با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Modeling Invasive Annual Grass Abundance in the Cold Desert Ecoregions of the Interior Western United States
مدل سازی توده های سالانه تهاجمی چمن در مناطق ساحلی سرد صحرای غربی ایالات متحده غربی-2020 Invasive annual grasses, primarily Bromus tectorum, are a severe risk to native vegetation of the intermountainWest.
Once established, annual grasses alter natural fire regimes and outcompete natives until,
in some places, they become the overwhelming dominant. We developed a regional spatial model
encompassing eight ecoregions to indicate the relative abundance of invasive annual grass at five levels
of canopy cover. We used field sample data representing invasive annual grass abundance to build and
calibrate the model. Explanatory variables, represented as map inputs, included image indices, climate,
landform, soil, and human-induced surface disturbance. As a novel modeling approach, we built multiple
models based on classes of invasive annual grass cover abundance were developed individually and then
combined into a final 90-m pixel resolution model that indicates locations relative to invasive annual
grass abundance into classes of < 5%, 515%, 1625%, 2645%, and > 45% cover. Each component model
was validated using held-out sample data, and relative accuracy was 86%, 74%, 62%, 62%, and 60%,
respectively, with an overall kappa of 0.773. The Columbia Plateau, Northern Basin and Range, and Snake
River Plain ecoregions appear to have the greatest overall proportions (4862%) mapped within at least
one of the invasive cover categories. Overlay of the resulting model with major vegetation types indicated
> 50 major vegetation types that are affected by current distribution of annual grasses and are at
risk of expansion. Among these, Intermountain Basins, Big Sagebrush Steppe, and Columbia Plateau
Steppe and Grassland each consistently scored high for invasive risk where they occur. Spatial models of
this type should assist with rangeland restoration and for decisions involving placement of infrastructure,
vegetation treatments where further surface disturbance could trigger additional cheatgrass
expansion. Options exist for extending this model, using climate projections over upcoming decades, to
indicate areas of increasing risk for invasion. Key Words: ecological condition | ecological integrity | human footprint | human modification | invasive annual grass | weeds |
مقاله انگلیسی |
2 |
Unsupervised deep learning and semi-automatic data labeling in weed discrimination
یادگیری عمیق نظارت نشده و برچسب زدن داده های نیمه اتوماتیک در تبعیض علف هرز-2019 In recent years, supervised Deep Neural Networks have achieved the state-of-the-art in image recognition and
this success has spread in many areas. In agricultural field, several researches have been conducted using architectures
such as Convolutional Neural Networks. Despite this success, these works are still highly dependent
on very time–costly manual data labeling. In contrast to this scenario, Unsupervised Deep Learning has no
dependency on data labeling and is targeted as the future of the area, but after a promising start has been
obfuscated by the success of supervised networks. Meanwhile, the low-cost of acquisition of field crop imagery
using Unnamed Aerial Vehicles could be largely boosted in real-world applications if these images could be
annotated without the need for a human specialist. In this work, we tested two recent unsupervised deep
clustering algorithms, Joint Unsupervised Learning of Deep Representations and Image Clusters (JULE) and Deep
Clustering for Unsupervised Learning of Visual Features (DeepCluster), using two public weed datasets. The first
dataset was captured in a soybean plantation in Brazil and discriminates weeds between grass and broadleaf. The
second dataset consists of 17,509 labeled images of eight nationally significant weed species native to Australia.
We evaluated the purely unsupervised clustering performance using the NMI and Unsupervised Clustering
Accuracy metrics and analysed the effects of techniques like data augmentation and transfer learning to improve
clustering quality in a broad discussion that can be useful for unsupervised deep clustering in general. We also
propose the usage of semi-automatic data labeling which greatly reduces the cost of manual data labeling and
can be easily replicated to different datasets. This approach achieved 97% accuracy in discrimination of grass
and broadleaf while reducing the number of manual annotations by 100 times, using a custom set of training
images, without images labeled using inaccurate clusters. Keywords: Deep learning | Unsupervised clustering | Weed discrimination | Semi-automatic labeling |
مقاله انگلیسی |
3 |
Deriving double dividends through linking payments for ecosystem services to environmental entrepreneurship: The case of the invasive weed Lantana camara
سود سهام مضاعف از طریق پیوند پرداخت خدمات اکوسیستم به کارآفرینی زیست محیطی: مورد تهاجمی علفهای هرز Lantana camara-2019 A payment for ecosystem services mechanism is designed to support an environmental enterprise aimed at
controlling Lantana camara, an invasive weed that is costly to eradicate. A forest reserve manager engages the
local community in lantana control efforts. The community converts the weeds into household durable items for
sale. However, as markets for such products may not account for the environmental services generated through
weed control, the enterprise could fail for want of additional financial support. The challenge addressed in this
paper is to incorporate the full environmental benefits of the weed-based enterprise and provide adequate
compensation to the local community. An optimal compensation mechanism is derived through linking the
ecological dimension of weed growth to its impact on biodiversity values within the reserve. Results indicate that
optimal payments to the community would need to take into consideration both the value addition to the
environment from controlling the invasive weed and the opportunity cost of participation by the community.
