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The construction of winter wheat smart water saving irrigation system based on big data and internet of things
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The advent of emerging big data and internet of things, a large number of agriculture data such as the seeding condition, soil moisture, fertiliser, water, pests and meteorology, which could be effectively analysed to guide the smart agriculture. In this paper, the winter wheat smart water saving irrigation system is established based on technologies including big data and internet of things. Through the automatic monitor system of winter wheat growing environment, the big data centre intelligently stores, screens, calibrates, mines and extracts the monitoring data, a system of winter wheat irrigation and fertilisation decision-making based on big data was built. According to the testing data and the weather forecast data, this system can forecast the water requirement of winter wheat in different growth periods and make decisions on automatic irrigation and fertilisation, leading to the timely and proper irrigation of crops.
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... Each department invested in the construction of software and hardware resources, resulting in scattered business systems and software and hardware resources, forming an isolated island with the boundaries of regions, specialties, departments, and systems. e security and stability of various systems are not high, and it is difficult to provide high-quality services to external users, which objectively forms a resource sharing gap [9]. ese problems lead to insufficient, reasonable, and effective utilization of information resources, which leads to repeated construction and waste of funds from time to time and limits the development of water conservancy informatization. ...
The study of how to construct the integration mode of water conservancy information resources for water conservancy applications is extremely practical. This paper discusses the history of cloud computing and data centers, as well as the development paths of related technologies, and proposes the use of cloud computing to integrate and share water conservation data resources. The current state of water conservation informatization, as well as its characteristics and future development trends, is investigated. The regional water conservancy integrated management system is analyzed in detail according to the process and ideas of software project design and development, and the overall architecture model based on cloud computing is determined by analyzing relevant regulations of water conservancy informatization infrastructure and the actual situation of water conservancy informatization construction in sample areas. And the system’s specific functions are designed and presented. This method has changed the traditional concept and mode of water conservation business management, and it can achieve service value-added and efficient and low-cost development and utilization of information resources, according to practice.
... According to the visual analysis method and system of microblog forwarding based on big data analysis technology, independent microblogs are associated to form the topological relationship of text [7]. Zhang et al. studied the application of data analysis such as sentiment analysis to IoT and SN combined data stored in SQL databases, and further studied algorithms and configurations to minimize the delay in data set processing and result retrieval [8], but the configuration equipment requirements are high and the data is precise, so the cost is relatively high. ...
The application degree and application scope of 5G Internet of Things technology and big data analysis technology are becoming wider and wider, bringing opportunities for the development of traditional enterprises and providing technological innovation support for the development of new enterprises. Based on 5G Internet of Things technology and big data technology, this paper designs and studies an intelligent agricultural monitoring platform. We collect crop growth data and monitor crop growth status through this platform to study the 5G-oriented IoT big data analysis method system. This paper studies the data collection and storage issues involved in the huge agricultural IoT data environment. This article analyzes the specific sources of agricultural big data, the specific methods of data collection, and the methods of various database storage technologies. Combining wireless sensor network technology, large-source data processing technology, and distributed data storage technology, a method is proposed to solve the problem of rural Internet data collection and storage in the big data environment. This paper proposes a spatiotemporal block processing TSBPS to store the first detection data. The method uses spatiotemporal preblocking, data compression, and caching to significantly improve the recording speed of near real-time storage and microdetection data. In the experimental part of this article, experiments are carried out on the key parts of the IOT-HSQM system model that may limit storage or query performance. Experimental results show that this article compares TSBPS and direct writing methods. The maximum write speed increased by 79%, and the average write speed increased by 42%. The IOT-HSQM system model can meet the requirements of compiling and query performance and statistical analysis.
As water supplies become scarce because of climatically change, there is an urgent need to irrigate more efficiently in order to optimize water use. In this context, farmers’ use of a decision-support system is unavoidable. Indeed, the real-time supervision of microclimatic conditions are the only way to know the water needs of a culture. Wireless sensor networks are playing an important role with the advent of the Internet of things and the generalization of the use of web in the community of the farmers. It will be judicious to make supervision possible via web services. The IOT cloud represents platforms that allow to create web services suitable for the objects integrated on the Internet. In this paper we propose an application prototype for precision farming using a wireless sensor network with an IOT cloud.
