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نتیجه جستجو - green

تعداد مقالات یافته شده: 366
ردیف عنوان نوع
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 A computer vision system for early detection of anthracnose in sugar mango (Mangifera indica) based on UV-A illumination
یک سیستم بینایی کامپیوتری برای تشخیص زودهنگام آنتراکنوز در انبه قندی (Mangifera indica) بر اساس نور UV-A-2022
The present work describes the development of a computer vision system for the early detection of anthracnose in sugar mango based on Ultraviolet A illumination (UV-A). Anthracnose, a disease caused by the fungus Colletotrichum sp, is commonly found in the fruit of sugar mango (Mangifera indica). It manifests as surface defects including black spots and is responsible for reducing the quality of the fruit. Consequently, it decreases its commercial value. In more detail, this study poses a system that begins with image acquisition under white and ultraviolet illumination. Furthermore, it proposes to analyze the Red, Green and Blue color information (R, G, B) of the pixels under two types of illumination, using four different methods: RGB-threshold, RGB-Linear Discriminant Analysis (RGB-LDA), UV-LDA, and UV-threshold. This analysis produces an early semantic segmentation of healthy and diseased areas of the mango image. The results showed that the combination of the linear discriminant analysis (LDA) and UV-A light (called UV-LDA method) in sugar mango images allows early detection of anthracnose. Particularly, this method achieves the identification of the disease one day earlier than by an expert with respect to the scale of anthracnose severity implemented in this work.
keywords: انبه قندی | آنتراکنوز | LDA | نور UV-A | درجه بندی | پردازش تصویر | Sugar mango | Anthracnose | LDA | UV-A light | Grading | Image processing
مقاله انگلیسی
3 Plasmonic Waveguides: Enhancing quantum electrodynamic phenomena at nanoscale
موجبرهای پلاسمونیک: افزایش پدیده های الکترودینامیکی کوانتومی در مقیاس نانو-2022
The emerging field of plasmonics may lead to enhanced light–matter interactions at extremely nanoscale regions. Plasmonic (metallic) devices promise to effi- ciently control classical and quantum properties of light. Plasmonic waveguides are usually employed to excite confined electromagnetic modes at nanoscale that can strongly interact with matter. Analysis shows that nanowaveguides share similarities with their low-frequency microwave counterparts. In this article, we review ways to study plasmonic nanostructures coupled to quantum optical emitters from a classical electromagnetic perspective. Quantum emitters are mainly used to generate single-photon quantum light that can be employed as a quantum bit, or “qubit,” in envisioned quantum information technologies. We demonstrate different ways to enhance a diverse range of quantum electrodynamic phenomena based on plasmonic configurations by using the Green’s function formalism, a classical dyadic tensor. More specifically, spontaneous emission and superradiance are analyzed through Green’s function-based field quantization. The exciting new field of quantum plasmonics could lead to a plethora of novel optical devices for communications and computing applications in the quantum realm, such as efficient single-photon sources, quantum sensors, and compact on-chip nanophotonic circuits.
مقاله انگلیسی
4 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
5 Developing a two-stage model for a sustainable closed-loop supply chain with pricing and advertising decisions
در حال توسعه یک مدل دو مرحله ای برای یک زنجیره تامین حلقه بسته پایدار با تصمیمات قیمت گذاری و تبلیغات-2021
Closed-Loop Supply Chain (CLSC) has become a critical problem due to its effects on various factors including economic motivations, environmental concerns, and social impacts. Moreover, there are coordination tools, such as pricing and advertising, which impact its performance. In this paper, we offer a two-stage approach to model and solve a sustainable CLSC, taking into account pricing, green quality, and advertising. In the first stage, optimal decisions on pricing, greening, and advertising are made, while in the second stage, a fuzzy multi- objective Mixed Integer Linear Programming (MILP) model is used to maximize the total profit, reduce CO2 emissions, and improve social impacts. Suitable solution methods are introduced according to the scale of the problem. For small-scale instances, an augmented ϵ-constraint method is used to solve the problem. For large-scale instances, approximations are required, and a Lagrangian relaxation algorithm solves the problem in polynomial time. The performance of the proposed model is evaluated through various numerical examples. The results illustrate the applicability and efficiency of the model, while confirming significant improvements in sustainable objectives under optimal pricing, green quality, and advertising. Besides, the proposed Lagrangian relaxation method significantly reduces the computational time for large-scale instances, with only a 2.308% deviation from the optimal results.
Keywords: Sustainable closed-loop supply chain | Multi-objective programming | Supply chain pricing | Augmented ϵ-constraint | Lagrangian relaxation | CO2 emissions
مقاله انگلیسی
6 Green supply chain management and clean technology innovation: An empirical analysis of multinational enterprises in China
مدیریت زنجیره تامین سبز و نوآوری در فناوری پاک: تجزیه و تحلیل تجربی شرکت های چند ملیتی در چین-2021
This study identifies the impact of green supply chain management (GSCM) on clean technology innovation (CTI) by enterprises in China as well as compares the effects of forward and backward GSCM and the differences by industry and home country. The effect of CTI on GSCM is tested by 501 samples of mostly multinational enterprises in China from 2014 to 2016. The results indicate that CTI benefits from GSCM, which remains robust to a series of sensitivity test. And different management directions show great differences, where the backward GSCM has a stronger promotion effect on CTI than the forward GSCM. Moreover, light polluting industries and capital-intensive industries have stronger incentives to adopt GSCM than heavy-polluting industries and labour intensive industries. And domestic companies perform better than foreign companies.
