scientific articles, and authorship gender was more equitable between male and
female researchers. The studies were mostly focused on conservation combined with productivity goals, as well
as pure conservation goals, although social studies and restoration goals were also present. Our findings highlight
a significant contribution to the paradigm shift in half of the scientific articles. Some studies provided recom-
mendations (specific or general) derived from their results, but we did not detected a clear relationship with
funding origin. Moreover, we found that the high contribution to the paradigm shift (studies supporting SFM
instead of traditional management) came from institutions based in Latin America. This article aims to contribute
to discussions related to scientific funding in Latin America, the North-South scientific relations, and the future of
forest in times of climate change. keywords: سیاست های جنگلداری | همکاری بین المللی | بررسی ادبیات | منابع طبیعی | تحقیق و توسعه | بودجه پژوهشی | Forestry policies | International cooperation | Literature review | Natural resources | Research and development | Research funding |
مقاله انگلیسی |
8 |
The importance of accounting-integrated information systems for realising productivity and sustainability in the agricultural sector
اهمیت سیستم های اطلاعاتی حسابداری یکپارچه برای تحقق بهره وری و پایداری در بخش کشاورزی-2021 Agricultural information systems are an integral part of modern farming and are helping to
make a significant contribution to improved farm productivity and profitability. To date,
however, there has been a failure to integrate accounting information systems with onfarm data, despite today’s farmers facing unprecedented and interconnected economic
and resource pressures. This study explores this problem in more detail, defines the objectives of the solution and develops a model of integrated accounting and agricultural information systems, drawing on a ‘fads and fashions’ framework and advancing our
understanding of bundled innovations. Using data from a participatory case study in
Australian potato farming, the study integrates accounting data with soil moisture and climate data to track, alert and inform irrigation decisions. Development of preliminary digital software based on the model demonstrates how cost-informed tracking, alerts and
forecasting can be supported by bundling accounting information systems and sensing
technology. In doing so, the model extends the fads and fashions framework for agricultural information systems and demonstrates how accounting information can be the key
for improved water productivity, profitability and agricultural sustainability.
keywords: تصمیم گیری کشاورزی | سیستم های حسابداری یکپارچه | نوآوری های همراه | سنسور | اطلاعات دیجیتال | ایستگاه های آب و هوا | تصویربرداری ماهواره ای | Agricultural decision-making | Integrated accounting systems | Bundled innovations | Sensors | Digital information | Weather stations | Satellite imagery |
مقاله انگلیسی |
9 |
Not just an engineering problem: The role of knowledge and understanding of ecosystem services for adaptive management of coastal erosion
فقط یک مشکل مهندسی نیست: نقش دانش و درک خدمات اکوسیستم برای مدیریت انطباق فرسایش ساحلی-2021 Coastal ecosystems are recognized as important providers of ecosystem services such as carbon storage, increased
fish productivity, and wave energy reduction. In a context of climate change, coastal ecosystems are exposed to
erosion and subject to coastal squeeze, even as they provide natural coastal protection against extreme weather.
While civil engineering solutions often take centre stage in mitigating coastal erosion and protecting infra-
structure from storms and sea level rise, we seek to explore the social dimension of adaptive management of
socio-ecological systems and more specifically the role of knowledge and learning. Using an ecosystem services
(ES) framework, we provide a first evaluation of local stakeholders’ perceptions of coastal habitats in maritime
Quebec. The findings demonstrate the importance of a social approach for coastal ES valuation, in particular in
addressing the complex question of cultural ES. A better understanding of the links between coastal stakeholders
and their natural environment can help decision-makers and practitioners design conservation management and
coastal adaptation measures mainstreaming the role of coastal habitats. Nevertheless, a change towards a socio-
ecological perspective will require long-lasting processes that build on social capacities, such as flexible in-
stitutions and multilevel governance systems. keywords: حکومت انطباقی | ابعاد اجتماعی | فرسایش ساحلی | خدمات محیط زیستی | زیستگاه های ساحلی | ادراک ذینفعان | Adaptive governance | Social dimension | Coastal erosion | Ecosystem services | Coastal habitats | Stakeholders’ perception |
مقاله انگلیسی |
10 |
Design and implementation of parallel algorithm for image matching based on Hausdorff Distance
طراحی و پیاده سازی الگوریتم موازی برای مطابقت تصویر بر اساس فاصله هاسدورف-2021 Object matching two-dimensional images in computer vision has become a significant subject of article acknowledgment and picture investigation. Hausdorff Distance assumes a significant function in coordinating image. To proposed system parallel algorithm for image matching manage the instance of arbitrary commotion, image coordinating, new Hausdorff Distance is proposed in this. In contrast to coordinating two twofold pictures, different techniques, the proposed strategy might be coordinated with a few dark scale image pixel esteems. One case of article acknowledgment is utilized to show the productivity of the proposed strategy. The outcomes demonstrated that, contrasted and, the new Hausdorff Distance (HD) might be more alluring approach to discard image clamor coordinating, because of the extensive reflection to decide the dark scale data Hausdorff Distance of adjoining pixels in the shooting of his realities into account. Besides, the strategy can be acknowledged in a straightforward manner. Keywords: Hausdorff Distance (HD) | Parallel algorithm | Two-dimensional images | Image matching | Computer vision |
مقاله انگلیسی |