Smart Farming is a development that emphasizes the use of information and communication technology in the cyber-physical farm management cycle. New technologies such as the Internet of Things and Cloud Computing are expected to leverage this development and introduce more robots and artificial intelligence in farming. This is encompassed by the phenomenon of Big Data, massive volumes of data with a wide variety that can be captured, analysed and used for decision-making. This review aims to gain insight into the state-of-the-art of Big Data applications in Smart Farming and identify the related socio-economic challenges to be addressed. Following a structured approach, a conceptual framework for analysis was developed that can also be used for future studies on this topic. The review shows that the scope of Big Data applications in Smart Farming goes beyond primary production; it is influencing the entire food supply chain. Big data are being used to provide predictive insights in farming operations, drive real-time operational decisions, and redesign business processes for game-changing business models. Several authors therefore suggest that Big Data will cause major shifts in roles and power relations among different players in current food supply chain networks. The landscape of stakeholders exhibits an interesting game between powerful tech companies, venture capitalists and often small start-ups and new entrants. At the same time there are several public institutions that publish open data, under the condition that the privacy of persons must be guaranteed. The future of Smart Farming may unravel in a continuum of two extreme scenarios: 1) closed, proprietary systems in which the farmer is part of a highly integrated food supply chain or 2) open, collaborative systems in which the farmer and every other stakeholder in the chain network is flexible in choosing business partners as well for the technology as for the food production side. The further development of data and application infrastructures (platforms and standards) and their institutional embedment will play a crucial role in the battle between these scenarios. From a socio-economic perspective, the authors propose to give research priority to organizational issues concerning governance issues and suitable business models for data sharing in different supply chain scenarios.
Agricultural modeling has long suffered from fragmentation in model implementation. Many models are developed, there is much redundancy, models are often poorly coupled, model component re-use is rare, and it is frequently difficult to apply models to generate real solutions for the agricultural sector. To improve this situation, we argue that an open, self-sustained, and committed community is required to co-develop agricultural models and associated data and tools as a common resource. Such a community can benefit from recent developments in information and communications technology (ICT). We examine how such developments can be leveraged to design and implement the next generation of data, models, and decision support tools for agricultural production systems. Our objective is to assess relevant technologies for their maturity, expected development, and potential to benefit the agricultural modeling community. The technologies considered encompass methods for collaborative development and for involving stakeholders and users in development in a transdisciplinary manner.
Our qualitative evaluation suggests that as an overall research challenge, the interoperability of data sources, modular granular open models, reference data sets for applications and specific user requirements analysis methodologies need to be addressed to allow agricultural modeling to enter in the big data era. This will enable much higher analytical capacities and the integrated use of new data sources. Overall agricultural systems modeling needs to rapidly adopt and absorb state-of-the-art data and ICT technologies with a focus on the needs of beneficiaries and on facilitating those who develop applications of their models. This adoption requires the widespread uptake of a set of best practices as standard operating procedures.
In near infrared spectroscopy (NIRS) analysis, the prediction ability of the model is seriously affected by outliers that may be the result of errors related to the spectral measurements, the chemical analysis, or a combination of both. In this paper, an outlier detection method is described based on the NIRS analysis data of soybean straw. We improved the resampling by half-mean (RHM) method by including a confidence interval (IRHM) and combined the IRHM and Cook’s distance methods (IRHM-COOK) to detect outlier samples in the NIRS data. The confidence interval is an important parameter in the IRHM-COOK method and the optimal confidence intervals for the IRHM and Cook’s distance methods are combined and used as the confidence interval for the IRHM-COOK method. The selection process for the confidence interval is aimed at relative independence between the detection of the spectrum outliers and the chemical outliers. The experimental results show that the IRHM-COOK method is superior to the traditional Mahalanobis distance method, the IRHM method, and the Cook’s distance method using a partial least squares regression (PLS) model. The determination coefficient (R²) of a hemicellulose PLS calibration model increased from 0.4397918 to 0.5333039 and the root mean square error (RMSE) decreased from 0.7926415 to 0.7287254. The PLS models for lignin and cellulose performed better using the IRHM-COOK method than the original model. The results show that the IRHM-COOK method can effectively identify spectrum outliers and chemical outliers for soybean straw biomass. In addition, it is an effective method to handle NIRS analysis data with one type of outlier, which is proven based on an NIRS analysis of starch.
