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نتیجه جستجو - گیاهی

تعداد مقالات یافته شده: 40
ردیف عنوان نوع
1 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
2 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
3 Utilizing LiDAR data to map tree canopy for urban ecosystem extent and condition accounts in Oslo
با استفاده از داده های LIDAR به نقشه سایبان درخت برای اکوسیستم های شهری و حساب های وضعیت در اسلو-2021
LiDAR-based segmentation of urban tree canopies and their physical properties (canopy height, canopy diameter, 3D surface and volume) is a replicable, complementary and useful information source for urban ecosystem condition accounts, and an important basis for ecosystem service modeling and valuation. However, using available LiDAR data collected for municipal purposes other than vegetation mapping (such as for example engineering) entails a level of accuracy which may limit the usefulness of the data for change detection in ecosystem accounts. To account for changes in the urban tree canopy of Oslo (capital city of Norway) between 2011 and 2017, a segmentation model was developed based on available airborne LiDAR data scanned for general purposes. The results from the entire built-up area of Oslo indicate a general increase in the number of tall trees (>15 m) and a moderate increase in the number of small trees (<15 m), with the exception of trees between 6 and 10 m which seem to have a relatively constant development over the given period. The total tree canopy area within the built-up area increased by 17.15%, with a corresponding 21.35% increase in the tree canopy volume. The results for the Small House plan area, a policy focus area subject to urban densification and special regulations for felling of large trees, indicate a large increase in small trees (<10 m) and a moderate decrease in tall trees (>10 m). The total tree canopy area within the Small House plan area decreased by 1.04%, with a corresponding 2.13% decrease in the tree canopy volume. With respect to the segmentation accuracy, the changes in aggregate tree canopy cover are too small to determine canopy change with confidence. This study demonstrates the potential for identifying ecosystem condition indicators as well as the limitations of using general purpose LiDAR data to improve the precision of urban ecosystem accounting. For future ecosystem service accounting in urban environments, we recommend that municipalities implement data acquisition programs that combine concurrent field data sampling and LiDAR campaigns designed for urban tree canopy detection, as part of general urban structural inventorying. We recommend using LiDAR and satellite remote sensing data depending on canopy densities. We also recommend that future tree canopy segmentation is done within a cloud-computing environment to ensure sufficient geoprocessing capacity.
keywords: تشخیص نور و محدوده (LIDAR) | سیستم های اطلاعات جغرافیایی (GIS) | سنجش از راه دور | حسابداری اکوسیستم | خدمات محیط زیستی | تقسیم بندی سایبان درخت | Light Detection And Ranging (LiDAR) | Geographical Information Systems (GIS) | Remote sensing | Ecosystem accounting | Ecosystem services | Tree canopy segmentation
مقاله انگلیسی
4 Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks
پدیده های گیاهی در زمان واقعی تحت راه اندازی رباتیک کشاورزی: یک پلت فرم مبتنی بر دید برای کارهای پیچیده فنوتیپ سازی گیاهان-2021
Plant phenotyping in general refers to quantitative estimation of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Analyzing big data is challenging, and non-trivial given the different complexities involved. Efficient processing and analysis pipelines are the need of the hour with the increasing popularity of phenotyping technologies and sensors. Through this work, we largely address the overlapping object segmentation & localization problem. Further, we dwell upon multi-plant pipelines that pose challenges as detection and multi-object tracking becomes critical for single frame/set of frames aimed towards uniform tagging & visual features extraction. A plant phenotyping tool named RTPP (Real-Time Plant Phenotyping) is presented that can aid in the detection of single/multi plant traits, modeling, and visualization for agricultural settings. We compare our system with the plantCV platform. The relationship of the digital estimations, and the measured plant traits are discussed that plays a vital roadmap towards precision farming and/or plant breeding.
