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检索条件"任意字段=2nd IFIP International Conference on Computer and Computing Technologies in Agriculture"

共 694 条记录，以下是171-180 订阅

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STUDYED ON THE EXTERNALDEFECTS SEGMENTATION BASED ON THE COLOR CHARACTER OF POTATOES

STUDYED ON THE EXTERNALDEFECTS SEGMENTATION BASED ON THE COL...

引用

The Second ifip international conference on computer and computing technologies in agriculture (CCTA2008)

作者： Min Hao~1 Shuoshi Ma~(1 *) 1 College of Electrical and Mechanical Engineering Inner Mogolia agriculture University Huhot 010018 China

Potato quality detection in China remains at the stage of dependent on human sense organ to identify and *** to the characteristics and requests of potatoes’ detection,The original imag

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THE RESEARCH OF VERTICAL SEARCH ENGINE FOR agriculture

THE RESEARCH OF VERTICAL SEARCH ENGINE FOR AGRICULTURE

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The Second ifip international conference on computer and computing technologies in agriculture (CCTA2008)

作者： Weiying Li Yan Zhao~* Bo Liu Qiang Li College of Information Science and Technology agriculture University of HeBei Baoding 071001 China

Following rapid expansion of huge agriculture information body on the Web, the efficient agriculture information gathering on specified top becomes more and more important in search engine research

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Elevating Crop Protection: Vision Transformer Models for Brassica Black Rot Detection 2

Elevating Crop Protection: Vision Transformer Models for Bra...

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2nd IEEE international conference on Intelligent and Innovative technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Kukreja, Vinay Sharma, Vikrant Sharma, Rishabh Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350306415

The agricultural sector is currently faced with the urgent problem of reducing the incidence of crop diseases to maintain food security and environmentally responsible farming practices. Among these, the Brassica black rot disease poses a considerable risk to crops in the Brassicaceae family, which includes several different cabbage and broccoli varieties. This study aims to revolutionize the diagnosis and classification of Brassica black rot disease at varied severity levels by tapping into the capabilities of cutting-edge deep learning (DL) techniques, more specifically ViT models. This study is based on a rigorously curated dataset that contains 5,000 high-resolution photos of Brassica leaves. These images have been categorized into six different severity levels, ranging from "Class 0"to "Class 5", and the researchers have annotated each of these images. The accuracy and reliability of the annotations were ensured under the oversight of seasoned plant pathologists. To aid in the evaluation and development of the model, a training set, a validation set, and a test set were meticulously collected from the dataset. As our research developed, the ViT-based model, a sophisticated DL architecture, became the key focus of our attention. Excellent accuracy was achieved in classifying the varying degrees of disease severity by meticulous training and tuning the model using the dataset containing cases of Brassica black rot. By incorporating Cohen's Upsilon (), a new performance indicator, we gained a more nuanced understanding of the model's propensity to correctly classify diseases. That was possible because of the additional context provided regarding the model's propensity to correctly classify diseases. Our research showed that the ViT-based model achieved a fantastic level of classification accuracy, with an average rate of 96.17%. In addition, the significant Cohen's Upsilon value of 0.94 demonstrated that the model was able to distinguish between different degrees

关键词： agriculture Technology Brassica Black Rot Crop Protection Deep Learning Disease Detection Machine Learning Precision agriculture Vision Transformer

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INTELLIGENT GROWTH AUTOMATON OF VIRTUAL PLANT BASED ON PHYSIOLOGICAL ENGINE

INTELLIGENT GROWTH AUTOMATON OF VIRTUAL PLANT BASED ON PHYSI...

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The Second ifip international conference on computer and computing technologies in agriculture (CCTA2008)

作者： Qingsheng Zhu~* Mingwei Guo Hongchun Qu Qingqing Deng College of computer Science Chongqing University Chongqing 400044 P.R.China

In this paper,a novel intelligent growth automaton of virtual plant is proposed. Initially,this intelligent growth automaton analyzes the branching pattern which is controlled by genes and then bui

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Soybean Extraction of Brazil Typical Regions Based on Landsat8 Images 1

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9th ifip WG 5.14 international conference on computer and computing technologies in agriculture (CCTA)