When there exists a risk of enterprise failure due to low profitability, higher payments by the manager are
required. However, the best environmental outcomes are obtained when the manager incorporates the welfare of
the local community within the utility function. Keywords: Lantana camara | Invasive weed | Environmental enterprise | Payments for ecosystem services | Environmental service | Biodiversity conservation |
مقاله انگلیسی |
4 |
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 |
مقاله انگلیسی |
5 |
Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland
مقیاس کنترل الگوریتم های بینایی ماشین برای تشخیص Rumex و Urtica در چمنزار-2017 Automated robotic weeding of grassland will improve the productivity of dairy and sheep farms while helping to conserve their environments. Previous studies have reported results of machine vision meth- ods to separate grass from grassland weeds but each use their own datasets and report only performance of their own algorithm, making it impossible to compare them. A definitive, large-scale independent study is presented of all major known grassland weed detection methods evaluated on a new standard- ised data set under a wider range of environment conditions. This allows for a fair, unbiased, independent and statistically significant comparison of these and future methods for the first time. We test features including linear binary patterns, BRISK, Fourier and Watershed; and classifiers including support vector machines, linear discriminants, nearest neighbour, and meta-classifier combinations. The most accurate method is found to use linear binary patterns together with a support vector machine.1© 2017 Elsevier B.V. All rights reserved. |
مقاله انگلیسی |
6 |
Using quantitative influence diagrams to map natural resource managers’ mental models of invasive species management
با استفاده از نمودارهای کمی نفوذ به نقشه مدل های ذهنی مدیران منابع طبیعی مدیریت گونه های مهاجم-2016 Despite the significant effect that invasive species have on natural values, the number and extent of
invasions continue to rise globally. At least three dominant reasons explain why policy development
and implementation can fail: differences in managers’ mental models of invasive species management;
cross-agency responsibility; and poor planning and management (i.e., planning–implementation gap).
We used a case study of cross-agency management of gamba grass (Andropogon gayanus) in Australia to
explore the differences in organizational staffs’ mental models of management. The gamba grass invasion
in northern Australia is continuing to expand and associated effects are increasing; coordinated action
across agencies is needed to manage the expansion. Our aim was to examine how staff would represent
their mental models as a diagram that we could compare between individuals and groups. We used cognitive mapping techniques to elicit models of 15 individuals from across 5 organizations, represented as
an influence diagram, which shows the interrelationships that define a system. We compiled the individual influence diagrams to create a team model of management that captures the common connections
across participants’ diagrams. The team model revealed that education, science, legislation, enforcement
and property management plans were perceived to be the most important management tools to control
or eradicate gamba grass. The Weed Management Branch was perceived to have the most central role
in gamba grass management, while other organizations were perceived to have specific roles according
to their core business. Significant positive correlations (i.e., shared perceptions) were observed across
half of the participants, indicating that the some participants have shared models that could be used as a
starting point for discussing the team model, clarifying roles and responsibilities, and potentially building
consensus around a shared model. Dominant opportunities for improvement identified by participants
were better use of management tools, namely education and enforcement, better coordination and collaboration between agencies and increased resourcing. Our research demonstrates the value and validity
of using influence diagrams to explore managers’ mental models and to create a team model that could
serve as a starting point for improved cross-agency natural resource management.
Keywords: Australia | Influence diagrams | Mental models | Weeds | Invasive species management | Perceptions | Social science |
مقاله انگلیسی |