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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81-90 订阅

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computer and computing technologies in agriculture V 1

引用

丛书名： ifip Advances in Information and Communication Technology

1000年

作者： Daoliang Li Yingyi Chen

ISBN: (数字)9783642272752

ISBN: (纸本)9783642272745;9783642439445

The three-volume set ifip AICT 368-370 constitutes the refereed post-conference proceedings of the 5th ifip TC 5, SIG 5.1 international conference on computer and computing technologies in agriculture, CCTA 2011, held in Beijing, China, in October 2011. The 189 revised papers presented were carefully selected from numerous submissions. They cover a wide range of interesting theories and applications of information technology in agriculture, including simulation models and decision-support systems for agricultural production, agricultural product quality testing, traceability and e-commerce technology, the application of information and communication technology in agriculture, and universal information service technology and service systems development in rural areas. The 59 papers included in the third volume focus on simulation, optimization, monitoring, and control technology.

关键词： Artificial Intelligence Information Systems Applications (incl. Internet) Special Purpose and Application-Based Systems computer Communication Networks Software Engineering Simulation and Modeling

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Development of intelligent equipments for precision agriculture

引用

1st international conference on computer and computing technologies in agriculture (CCTA 2007)

作者： Zhang, Xiaochao Hu, Xiaoan Mao, Wenhua Chinese Acad Agr Mechanizat Sci Inst Machine & Elect Technol Beijing 100083 Peoples R China

ISBN: (纸本)9780387772523

This paper briefly described the general production of intelligent agricultural machine in precision agriculture. It summarized the basic principle, and the application in precision agricultural demonstration field of those agricultural machines, which mainly included wheat variable ferti-seeder, yield distribution information acquiring system, variable controlled large irrigation system moving in synchronous, intelligent spraying herbicide machine, ultra-low altitude remote system, fast-analysis system of food quality and on-board computer, GPS, GIS specially designed for precision agriculture.

关键词： precision agriculture variable ferti-seeder yield distribution variable controlled intelligent spraying

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Bioinformatics and its applications in agriculture

引用

1st international conference on computer and computing technologies in agriculture (CCTA 2007)

作者： Xue, Jian Zhao, Shoujing Liang, Yanlong Hou, Chunxi Wang, Jianhua Jilin Univ Coll Biol & Agr Engn Changchun 130022 Peoples R China

ISBN: (纸本)9780387772523

The field of bioinformatics emerged as a tool to facilitate biological discoveries more than 10 years ago. With the development of Human Genome Project (HGP), the data of biology increased fabulously and marvelously. The ability to capture, manage, process, analyze and interpret data became more important than ever. Bioinformatics and computers can help scientists to solve it. Here are introduced roles of bioinformatics, meanwhile Web tools and resources of bioinformatics were reviewed. And its applications in agriculture were also discussed.

关键词： bioinformatics WorldWideWeb agriculture

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Predictive Analytics for Crop Management: Leveraging Machine Learning for Yield Analysis 2

Predictive Analytics for Crop Management: Leveraging Machine...

引用

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

作者： Singh, Inderjeet Sharma, Muskan Tiwari, Yashs Chandigarh University Department of Computer Science Punjab Mohali India DSMNRU IET Department of Computer Science U.P. Lucknow India

ISBN: (纸本)9798331519056

Precision farming is being disrupted by predictive analytics and machine learning, which add precision on yield forecast level using historical data about the field. To this end, in this study a product-specific machine learning system was created to assess differences across diverse agricultural data types such as weather conditions, soil properties and crop health indicators. The purpose of the study is to find out key drivers that impact cereal production and build reliable predictive models which could predict yield accurately. Using sophisticated algorithms and data-driven insights, this work offers practical suggestions to farmers who take measures to enhance farming methods with a view of scaling up agricultural production. Ultimately, the aim is to advance decision making so that such data can be used for precision agriculture and better crop management strategies with a high degree of sustainability. This particular paper out but how machine learning is used to make leaps and integration in agricultural analytics to predict the yield which may contribute towards overall crop management optimization. © 2024 IEEE.

关键词： Adversarial machine learning

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Solar Power Based IoT Agribot for Irrigation and Farm Monitoring 2

Solar Power Based IoT Agribot for Irrigation and Farm Monito...

引用

2nd IEEE international conference on Device Intelligence, computing and Communication technologies, DICCT 2024

作者： Singh, Gulbir Bhatnagar, Vivek Kumar, Suneet Srivastava, Vivek Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Haldwani Campus Nanital India Mullana Mmict & Bm Haryana Ambala India Mullana Department of Computer Science and Engineering Haryana Ambala India Mmict & Bm Haryana Ambala India

ISBN: (纸本)9798350372847

India's agricultural sector is a significant part of the country's economy, with the majority of land used for agriculture. Water is a crucial resource in this sector, and the use of IoT technology is essential for efficient water use. The Solar Power Based IoT Agribot uses sensing prediction criteria to anticipate irrigation management based on soil temperature, moisture, and humidity. The Agribot measures soil temperature and moisture content using sensors at regular intervals. The innovative SLR and SVM approach is used to accurately estimate soil moisture levels. The experimental data supports intelligent irrigation, which uses less water while producing high crop yields. The Agribot's main purpose is to optimize water reservoir utilization. The SLR algorithms accurately estimated soil moisture with a performance level of 98.63%, and comparisons were made between prediction accuracy and correlation coefficient. © 2024 IEEE.

关键词： Soil moisture

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Potato Plant Disease Detection and Classification for Improved agriculture 2

Potato Plant Disease Detection and Classification for Improv...

