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نتیجه جستجو - agriculture

تعداد مقالات یافته شده: 136
ردیف عنوان نوع
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
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