作者： Shen, Kejian Han, Xue Wang, Haijun Jiao, Weijie Chinese Acad Agr Engn Innovat Team Crop Monitoring Remote Sensing Beijing Chaoyang Dist Maizidian St 41 Beijing 100125 Peoples R China

ISBN: (数字)9783319483542

ISBN: (纸本)9783319483542;9783319483535

Considering the spatial distribution and harvest times of Brazil soybean, using Landsat8 data, this paper chose 3 study area, determine the optimum classification images by visually comparison of multi-period images, extract soybean by ISODATA unsupervised classification method and visual correction. The conclusion is that Landsat8 path/row of 225/75 (between Study area3 and Study area2) are soybean transition area of harvest 1 time in a year and harvest 2 times in a year;Classification result can be used for sampling survey of national scale and the full coverage survey of county scale. Soybean classification method can be used to improve the method of low resolution image and to guide other medium resolution image.

关键词： Soybean transition area Harvest 2times in a year Sampling survey

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The Knowledge Structure and Core Journals Analysis of Crop Science Based on Mapping Knowledge Domains 9th

The Knowledge Structure and Core Journals Analysis of Crop S...

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9th ifip WG 5.14 international conference on computer and computing technologies in agriculture (CCTA)

作者： Liu, Minjuan Chen, Lu Yuan, Xue Wang, Ting Yan, Yun Wang, Yuefei Chinese Acad Agr Sci Agr Informat Inst Beijing 100081 Peoples R China

ISBN: (纸本)9783319483542;9783319483535

This paper aims at revealing the potential structure of crop science and core journals distribution to provide reference and help for crop research and journal work. On the basis of journal co-citation analysis this paper draws the co-citation map of Crop Science Journal by means of mapping knowledge domains and information visualization technology. 86 crop science journals can be roughly divided into two groups and each group of journals can be subdivided into two regions. Plant science (including plant physiology, plant ecology, plant cell etc.) and biology and chemistry (including biochemistry and molecular biology, gene and genetics etc.) are the mainstream research field of crop science. Crop production and soil science is another important branch of crop science. In addition, there is the phenomenon of cross integration between crop science and environment, horticultural science, plant protection, food processing, animal husbandry etc.

关键词： international Journals Crop science Journal co-citation analysis Mapping knowledge domains

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A Study on the RFID/USN Integrated Middleware for Effective Event Processing in Ubiquitous Livestock Barn

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2nd international conference on Ubiquitous computing and Multimedia Applications

作者： Hwang, Jeonghwan Yoe, Hyun Sunchon Natl Univ Sch Informat & Commun Engn Sunchon South Korea

ISBN: (纸本)9783642209970

The RFID/USN technology is one of the important technologies to implement the ubiquitous society. and it could make many changes in the existing agricultural environment including livestock rearing, cultivation and harvest of agricultural products if such a RFID/USN technology is applied to the agricultural sector. In particular. for livestock barns, the RFID/USN technology has been applied to manage livestock's history and monitor environments of livestock barns, and it is secured the increase of productivity and the transparency of distribution channels through it. In the case of livestock barn systems applying such a RFID/USN technology, an integrated RFID/USN middleware technology is required to provide a combined service through effective processing of generated data and organic connections between application services because it should be processed an enormous amount of data collected via the RFID/USN in real-time. This paper would propose an integrated RFID/USN middleware for implementing a combined service by efficiently processing data collected from the RFID, which is used to identify livestock, and the USN, which is used to monitor livestock barn's environment, and by organically connecting with data. The proposed middleware has features to reduce application's load and provide data requested by applications by filtering data generated in real-time in the RFID and USN environment applied to livestock barns.

关键词： RFID USN Middleware Ubiquitous agriculture

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A Novel Detection on Wheat Disease through CL and RGB Filters by LDA and QDA 2

A Novel Detection on Wheat Disease through CL and RGB Filter...