引用

2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Chaudary, Sarthak Bakshi, Khushi Iniyan, S. Department of Computer Science and Engineering SRM Institute of Science and Technolgy Kaatankulathur Tamilnadu Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technolgy Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350340235

agriculture is evolving, and deep learning techniques, especially convolutional neural networks, play a pivotal role. They streamline disease detection, providing farmers with timely information to deploy effective disease control strategies and amplify crop yields. Imagine a world where diseases in plants are detected with unparalleled speed and precision. In the age of technological advancement, agriculture stands at the brink of transformation. The proposed work delves into the world of innovation, by harnessing the power of cutting-edge technologies to safeguard crops. Embarking on a journey where image processing, artificial intelligence, and machine learning join forces to become the guardians of the farmlands. This is the vision that drives the project. The magic of image processing techniques, enabling to swiftly identify and categorize leaf diseases. As a result, farmers can now take immediate, well-informed actions, preventing their crops from getting plagued by diseases and ensuring a healthy yield of crops. The project introduces artificial intelligence and machine learning as formidable allies in the quest to diagnose crop diseases at an early stage. These intelligent algorithms detect disease symptoms even before the human eye can perceive them. These models, powered by convolutional neural networks, meticulously analyze leaf images, extract essential features, and classify diseases with remarkable accuracy. By fusing image processing and deep learning, the approach provides a holistic solution to the complex task of plant disease detectionin agriculture. © 2023 IEEE.

关键词： Artificial Intelligence (AI) Convolutional Neural Networks (CNN) Deep Learning Images Machine Learning (ML)

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Estimating pig weight from 2D images

引用

1st international conference on computer and computing technologies in agriculture (CCTA 2007)

作者： Yang, Yan Teng, Guanghui China Agr Univ Dept Agr & Bioenvironm Engn Beijing 100094 Peoples R China

ISBN: (纸本)9780387772523

This paper presents a new method that utilizes the technologies of image processing and computer vision. Firstly, the projected areas of the pig's image captured directly from top view are computed. Secondly, the heights are obtained from side view. Then the pig's weight is estimated by the projected areas and heights. By comparing with the real weight, the mean relative error is 3.2%. The experiment indicates that this hands-off method has great significance in scientific management of the pig's production which does not require large labor and material resources, and also avoid the loss in production resulted from stress.

关键词： pig weight image processing projected area height

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Geo-referenced spatial multimedia application for agricultural resource management

引用

1st international conference on computer and computing technologies in agriculture (CCTA 2007)

作者： Liu, Hui Liu, Gang Meng, Zhijun China Agr Univ Minist Educ Key Lab Modern Precis Agr Syst Integrat Res Beijing 100083 Peoples R China Natl Engn Res Ctr Informat Technol Agr Beijing Peoples R China

ISBN: (纸本)9780387772523

Agricultural resource management is one of important technologies in agricultural. application. The new technology, geo-referenced spatial multimedia has revolutionized the data collection, storage, management and display for agricultural resource management. This paper presents geo-referenced spatial multimedia technology including the system architecture, data collection procedures and data processing based on VMS system of Red Hen Company, and then introduced the application for agricultural resource management.

关键词： GPS GIS geo-referenced spatial multimedia precision agriculture

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Rainfall Prediction Using Deep Learning and Machine Learning Techniques 2

Rainfall Prediction Using Deep Learning and Machine Learning...

引用

2nd IEEE international conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Kothai, G. Sushmitha, Y. Saranya, K Lakshmi Naga Ramya Sri, P. Amulya, P. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India

ISBN: (纸本)9798350315905

Predicting rainfall is a crucial component of contemporary weather forecasting, with consequences for agriculture, hydrology, water management, and disaster preparedness and response. The conventional method of predicting rainfall has depended on historical information and meteorological observations. Yet, more precise and timely predictions of rainfall patterns are now possible thanks to the development of sophisticated computer models, remote sensing technologies, machine learning, and deep learning algorithms. An overview of the methods for predicting rainfall is given in this work, including the analysis of data from satellite imaging, atmospheric conditions, ocean temperatures, and other climate variables. We also look at how machine learning and deep learning algorithms are evolving to build more sophisticated models that can evaluate vast quantities of data and predict rainfall patterns with greater accuracy. © 2023 IEEE.

关键词： Rain

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A Comparison of Machine Learning technologies for Soil Analysis in Smart agriculture: A Comprehensive Review 2

A Comparison of Machine Learning Technologies for Soil Analy...

引用

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

作者： Chaudhary, Preety Gulia, Preeti Gill, Nasib Singh Maharshi Dayanand University Department of Computer Science Haryana Rohtak India

ISBN: (纸本)9798331519056

Irregular ranges of soil fertility alter the efficiency of crops and agricultural ecosystems. Appropriate usage of fertilizer and carbon compost, as well as crop selection based on soil conditions, are made possible by soil fertility management techniques. But rather than assessing the nutrient status of their soil, farmers just threw fertilizer on top at random. Agrochemical doses that are out of balance contaminate agricultural resources. Procedures for laboratory soil analysis aid in monitoring the fertility of the soil. For Indian farmers, however, the methods of laboratory soil analysis are inadequate. An on-the-field soil condition measuring system is the result of recent research and development. Moreover, farmers benefit from enhanced productivity and resource optimization through the use of internet-based soil fertility monitoring and machine-learning approaches. This paper compares several machines learning approaches, including XGboost, SVM, decision trees, random forests, and naive bayes. XGBoost performs better than other machine learning approaches. XGBoost outperforms other machine learning methods regarding accuracy and precision, F1-Score. © 2024 IEEE.

关键词： Adversarial machine learning

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