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

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Smart farming - A flexible approach to improve crop yield and profit using machine learning techniques 2

Smart farming - A flexible approach to improve crop yield an...

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2nd international conference for Emerging Technology, INCET 2021

作者： Sravan Kumar, G. Venkatramaphanikumar, S. Venkata Krishna Kishore, K. VFSTR Deemed to Be University Department of Information Technology Vadlamudi Andhra Pradesh Guntur India VFSTR Deemed to Be University Department of Computer Science and Engineering Vadlamudi Andhra Pradesh Guntur India

ISBN: (纸本)9781728170299

A smart farming system is a farm managing mechanism, which significantly increases the yield of crops by incorporating new technologies in the field of agriculture. Here we present an overview of smart farming software solutions that are recently developed. The proposed methodology works on the data mining techniques and data extracted from satellite information, the Internet, and from soil testing reports fed in the existing databases. This methodology elegantly makes use of the supervised algorithms and regression model for making decisions based on the cognizance of weather changes, season, crop influence factors such as humidity, type of soil, crop type, etc., This system can increase the productivity of fields by managing farm operations smartly. It has emerged together with the Internet of things and high-performance computing to create new opportunities to quantify and understand data-intensive processes in agricultural operational environments. Descriptively, in this work, we analyze the pollutants that cause the crop damage from the year 1980 and forecasted the pollutant like Benzene, PM levels from 2021 to 2051 on different crops. The proposed model predicts the crop life based on pesticides and the dose quantity that is used. This paper draws attention to analyzing and moderating the pesticides dosage level and helps farmers to protect the crop by moderating the dosage and fertilizer levels. © 2021 IEEE.

关键词： Pesticides

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IoT-Enabled Horticultural Lighting for Optimizing Plant Growth and agriculture Operations 2

IoT-Enabled Horticultural Lighting for Optimizing Plant Grow...

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2nd IEEE international conference on Networking and Communications, ICNWC 2024

作者： Srinivasan, S. Lk, Shoba Alavanthar, T. Srinivasan, C. Murugan, S. Sujatha, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India Department of Biomedical Engineering Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350365269

The Internet of Things (IoT) and horticulture lighting systems may improve plant growth and agricultural operations. The variation in the light spectrum, intensities, and durations affect plant physiological systems. The proposed system can dynamically adjust and control lighting settings using real-time sensor data by seamlessly integrating IoT capabilities. These sensors carefully track plant health, ambient conditions, and energy use. This dynamic feedback system allows operators to make informed choices and adjust lighting techniques for optimum growth, yields, and resource use. Plant growth and resource efficiency benefit from light parameter improvement. The connection between IoT and horticulture lighting leads to sustainable agriculture that maximizes agricultural yields and energy efficiency. Moving from static to adaptive lighting systems represents a paradigm change in agriculture, as data-driven decisions enable precision farming. Combining IoT's real-time abilities with horticulture lighting systems promises unique yields, energy savings, and sustainable agriculture practices. © 2024 IEEE.

关键词： Horticulture

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A Novel Transfer Learning Technique for Rice Leaf Disease Detection 2

A Novel Transfer Learning Technique for Rice Leaf Disease De...

引用

2nd IEEE international conference on Advanced technologies in Intelligent Control, Environment, computing and Communication Engineering, ICATIECE 2022

作者： Kumar, Naveen Nikshepa, T. Pareek, Piyush Kumar Parameshachari, B.D. Madhumala, R.B. Sowmya, S. Bengaluru India Dayananda Sagar Academy of Technology and Management Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Dayananda Sagar Academy of Technology and Management Department of ISE Bangalore India

ISBN: (纸本)9781665493963

Image processing has become more sophisticated over the course of the last several years. In this study, we are presenting a model that, based on a convolution network, can identify the status of the rice leaf, namely whether it is a healthy leaf or one that has been afflicted by any illness. Where rice is widely regarded as the primary source of sustenance not just in India but also in other areas of the globe. The agriculture may get infected with the illness and the crops may be affected by the disease for some of the causes listed above. When it may be more difficult to discover the kind of illness, and where farmers may not have the information to find the specific type of disease that an infected leaf was afflicted with. In order to do this, we are use the VGG16 network, which is an architecture based on a convolution neural network (CNN). This is done so that the crop disease can be readily identified, and so that correct findings can be obtained. © 2022 IEEE.