Big data represents a new technology paradigm for data that are generated at high velocity and high volume, and with high variety. Big data is envisioned as a game changer capable of revolutionizing the way businesses operate in many industries. This article introduces an integrated view of big data, traces the evolution of big data over the past 20 years, and discusses data analytics essential for processing various structured and unstructured data. This article illustrates the application of data analytics using merchant review data. The impacts of big data on key business performances are then evaluated. Finally, six technical and managerial challenges are discussed.
Although a large number of reviews have studied different scales of forest ecosystem services, few have concentrated on accounting the spatiotemporal dynamic changes of forest ecosystem services in a region. To make a more accurate accounting, this paper selects intense data sets that assess the physical quantity and value of forest ecosystem services in Anhui provinces from 2009 to 2014. Regulating and supporting Services such as water conservation, carbon sequestration and oxygen release, soil conservation and air purification are included in the paper as variables. Optimize analysis was further conducted in order to identify the spatial and temporal heterogeneity during the study period. We establish a dynamic ecosystem services assessment model with the coefficient of spatial heterogeneity and coefficients of scarce resources and social development to analyze the trend of the value in Anhui province. This model is designed for comparisons of forest services in different scales in Anhui province. The results demonstrate that the total value of forest ecosystem services is beyond the previous estimation while using spatiotemporal visualization. The implications are that the evaluation of forest ecosystem services should be measured more accurately with the help of big data in the future.
With the advent of big data, such data regarding the harmonious relationship of “people-land-time” have also gradually entered into the fields of natural resource management. Ecological land is one of the important natural resources and is fundamental to maintaining ecological security. Ecological land change can lead to a series of eco-environmental problems, including water shortages, soil erosion, increased drought intensity, ecosystem damage, and biodiversity loss. Based on relevant sets of big data, including spatial land data, soil data, DEM, climatic data, and socio-economic data, this study explores the factors influencing ecological land change during the period of 2000-2005 in China’s Beijing-Tianjin-Hebei Region. The results show that the factors influencing different types of ecological land change have substantial differences. For forest land coverage change, Slope type, Soil organic matter (SOM) content, Farmer’s population percentage, and Landform type are the most important independent variables. However, for grassland change, altitude, distance to the primary road and GDP per capita are the most important spatial determinants. Regarding the wetland change, farmer’s population percentage, GDP per capita and altitude are the most important factors influencing wetland changes. This study indicates that natural and social-economic factors can affect ecological land change in China’s Beijing-Tianjin-Hebei Region.
Machine Learning (ML) is a powerful tool that can be used to make predictions on the future nature of data based on the past history. ML algorithms operate by building a model from input examples to make data-driven predictions or decisions for the future. The growing concept “Big Data” has brought much success in the field of data science; it provides data scalability in a variety of ways that empower data science. ML can also be used in conjunction with Big Data to build effective predictive systems or to solve complex data analytic problems. In this work, we propose an electricity generation forecasting system that could predict the amount of power required at a rate close to the electricity consumption for the United States. The proposed scheme uses Big Data analytics to process the data collected on power management in the past 20 years. Then, it applies a ML model to train the system for the prediction stage. The model can forecast future power generation based on the collected data, and our test results show that the proposed system can predict the required power generation close to 99% of the actual usage. Our results indicate that the ML with Big Data can be integrated in forecasting techniques to improve the efficiency and solve complex data analytic problems existing in the power management systems.