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2nd international conference on Smart technologies and Systems for Next Generation computing, ICSTSN 2023

作者： Rajesh Kanna, R. Ulagamuthalvi, V. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348002

Wheat is essential everywhere. Wheat leaf diseases hinder growth. Wheat leaf disease identification is crucial to wheat quality and agriculture. This work presents an integrated machine learning strategy to improve wheat leaf disease identification: Colour Layout Filter with Linear Discriminant Analysis, Colour Layout Filter with Quadratic Discriminant Analysis, RGB Filter with Linear Discriminant Analysis and RGB Filter with Quadratic Discriminant Analysis can identify damaged wheat leaves. The agricultural autonomous leaf infection detection system uses images, processing, feature extraction, selection, and learning. This technology helps farmers quickly and reliably diagnose plant illnesses. Automatic leaf disease detection speeds crop diagnosis. This study's Linear Discriminant Analysis Color Layout Filter classifies wheat diseases well. LDA-CLF is most accurate at 88.33%. QDA-RGBF has 80% accuracy. CLF with LDA 0.88 is ideal. RGBF QDA accuracy is 0.80, poor. LDA-CLF recalls 0.88. RGBF with QDA recall is 0. S0, low. With 0.66 kappa, CLF and LDA lead. RGBF with QDA has lowest kappa (0.49). Our best model is CLF's0.88 LDA F-Measure. QDA-enhanced RGBF has 0.80 F-Measure. LDA-CLF has the highest MCC at 0.66. RGBF QDA MCC lowest is 0.5. 0.93 CLF-LDA ROC. RGBF's LDA-based ROC is 0. S4. RGBF QDA models have the highest PRC (0.92). RGBF-LDA has the lowest PRC (0.78). Linear Discriminant Analysis-based Color Layout Filters outperformed other models in this study. © 2023 IEEE.

关键词： Color

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Development and Testing of a CNN-Based Smart Web Application Deep Learning for Early Detection of Plant Diseases 2

Development and Testing of a CNN-Based Smart Web Application...

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2nd international conference on Advanced computing and Communication technologies, ICACCTech 2024

作者： Reddy, Premkumar Singh, Jagendra Rawat, Gopal Singh Shashikant Habelalmateen, Mohammed I. Sree Vani, M. Frisco TX United States School of Computer Science Engineering & Technology Bennett University Greater Noida India School of Engineering Babu Banarasi Das University Electrical Engineering Department Lucknow India The Islamic university Najaf Iraq BVRIT Hyderabad College Of Engineering For Women Department of CSE Hyderabad India

ISBN: (纸本)9798331519056

The primary aim of the current research is to solve this problem that rises at early stage thereby making detection and management easier for plant diseases in hibiscus leaf using deep learning techniques specifically ResNet50 model. The dataset for the study comprised 2500 imges, including healthy leaves and affected ones by powdery mildew, downy mildew or botrytis. These images were preprocessed significantly (resizing, normalization, augmentation) to be ready for being trained. Three deep learning models VGG16, VGG19 and ResNet50 were used for the comparison of these three techniques on disease classification with best performance achieved by ResNet 50 (96.89% accuracy). The research methodology especially around training each model on the dataset, testing them with structured data accuracy along precision and recall metrics using Confusion Matrices provided by libraries such as yellowbrick as well, also integrating ResNet50 within a web application. This app (real time disease identification) would enable the user to upload an image of a hibiscus leaf and would display how accurate resnet50 is able classify healthy or diseased leaves. The results also demonstrate the superiority of ResNet-50 in disease detection compared to other models and prove that this model is capable, strong generalizable feature extractors for agricultural uses. The findings presented here contribute towards the development of precision agriculture as they supply a useful new tool for disease detection at an early stage, enabling easy and rapid intervention mitigating crop losses and promoting agricultural sustainability. © 2024 IEEE.

关键词： Deep reinforcement learning

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DESIGN And IMPLEMENTATION OF ACULOPS LYCOPERSICI POPULATION DYNAMIC MODEL PROTOTYPE BASED ON CELLULAR AUTOMATA

DESIGN AND IMPLEMENTATION OF ACULOPS LYCOPERSICI POPULATION ...

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The Second ifip international conference on computer and computing technologies in agriculture (CCTA2008)

作者： Shuai Zhang~* Dongsheng Wang Linyi Li Yongda Yuan Center of Information Technology in agriculture Shanghai Academy of agriculture Sciences Shanghai P.R.China 201106

Faced upon the research status of Aculops lycopersici,the importance of population dynamic has been put *** feasibility and superiority of cellular automata applied in the simulation of Acu

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