关键词： Convolutional neural networks

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IOT and Artificial Intelligence based Smart agriculture System Using Node MCU 2

IOT and Artificial Intelligence based Smart Agriculture Syst...

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2nd IEEE international conference on Trends in Quantum computing and Emerging Business technologies, TQCEBT 2024

作者： Dhandapani, Padmapriya Kg College of Arts and Science Department of Computer Technology Saravanampatti Coimbatore641 035 India

ISBN: (纸本)9798350384277

Rural action on the cultivate requires exertion for occurrence, planting, keeping up, and collecting crops requires assets such as cash, time, vitality, and work. An counterfeit insights group created a framework that can distinguish plant infections. This analysts utilized a strategy called exchange learning to instruct the AI to recognize edit illnesses and creepy crawly *** has been affected by innovation in numerous ways, which has changed cultivating hones over time. A second obstacle to arrival will be the approximately 4% additional land under construction on the world by 2055, when the population is predicted to rise from 8.0 billion to 9.9 billion. Farming is the essential industry that makes occupations in a expansive number of countries around the world. Agriculturists will in this manner require to utilize less assets with more prominent exertion. In arrange to bolster an additional two billion individuals, nourishment yield would require to rise by 60%, concurring to the same appraise. [1]In today's world, Farming needs to keep up with innovative headway's as well. IoT is fundamental to cleverly cultivating. Web of Things (IoT) sensors grant the vital information approximately cultivate lands. The capacity to screen horticulture utilizing remote sensor systems and collect information from a few sensors that are scattered over diverse regions and transmitted through remote convention is the fundamental advantage of the Web of Things. The IoT stage that supports keen farming is called NodeMCU. In addition to the temperature, wetness, and mugginess sensors, it contains a DC engine. The degree of stickiness and moisture is started to be graded by this framework. © 2024 IEEE.

关键词： Sensor nodes

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Low Cost IoT based Flood Monitoring System Using Machine Learning and Neural Networks: Flood Alerting and Rainfall Prediction 2

Low Cost IoT based Flood Monitoring System Using Machine Lea...

引用

2nd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2020

作者： Rani, Dola Sheeba Jayalakshmi, G.N. Baligar, Vishwanath P. School of Computer Science and Engineering KLE Technological University Huballi Karnataka580020 India

ISBN: (纸本)9781728141671

The term Internet of Things [IoT] refers to the ever expanding complex system of basic things that emphasize communication between computing objects, devices and systems by offering connectivity from anyplace and at any time. It is estimated that by the end of 2020, 50 billion devices are said to be connected. IoT technologies play a crucial role to encompass many smart applications in real life. On the other hand, the crosscutting nature of IoT components and systems, introduce new security challenges. IoT covers advantages for various fields like agriculture, industry, healthcare, automobiles and home automation for improving and automating various day-to-day activities. Flood is usually caused either by change in the state of water body or due to the overflow of rivers, dams, etc. Due to advanced civilization and improved human life, environment problems also tend to increase. This paper includes the effective and flexible method for the detection of flood and alerting system. The most advanced technologies like machine learning (ML) provide significant boon to the field of technology are very powerful in monitoring the normal and abnormal behavioral characters of any machine. The objective of this work is to survey on flood issues. Neural networks are most popular, widely used for rainfall forecasting and perform efficiently. © 2020 IEEE.

关键词： Internet of things

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Smart Farming based on Deep Learning Approaches 2

Smart Farming based on Deep Learning Approaches

引用

2nd Odisha international conference on Electrical Power Engineering, Communication and computing Technology, ODICON 2022

作者： Tripathy, Subhranshu Sekhar Tripathy, Niva Rath, Mamata Pati, Abhilash Panigrahi, Amrutanshu Dash, Manoranjan Driems Autonomous College Dept. of Computer Science and Engg. Odisha Cuttack India Siksha 'o' Anusandhan University Dept. of Computer Science and Engg. Odisha Bhubaneswar India Siksha 'o' Anusandhan University Faculty of Management Sciences Odisha Bhubaneswar India

