Big Data Challenges and Opportunities in Agriculture

Gopal, P S Maya; Chintala, Bhargavi Renta

doi:10.4018/ijaeis.2020010103

Cited by 28 publications

(17 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Big Data technology provides a solution for effective data mining. Big Data refers to the technology of analyzing, processing and storing data, which is s very important technology of mass data processing [41]. Data mining is one aspect of Big Data application, and can be divided into 4 steps: data pre-processing, data reduction, data modeling and solution analysis [42].…”

Section: Big Data: Data-driven Unmanned Farmmentioning

confidence: 99%

From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production

Wang

et al. 2021

Agriculture

View full text Add to dashboard Cite

Agriculture is the most important industry for human survival and solving the hunger problem worldwide. With the growth of the global population, the demand for food is increasing, which needs more agriculture labor. However, the number of people willing to engage in agricultural work is decreasing, causing a severe shortage of agricultural labor. Therefore, it is necessary to study the mode of agricultural production without labor force participation. With the rapid development of the Internet of Things, Big Data, artificial intelligence, robotics and fifth-generation (5G) communication technology, robots can replace humans in agricultural operations, thus enabling the establishment of unmanned farms in the near future. In this review, we have defined unmanned farms, introduced the framework of unmanned farms, analyzed the current state of the technology and how these technologies can be used in unmanned farms, and finally discuss all the technical challenges. We believe that this review will provide guidance for the development of unmanned farms and provide ideas for further investigation of these farms.

show abstract

Section: Big Data: Data-driven Unmanned Farmmentioning

confidence: 99%

From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production

Wang

et al. 2021

Agriculture

View full text Add to dashboard Cite

show abstract

“…For offering sustainable agricultural production, it is necessary to use cutting-edge technologies such as Blockchain, IoT, Big Data, Machine Learning (ML), among others [3], [4]. Data-driven agriculture through these technologies is the most promising approach to solve current and future issues.…”

Section: Introductionmentioning

confidence: 99%

“…To improve these aspects, the mechanism used in smart farming is ML, the scientific field that affords a machine the ability to learn without much programming. It has arisen along with Big Data technologies and high-performance computing to create new opportunities to facilitate, quantify and understand the intensive data processes in agricultural operating environments [4].…”

Section: Introductionmentioning

confidence: 99%

“…The developments indicate that agriculture can benefit from ML at every stage, such as species management, field management, crop management, and livestock management [4]. ML is used in a range of agricultural applications including yield prediction algorithms, image recognition algorithms, and robotics to harvest different types of specialty crops [8].…”

Section: Introductionmentioning

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

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.

show abstract

“…The expected outcomes of digital agriculture are sometimes overstated in the scientific literature [9]. Given the relative novelty of the concept of big data, quantitative evidence on the benefits and intricacies of agricultural big data and knowledge about their impact on research and development is still emerging [13,14]. This article provides insights from a panel of international experts on how big data are currently used in their work environment, what related benefits in the medium term they expect, what issues are likely to arise in the data-sharing process.…”

Section: Introductionmentioning

confidence: 99%

Expert Insights on the Impacts of, and Potential for, Agricultural Big Data

et al. 2021

View full text Add to dashboard Cite

Advanced digital technologies are rapidly permeating agriculture from laboratory to field. Machine-based breeding, robotics and big data technologies have deeply transformed not only production systems but also the way scientific research is conducted. How are digital applications revolutionizing people’s jobs and skills? What are the challenges and opportunities for managing and sharing agricultural big data? This article addresses these and other questions by surveying international experts in plant biotechnology. Results show that digital innovations in the form of decision-support tools are perceived as promising. Most surveyed experts anticipate the deployment of big data analytics and artificial intelligence to boost agricultural productivity. Another key finding is that substantial physical investment, specialized human capital and effective data governance are critical to successful implementation of technological innovations associated with big data.

show abstract

Big Data Challenges and Opportunities in Agriculture

Cited by 28 publications

References 71 publications

From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production

From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production

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

Expert Insights on the Impacts of, and Potential for, Agricultural Big Data

Contact Info

Product

Resources

About