Keywords: Phenotype | Image processing | Spectral | Robotics | Object localization | Precision agriculture | Plant science | Pattern recognition | Computer vision | Automation | Perception
مقاله انگلیسی
5 Biometric traits of onion ( Allium cepa L:) exposed to 137Cs and 243Am under hydroponic cultivation
صفات بیومتریک پیاز (Allium cepa L:) در معرض 137 درجه سانتیگراد و 243 آمپر در زیر کشت هیدروپونیک-2021
≈ ≈≈ ≈To elucidate the features of bioaccumulation and phytotoxic effects of long-lived artificial radionuclides, a hydroponic experiment was carried out with the cultivation of onion (Allium cepa L.) in low-mineralized solutions spiked with 137Cs (250 kBq L—1) or 243Am (9 kBq L—1). After the 27-day growth period, 70% of 137Cs and 14% of 243Am were transferred from the solutions to onion biomass with transfer factor values 400 and 80, respectively. Since the bioaccumulation of both radionuclides mainly took place in the roots of onion (77% 137Csand 93% 243Am of the total amount in biomass), edible organs – bulbs and leaves – were protected to some extent from radioactive contamination. At the same time, the incorporation of the radionuclides into the root tissues caused certain changes in their biometric (geometric and mass) traits, which were more pronounced under the243Am-treatment of onion. Exposure to 243Am significantly reduced the number, length, and total surface area of onion roots by 1.3–2.6 times. Under the influence of 137Cs, the dry-matter content in roots decreased by 1.3 times with a corresponding increase in the degree of hydration of the root tissues. On the whole, the data obtained revealed the specific features of 137Cs and 243Am behaviour in “hydroponic solution – plant” system and suggested that biometric traits of onion roots could be appropriate indicators of phyto(radio)toxicity.
Keywords: Radionuclides | Bioaccumulation | Root uptake | Transfer factor | Root–to–shoot translocation | Phytotoxicity
مقاله انگلیسی
6 Phytomyxid infection in the non-native seagrass Halophila stipulacea in St Eustatius, Caribbean Netherlands
عفونت فیتومیکسید در علف دریایی غیر بومی Halophila stipulacea در سنت یوستاتیوس ، کارائیب هلند-2021
Phytomyxids are a monophyletic group of biotrophs/parasites of a variety of organisms including seagrasses with a wide distribution range that includes the Caribbean. The seagrass Halophila stipulacea, native to the Indo-Pacific and Red Sea, is a known host for phytomyxids in the Mediterranean. However, to date phytomyxid infection has not been reported for H. stipulacea in the Caribbean. Infection in H. stipulacea is characterized by swelling of the leaf petioles due to gall formation, and coloration of these galls varies depending on the stage of maturity.H. stipulacea fragments with an apparent phytomyxid infection as well as uninfected fragments were collected in St Eustatius, north-eastern Caribbean, for comparative biometric analysis. Measurements of leaf length, leaf width, internode and root length were taken. Infected H. stipulacea fragments were significantly smaller than uninfected fragments across all biometrics measured, and exhibited similar gall colorations and swelling of the leaf petioles previously described for H. stipulacea in the Mediterranean. Based on our observations, the apparent infection in H. stipulacea fragments on St. Eustatius is likely caused by a phytomyxid parasite and is the first record of phytomyxid infection of this seagrass species in the Caribbean.
Keywords: Non-native seagrass | Plant parasite | Aquatic plant | Infection | Gall | Morphological change
مقاله انگلیسی
7 Management strategies, silvopastoral practices and socioecological drivers in traditional livestock systems in tropical dry forests: An integrated analysis
استراتژی‌های مدیریت، شیوه‌های سیلووپاستورال و محرک‌های اجتماعی-اکولوژیکی در سیستم‌های دام سنتی در جنگل‌های خشک استوایی: یک تحلیل یکپارچه-2021
Understanding traditional livestock management is essential in the design of more sustainable systems, given the forest loss associated to the growing demand for meat. In Latin America, where extensive livestock production is increasing, along with tropical dry forest (TDF) transformation, the role of small holders is critical for designing more sustainable management practices. This study is an integrated socioecological analysis of traditional li- vestock systems in a region with TDF in Mexico. The objectives were to: a) characterise the historical devel- opment and current state of livestock systems and silvopastoral practices, b) define the management strategies and their impacts on forests, and c) identify the regional and local socioecological drivers that influence decision- making processes in livestock and forest management. In-depth interviews were carried out to 32 cattle farmers and analysed using a qualitative-interpretative approach which included multivariate and narrative analyses. Three historical stages (colonization, promotion of livestock and forest conservation) had a strong impact in the development and current state of livestock systems. Access to natural and economic resources and proportion of plant cover (grassland/forest) were essential in defining four groups of management strategies. The main re- gional drivers favouring or restricting production include climate, native vegetation, markets and public policies; at the local scale, socioecological factors, such as water availability, native vegetation, economic assets, local knowledge and their interactions determine heterogeneity in management strategies, decision-making processes and their impacts on forests. Adaptive management of livestock and forests in a context of limited economic resources has allowed the conservation of forest areas and the use of silvopastoral practices with local tree species. The integrated socio-ecological approach and the use of mixed methods allowed a better understanding of drivers and their interrelationships, the local knowledge, objectives and perceptions of farmers in the decision- making processes regarding livestock and forest management. Perspectives of farmers on resource use can contribute to the design of more effective and inclusive policies for sustainable livestock systems in the dry tropics.
