506 research outputs found

    Local Industrialization Based Lucrative Farming Using Machine Learning Technique

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    In recent times, agriculture have gained lot of attention of researchers. More precisely, crop prediction is trending topic for research as it leads agri-business to success or failure. Crop prediction totally rest on climatic and chemical changes. In the past which crop to promote was elected by rancher. All the decisions related to its cultivation, fertilizing, harvesting and farm maintenance was taken by rancher himself with his experience. But as we can see because of constant fluctuations in atmospheric conditions coming to any conclusion have become very tough. Picking correct crop to grow at right times under right circumstances can help rancher to make more business. To achieve what we cannot do manually we have started building machine learning models for it nowadays. To predict the crop deciding which parameters to consider and whose impact will be more on final decision is also equally important. For this we use feature selection models. This will alter the underdone data into more precise one. Though there have been various techniques to resolve this problem better performance is still desirable. In this research we have provided more precise & optimum solution for crop prediction keeping Satara, Sangli, Kolhapur region of Maharashtra. Along with crop & composts to increase harvest we are offering industrialization around so rancher can trade the yield & earn more profit. The proposed solution is using machine learning algorithms like KNN, Random Forest, Naïve Bayes where Random Forest outperforms others so we are using it to build our final framework to predict crop

    A Big Data Analytics Framework in Climate Smart Agriculture

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    Climate Smart Agriculture incorporates information on soils, bothers, maladies, costs and different variables to increment illustrative power. Creating atmosphere strong horticulture is fundamental to accomplishing future sustenance security and environmental change objectives. Through CSA application the ranchers can anticipate crop type (crop exhortation) which is suitable for accessible condition. Climate Smart farming has been generally used to express agricultural practices that will increment horticultural efficiency and nourishment security and to foresee rural items. CSA application have Climate information (like: Max Temp, Min Temp, Humidity, Rain fall, daylight, wind course, wind speed), Fertilizer and Soil Data's (like, Black Soil, Red Soil).The review aim to help farmers better adapt to temperature extremes, droughts or excess water in fields so that they can make better decisions for the environment and maximize production or profits. The data collection is an important role in the work process. Enabling farmers to head massive amounts of data collected through sensors to predict the best time to plant, what type of seed to use, and where to plant in order to improve yields, cut operational costs, and minimize environmental impact.Big data analytics provide new ways for businesses updates and requirement for updating and government to analyze unstructured data. Now a day, big data is one of the most important and challenging point in information technology world. It is executing very important role in future. Big data changes the way of world for management and use big amount of data Keywords: Climate Smart Agriculture, Big Data Analytics and Hadoop DOI: 10.7176/CEIS/10-6-01 Publication date:July 31st 201

    Application of Data Visualization and Big Data Analysis in Intelligent Agriculture

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    Intelligent agriculture can renovate agricultural production and management, making agricultural production truly scientific and efficient. The existing data mining technology for agricultural information is powerful and professional. But the technology is not well adapted for intelligent agriculture. Therefore, this paper introduces data visualization and big data analysis into the application scenarios of intelligent agriculture. Firstly, an intelligent agriculture data visualization system was established, and the RadViz data visualization method was detailed for intelligent agriculture. Moreover, the intelligent agriculture data were processed using dimensionality reduction through principal component analysis (PCA) and further optimized through k-means clustering (KMC). Finally, the crop yield was predicted using the multiple regression algorithm and the residual principal component regression algorithm. The crop yield prediction model was proved effective through experiments

    Big Data and the Internet of Things

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    Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

    Approaches for advancing scientific understanding of macrosystems

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    The emergence of macrosystems ecology (MSE), which focuses on regional- to continental-scale ecological patterns and processes, builds upon a history of long-term and broad-scale studies in ecology. Scientists face the difficulty of integrating the many elements that make up macrosystems, which consist of hierarchical processes at interacting spatial and temporal scales. Researchers must also identify the most relevant scales and variables to be considered, the required data resources, and the appropriate study design to provide the proper inferences. The large volumes of multi-thematic data often associated with macrosystem studies typically require validation, standardization, and assimilation. Finally, analytical approaches need to describe how cross-scale and hierarchical dynamics and interactions relate to macroscale phenomena. Here, we elaborate on some key methodological challenges of MSE research and discuss existing and novel approaches to meet them

    A Web application to estimate the climate change effects on agriculture in Thailand

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    A Machine Learning Framework to Predict Determinant Factors of Seeds

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    In this paper, we audit the machine learning apparatuses for foreseeing determinant components of seeds. We depict this issue regarding Big Data, ANN, Hadoop and R. We consider Machine-learning techniques especially suited to forecasts dependent on existing information, yet exact expectations about the far off future are frequently on a very basic level unthinkable. Farming is an industry where recorded and current information flourish. This survey researches the various information sources accessible in the horticultural field and dissects them for utilization in Seed determinant factor Predictions. We recognized certain relevant information and researched techniques for utilizing this information to improve forecast inside the farming action
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