ISBN: (纸本)9781665478397

A novel idea called 'smart farming' aims to increase the productivity and effectiveness of agriculture by utilizing cutting-edge information technologies. Using the most recent developments in automation, artificial intelligence, and networking, farmers are better able to keep an eye on every step of the process and apply exact treatments selected by machines with superhuman accuracy. With an expanding global population comes a rise in the need for both employment and food. The farmers' traditional practices fell short of meeting these demands. New automated methods were consequently proposed. These innovative techniques satisfied the world's food needs while also creating jobs for billions of people. For effective farm management, a variety of techniques are utilized, including IoT, cloud computing, AI, machine learning, deep learning, big data, etc. Deep learning is one of these, and it is a developing field of study for crop yield forecasting. Engineers, data scientists, and farmers are still developing methods to optimize the amount of human labor needed in agriculture. As key information sources improve, smart farming transforms into a learning system that grows smarter every day. Artificial neural network principles are used in a machine learning method known as deep learning. The depth of deep learning networks, which also allows them to detect latent structures in unlabeled, unstructured data, is what sets them apart from neural networks. Compared to ML techniques, deep learning networks that automatically extract features without human involvement have a considerable advantage. In this research, we suggest a recommendation model and an algorithm to assess crop yield in the upcoming year. We have contrasted the deep learning algorithm with the random forest machine learning algorithm. These algorithms have been the subject of a brief comparison. Python has been used to implement the suggested model. © 2022 IEEE.

关键词： Cloud computing

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

引用

2nd IEEE international conference on Trends in Quantum computing and Emerging Business technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural Networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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Foreword

Foreword

引用

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

作者： Daoliang LI Chunjiang Zhao Co-Chairs of CCTA2008

The papers in this volume comprise the refereed proceedings of the Second ifip international conference on computer and computing technologies in agriculture (CCTA2008), in Beijing, China, 2008.

关键词： Foreword

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A bioeconomic model by quantitative biology to estimate swine production

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

作者： Yuan, Hui Xiao, Surong Wang, Qiujuan Wu, Keliang China Agr Univ Coll Anim Sci & Technol Beijing 100094 Peoples R China Shanghai Xiangxin Livestock Ltd Shanghai 201302 Peoples R China

ISBN: (纸本)9780387772509

A bioeconomic computer model was constructed to simulate biological and economic inputs and outputs for life cycle swine production. Parameters and relationships used in model were developed and verified by comparison with experimental results in the literature. The bioeconomic model was constructed by several modules such as growth and development, pregnancy, lactation, and replacement gilt etc. The result is: (1) the bioeconomic computer model was efficient way to describe pig production system and research factors' effect and their interactions. (2) Traits in the model were: oestrus traits;mature weight and feed requirements of sows;longevity of sows;litter size;growth rate and daily feed intake of young pigs and fatteners;mortality rate of pigs. (3) Sow longevity is 2.12 years and yearly culling rate is 47.20%. Yearly farrowing sow is 2.08 and total numbers of farrowings per gilt is 4.62. (4) Average litter size total born is 11.23, litter size born alive is 10.40, litter size weaned is 8.80.

关键词： quantitative genetics economics computer simulation swine system analysis

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Segmentation of small animal computed tomography images using original CT values and converted grayscale values 7

Segmentation of small animal computed tomography images usin...

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7th ifip WG 5.14 international conference on computer and computing technologies in agriculture, CCTA 2013

作者： Ma, Guoqiang Li, Naixiang Wang, Xiaojuan Dept. of Computer Science & Information Engineering Tianjin Agriculture University Tianjin300384 China Tianjin Agriculture University Tianjin300384 China

ISBN: (纸本)9783642543432

Medical image segmentation is the foundation of normal and diseased tissue 3D visualization, operation simulation and visual operation. In this paper, we comprehensively use two values that represent the same object (body tissue), to segment by the same algorithm implementation. The original CT images are downloaded from the web source, the dog rib tumor CT scan image by GE medical systems, all the experiment of dataset of 312 thoracic CT scans. The core of the segmentation is k-means clustering algorithm. The segmentation process consisted of two phases: (1) convert CT value to JPG gray value or not use the original CT value as the data sets for clustering;(2) segmentation bone tissue using the new k-means clustering algorithm program which is implemented with MATLAB 2012a programming language and for two-dimensional data matrix directly. The experiment produced strikingly different results. These results may be indicating that not only the segmentation algorithm of CT image is important, but also the data for segmentation is important too. © ifip international Federation for Information Processing 2014.

关键词： computerized tomography

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