keywords: سیستم های اکولوژیکی اجتماعی | دامپروری | شیوه های سیلووپاستوری | جنگل خشک استوایی | Socioecological systems | Livestock management | Silvopastoral practices | Tropical dry forest
مقاله انگلیسی
8 In-field automatic detection of maize tassels using computer vision
تشخیص خودکار کاکل ذرت با استفاده از بینایی ماشین-2021
The heading stage of maize is an important period during its growth and development and indicates the beginning of its pollination. In this regard, an automated method for maize tassel detection is highly important to monitor maize growth. However, the recognition of maize heading stage mainly relies on visual evaluation. This method presents some limitations, such as expensive and subjective. This work proposed a novel method for automatic tassel detection. In the proposed algorithm, a color attenuation prior model was used to model the scene depth of saturation graph to remove image saturation. An Itti visual attention detection algorithm was used to detect the area of interest. Texture features and vegetation indices were used to develop a classification model to eliminate false positives. Pictures were captured using a commercial camera for two years to verify the stability of the proposed algorithm. Three indices were calculated to quantitatively assess and rate the algorithms. Experimental results show that the proposed method outperforms other existing methods, and its recall, precision, and F1 measure values are 86.30%, 91.44%, and 88.36%, respectively. Results indicate that the proposed method can effectively detect maize tassels in field images and remain stable with time.© 2020 China Agricultural University. Production and hosting by Elsevier B.V. on behalf of KeAi. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Maize tassel detection | Texture feature | Vegetation index | Saliency based
مقاله انگلیسی
9 Plant trait estimation and classification studies in plant phenotyping using machine vision – A review
برآورد و طبقه بندی صفات گیاهی در فنوتیپ سازی گیاهان با استفاده از بینایی ماشین - مرور-2021
Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques. Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field. Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red, green and blue (RGB) imaging, thermal imaging, chlorophyll fluorescence imaging (CFIM), hyperspectral imaging, 3-dimensional (3-D) imaging or high resolution volumetric imaging. This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping. This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification. In this paper, information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods. This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural (2-D and 3-D), physiological and temporal trait estimation, and classification studies in plants.
Keywords: Plant phenotyping | Machine vision | Plant trait estimation | Imaging techniques | Leaf segmentation and counting | Plant classification studies
مقاله انگلیسی
10 A grapevine leaves dataset for early detection and classification of esca disease in vineyards through machine learning
یک پایگاه داده انگور برای تشخیص زودهنگام و طبقه بندی بیماری esca در تاکستان ها از طریق یادگیری ماشین-2021
Esca is one of the most common disease that can severely damage grapevine. This disease, if not properly treated in time, is the cause of vegetative stress or death of the attacked plant, with the consequence of losses in production as well as a rising risk of propagation to the closer grapevines. Nowadays, the detection of Esca is carried out manually through visual surveys usually done by agronomists, requiring enormous amount of time. Recently, image processing, computer vision and machine learning methods have been widely adopted for plant diseases classification. These methods can minimize the time spent for anomaly detection ensuring an early detection of Esca disease in grapevine plants that helps in preventing it to spread in the vineyards and in minimizing the financial loss to the wine producers. In this article, an image dataset of grapevine leaves is presented. The dataset holds grapevine leaves images belonging to two classes: unhealthy leaves acquired from plants affected by Esca disease and healthy leaves. The data presented has been collected to be used in a research project jointly developed by the Department of Information Engineering, Polytechnic University of Marche, Ancona, Italy and the STMicroelectronics, Italy, under the cooperation of the Umani Ronchi SPA winery, Osimo, Ancona, Marche, Italy. The dataset could be helpful to researchers who use machine learning and computer vision algorithms to develop applications that help agronomists in early detection of grapevine plant diseases.
Keywords: Plant diseases recognition | Esca disease | Machine learning | Image dataset | Image classification
مقاله انگلیسی
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