The Implementation of a Practical Agricultural Big Data System

Wang, Xingqi; Yang, Kai; Liu, Tailian

doi:10.1109/iccc47050.2019.9064475

Cited by 7 publications

(19 citation statements)

References 10 publications

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“…Among these the following stand out: Apache Hive and Apache Kafka. Apache Hive was used to configure data warehouses that streamline the process of working with large data sets that were stored in distributed units [58], [59]. Apache Kafka was used for the transmission of information or messages to different nodes of the designed Big Data architecture [59], [41].…”

Section: Agricultural Big Data Technologiesmentioning

confidence: 99%

“…Apache Kafka was used for the transmission of information or messages to different nodes of the designed Big Data architecture [59], [41]. The most widely used technology, Apache Spark framework, was mainly used for processing the collected data [44], [58], [34], [59], [54], [42].…”

Section: Agricultural Big Data Technologiesmentioning

confidence: 99%

“…In Wang (2019), a Big Data system for agriculture is proposed, designed based on the collection, storage, analysis and application of pear tree data [58]. For the collection of tree growth data (air temperature, soil moisture, light intensity, etc.…”

Section: Agricultural Big Data Technologiesmentioning

confidence: 99%

See 2 more Smart Citations

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

Cravero¹,

Pardo²,

Sepúlveda³

et al. 2022

Preprint

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Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address different problems such as farmers' decision-making, crops, weeds, animal research, land, food availability and security, weather, and climate change. The purpose of this paper is to synthesize the evidence regarding the challenges involved in implementing machine learning in Agricultural Big Data. We conducted a Systematic Literature Review applying the PRISMA protocol. This review includes 30 papers, published from 2015 to 2020. We develop a framework that summarizes the main challenges encountered, the use of machine learning techniques, as well as the main technologies used. A major challenge is the design of Agricultural Big Data architectures, due to the need to modify the set of technologies adapting the machine learning techniques, as the volume of data increases.

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Section: Agricultural Big Data Technologiesmentioning

confidence: 99%

Section: Agricultural Big Data Technologiesmentioning

confidence: 99%

See 1 more Smart Citation

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

Cravero¹,

Pardo²,

Sepúlveda³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…(b) Application: 13.5% of the analyzed works describe the development of an application. Wang, Yang and Liu [71], used IoT technology, Big Data and ML algorithms, to create an application that includes data collection, data storage, data analysis and visualization management, we present a complete scheme. Through the agricultural Big Data system, they reduce the cost of production and improve production efficiency.…”

Section: Answer For Rq4: What Is the Area Of Agriculture In Which Big Data Machine Learning Is Used?mentioning

confidence: 99%

Use and Adaptations of Machine Learning in Big Data—Applications in Real Cases in Agriculture

Cravero

Sepúlveda

2021

Electronics

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The data generated in modern agricultural operations are provided by diverse elements, which allow a better understanding of the dynamic conditions of the crop, soil and climate, which indicates that these processes will be increasingly data-driven. Big Data and Machine Learning (ML) have emerged as high-performance computing technologies to create new opportunities to unravel, quantify and understand agricultural processes through data. However, there are many challenges to achieve the integration of these technologies. It implies making some adaptations to ML for using it with Big Data. These adaptations must consider the increasing volume of data, its variety and the transmission speed issues. This paper provides information on the use of Big Data and ML for agriculture, identifying challenges, adaptations and the design of architectures for these systems. We conducted a Systematic Literature Review (SLR), which allowed us to analyze 34 real cases applied in agriculture. This review may be of interest to computer or data scientists and electronic or software engineers. The results show that manipulating large volumes of data is no longer a challenge due to Cloud technologies. There are still challenges regarding (1) processing speed due to little control of the data in its different stages, raw, semi-processed and processed data (value data); (2) information visualization systems, which support technical data little understood by farmers.

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“…Ngo et al [11] designed and implemented a BDW for agricultural data using Hive, MongoDB, and Cassandra. In the same domain, Wang et al [23] developed and implemented an end to end system for farm management based on HDFS, Spark, Hive, and Hbase. Doreswamy et al [24] used a hybrid DW model with an OLTP system and Hadoop to develop a meteorological DW using a star schema.…”

Section: Related Workmentioning

confidence: 99%

Advancing Logistics 4.0 with the Implementation of a Big Data Warehouse: A Demonstration Case for the Automotive Industry

et al. 2021

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The constant advancements in Information Technology have been the main driver of the Big Data concept’s success. With it, new concepts such as Industry 4.0 and Logistics 4.0 are arising. Due to the increase in data volume, velocity, and variety, organizations are now looking to their data analytics infrastructures and searching for approaches to improve their decision-making capabilities, in order to enhance their results using new approaches such as Big Data and Machine Learning. The implementation of a Big Data Warehouse can be the first step to improve the organizations’ data analysis infrastructure and start retrieving value from the usage of Big Data technologies. Moving to Big Data technologies can provide several opportunities for organizations, such as the capability of analyzing an enormous quantity of data from different data sources in an efficient way. However, at the same time, different challenges can arise, including data quality, data management, and lack of knowledge within the organization, among others. In this work, we propose an approach that can be adopted in the logistics department of any organization in order to promote the Logistics 4.0 movement, while highlighting the main challenges and opportunities associated with the development and implementation of a Big Data Warehouse in a real demonstration case at a multinational automotive organization.

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The Implementation of a Practical Agricultural Big Data System

Cited by 7 publications

References 10 publications

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

Use and Adaptations of Machine Learning in Big Data—Applications in Real Cases in Agriculture

Advancing Logistics 4.0 with the Implementation of a Big Data Warehouse: A Demonstration Case for the Automotive Industry

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