Agricultural big data
211450752
Agricultural Big Data Analytics and the Ethics of Power
Agricultural Big Data analytics (ABDA) is being proposed to ensure better farming practices, decision making, and a sustainable future for humankind. However, the use and adoption of these technologies may bring about potentially undesirable consequences, such as exercises of power. This paper will analyse Brey's five distinctions of power relationships (manipulative, seductive, leadership, coercive, and forceful power) and apply them to the use agricultural Big Data. It will be shown that ABDA can be used as a form of manipulative power to initiate cheap land grabs and acquisitions. Seductive power can be exercised by pressuring farmers into situations they would not have otherwise chosen (such as installing monitors around their farm and limited access to their farm and machinery) It will be shown that agricultural technology providers (ATPs) demonstrate leadership power by getting farmers to agree to use ABDA without informed consent. Coercive power is exercised when ATPs threaten farmers with the loss of ABDA if they do not abide by the policies and requirements of the ATP or are coerced to remain with the ATP because of fear of legal and economic reprisal. ATPs may use ABDA to determine willingness to pay rates from farmers, using this information to force farmers into precarious and vulnerable positions. Altogether, this paper will apply these five types of power to the use and implementation of ABDA to demonstrate that it is being used to exercise power in the agricultural industry.
Mark Ryan
221918041
Research Status and Future Expectation of Agricultural Big Data Technology
Currently, China has entered the era of digital economy at a high speed, and Big Data has become a new factor of Chinese national production. In order to promote the transformation and upgrading of agriculture in China and promote the all round and rapid development of agricultural Big Data in China, this paper summarizes Big Data technology, especially the related technology of agricultural Big Data, studies the present situation of the development of agricultural Big Data technology in foreign countries, focuses on the overall situation of the development of agricultural Big Data technology in China based on the investigation and analysis of the relevant literature of agricultural Big Data at home and abroad. Finally, the task and direction of agricultural Big Data technology development in China in the future are discussed accordingly in this paper.
2020 15th International Conference on Computer Science Education (ICCSE)
Li-Ping Yang Fang Zheng Li Qin
226063406
Applied Research on Agricultural Big Data
With the continuous advancement of digital informationization, the agricultural field has naturally entered the era of big data. At present, big data technology has achieved remarkable results in the Internet industry, but it is still in its infancy in the agricultural field. Due to the complexity of the agricultural field itself and the obvious diversity and heterogeneity of data, the agricultural Internet of Things in the big data environment is facing many difficulties and challenges, especially the collection and storage of massive data. The purpose of this article is to study the application of agricultural big data. This article briefly introduces the background of the development of agricultural big data and the significance of developing agricultural big data, and summarizes the meaning of big data. At the same time, this article summarizes and analyzes the major applications of agricultural big data in production process management, agricultural resource management, and agricultural ecology environmental management, agricultural products and food safety management. In the experimental part, this paper designed and implemented an agricultural big data system platform based on Hadoop. This article simulated the query of 10, 100 and 1000 records respectively for 10,000, 100,000, 1 million and 10 million records, and the average response time was 56ms. The experimental results show that the performance of the agricultural big data platform designed in this paper can meet the needs.
Feng Wang
221473509
Research on Innovation of Agricultural Big Data Information Transmission
This paper analyzes the transmission of agricultural big data information, which is helpful to enhance the synergy and operational efficiency of agricultural technology innovation and achievement transformation. Starting from the practical problems faced in the transmission of agricultural information, we discuss the historical opportunities in the effective transmission of agricultural big data information, analyze the linkage mechanism and path selection of agricultural big data information transmission, and propose policy suggestions for improving the linkage mechanism of agricultural information transmission.
2020 International Conference on Wireless Communications and Smart Grid (ICWCSG)
Ya Wang Yanmei Yang
222223734
A Standard Architecture of Agricultural Big Data for Deep Learning
Agricultural big data has become a key resource for the development of modern agriculture, and has occupied an important role in the field of agricultural scientific and technological innovation. With the development of deep learning in recent years, we can effectively improve agricultural production efficiency by existing agricultural data sets to build deep learning algorithm models. There are new ideas to explore the standard architecture of agricultural big data. By formulating standard and standardizing agricultural data process, we established a standard architecture of agricultural big data for deep learning. The result can promote the implementation of deep learning application, and support standardization of technological achievements transformation in agricultural big data.
2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA)
Xiaojun Cui Zihang Gao
212549006
Crop Knowledge Discovery Based on Agricultural Big Data Integration
Nowadays, the agricultural data can be generated through various sources, such as: Internet of Thing (IoT) sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses. The analysis of this big data enables farmers, companies and agronomists to extract high business and scientific knowledge, improving their operational processes and product quality. However, before analysing this data, different data sources need to be normalised, homogenised and integrated into a unified data representation. In this paper, we propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models. We also apply some methods to extract knowledge with the view to improve crop yield; these include finding suitable quantities of soil properties, herbicides and insecticides for both increasing crop yield and protecting the environment.
ICMLSC
Vu Minh Ngo M Tahar Kechadi
215833827
IoT devices for agricultural big data
Masayuki Hirafuji Tokihiro Fukatsu
204820541
Research on the Application of Agricultural Big Data Processing with Hadoop and Spark
Numerous terminal equipment in the agricultural park collect environmental data that affects crop growth every day. Proper analysis of these massive amounts of data can acquire useful information on the status of crop growth. In this paper, two cloud computing frameworks, Apache Hadoop and Apache Spark are used to study agricultural big data analysis. This paper developed applications for real agricultural park big data analysis in both frameworks and implemented a yield prediction model based on multiple linear regression using Spark MLlib. The performance of the two frameworks in agricultural big data processing was studied and compared through various experiments. The experiments show that the comprehensive performance of Spark is higher than Hadoop, and the model can obtain better prediction results.
2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)
Yan Cheng Qiang Zhang Ziming Ye
215800553
The Implementation of a Practical Agricultural Big Data System
Agricultural big data is growing rapidly and it faces many known and unknown obstacles. This paper focuses on the need for managing farms effectively and making decisions accurately. Using agricultural IoT technology, big data technology and machine learning algorithms, the big data application system is established. Including data collection, data storage, data analysis and visualization management, we present a complete scheme. Through the agricultural big data system, the production cost is reduced and the production efficiency is improved.
2019 IEEE 5th International Conference on Computer and Communications (ICCC)
Xingqi Wang Kai Yang Tailian Liu
168170063
Designing and Implementing Data Warehouse for Agricultural Big Data
In recent years, precision agriculture that uses modern information and communication technologies is becoming very popular. Raw and semi processed agricultural data are usually collected through various sources, such as: Internet of Thing (IoT) sensors, satellites, weather stations, robots, farm equipment, farmers and agribusinesses, etc. Besides, agricultural datasets are very large, complex, unstructured, heterogeneous, non standardized, and inconsistent. Hence, the agricultural data mining is considered as Big Data application in terms of volume, variety, velocity and veracity. It is a key foundation to establishing a crop intelligence platform, which will enable resource efficient agronomy decision making and recommendations. In this paper, we designed and implemented a continental level agricultural data warehouse by combining Hive, MongoDB and Cassandra. Our data warehouse capabilities: (1) flexible schema; (2) data integration from real agricultural multi datasets; (3) data science and business intelligent support; (4) high performance; (5) high storage; (6) security; (7) governance and monitoring; (8) replication and recovery; (9) consistency, availability and partition tolerant; (10) distributed and cloud deployment. We also evaluate the performance of our data warehouse.
BigData Congress
Vu Minh Ngo Nhien-An Le-Khac M Tahar Kechadi