Keywords: Green supply chain | Clean technology innovation | Supply chain management direction | Multinational enterprises
مقاله انگلیسی
7 Integrating corporate website information into qualitative assessment for benchmarking green supply chain management practices for the chemical industry
ادغام اطلاعات وب سایت شرکت ها در ارزیابی کیفی برای محک زدن شیوه های مدیریت زنجیره تامین سبز برای صنایع شیمیایی-2021
The China’s chemical industry has been endeavoring to promote sustainable development through practicing green supply chain management (GSCM). This paper proposes a multi-criteria decision framework with twenty practices to guide companies in the industry to enact GSCM effectively. The exploratory factor analysis (EFA) has been used to cluster the proposed practices. We found five aspects, including economic initiatives, environmental management, eco-design, resource recycling, and stakeholder and employee, constitute the underlying structure of GSCM. A mixed decision tool combining the entropy weight method (EWM) and the analytic hierarchy process (AHP) has been developed and applied to identify key factors. Official website information has been collected and used to analyse the website contents of five benchmarking companies in the China’s chemical industry. The results reveal that the aspects of environmental management, eco-design and resource recycling are the most important GSCM themes. Moreover, the top five practices are top management support, performing life cycle assessment, managing environmental risks, advancing recycling technologies and integrating reverse logistics. Conceptual and practical implications are discussed.
Keywords: Environmental management | Eco-design | Resource recycling | Entropy weight | Analytic hierarchy process | Decision analysis
مقاله انگلیسی
8 Who will take on green product development in supply chains? Manufacturer or retailer
چه کسی توسعه محصول سبز را در زنجیره های تامین به عهده خواهد گرفت؟ تولید کننده یا خرده فروش-2021
: This paper investigates the optimal decisions, profits and social welfare in a green supply chain (GSC) when the manufacturer or retailer conducts green product development. Two Stackelberg game models are constructed here: the manufacturer-led green product development model (MD model) and retailer-led green product development model (RD model), and it is assumed that the green product developer is risk-averse. Then the optimal decisions and members’ profits under two models are obtained. Through comparing them, the results show that the product greenness and leader’s profit are always higher in MD model, but in which model the retail price, wholesale price and follower’s profit are higher/lower is related to the cost coefficient of green product development, the leaders’ risk aversion, and demand uncertainty. Moreover, the risk aversion and demand uncertainty have a negative impact on most decisions and profits, but their impact on followers’ profits under two models and the wholesale price of RD model are still affected by the cost coefficient of green product development. Finally, numerical experiments are used to compare the total profits and social welfare under two models. The results indicate that in most cases, the GSC’s total profit under RD model is higher, but the social welfare under MD model is higher.
Keywords: Green product development | Green supply chain management | Stackelberg game | Risk aversion
مقاله انگلیسی
9 A detailed MILP formulation for the optimal design of advanced biofuel supply chains
یک فرمول دقیق MILP برای طراحی بهینه زنجیره های پیشرفته تأمین سوخت زیستی-2021
The optimal design of a biomass supply chain is a complex problem, which must take into account multiple interrelated factors (i.e the spatial distribution of the network nodes, the efficient planning of logistics activities, etc.). Mixed Integer Linear Programming has proven to be an effective mathematical tool for the optimization of the design and the management strategy of Advanced Biofuel Supply Chains (ABSC). This work presents a MILP formulation of the economical optimization of ABSC design, comprising the definition of the associated weekly management plan. A general modeling approach is proposed with a network structure comprising two intermediate echelons (storage and conversion facilities) and accounts for train and truck freight transport. The model is declined for the case of a multi- feedstock ABSC for green methanol production tested on the Italian case study. Residual biomass feed- stocks considered are woodchips from primary forestry residues, grape pomace, and exhausted olive pomace. The calculated cost of methanol is equal to 418.7 V/t with conversion facility cost accounting for 50% of the fuel cost share while transportation and storage costs for around 15%. When considering only woodchips the price of methanol increases to 433.4 V/t outlining the advantages of multi-feedstock approach.© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Residual biomass | Advanced biofuels | Supply chain design | Logistics network | MILP | Optimization
مقاله انگلیسی
10 Biomass supply chain equipment for renewable fuels production: A review
تجهیزات زنجیره تأمین زیست توده برای تولید سوخت های تجدیدپذیر: یک مروز-2021
The production of renewable fuels is a critical component of global strategies to reduce greenhouse gas (GHG) emissions. Moreover, the collection of raw materials for its production can provide added benefits such as reduction of wildfire risk, additional income for farmers, and decreased disposal costs. Although there is substantial literature on design and modeling of supply chains, the authors were unable to find a single reference with the information needed for the selection and cost estimation of each type of equipment involved in the supply chain. Therefore, the goal of this research is to gather information necessary for the construction and utilization of models that might drive the identification of a feasible supply chain to produce renewable fuels at a commercial scale. The primary objectives are to 1) understand the supply chain of critical feedstocks for renewable fuels production; 2) identify the equipment commercially available for collection and ad equation of feedstock; and 3) consolidate information regarding equipment cost, energy consumption, and efficiency, as well as feedstock storage and transportation systems. This paper provides a compilation for five feedstock types studied for sustainable aviation fuel production: 1) agricultural residues and grasses, 2) forest residues, 3) urban wood waste, 4) oilseeds, 5) fats, oils & greases. All the technologies involved from the field to the gate of the preprocessing or conversion unit were reviewed. The information on fats, oils & greases supply chains and equipment purposely designed for forest thinning and pruning was very limited.
Keywords: Feedstock | Collection and adequation | Biofuels | Renewable fuels | Sustainable aviation fuel | Supply chain configuration
مقاله انگلیسی
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