A Review on the Practice of Big Data Analysis in Agriculture

Big Data Analytics is a Data-Driven technology useful in generating significant productivity improvement in various industries by collecting, storing, managing, processing and analyzing various kinds of structured and unstructured data. The role of big data in Agriculture provides an opportunity to increase economic gain of the farmers by undergoing digital revolution in this aspect we examine through precision agriculture schemas equipped in many countries. This paper reviews the applications of big data to support agriculture. In addition it attempts to identify the tools that support the implementation of big data applications for agriculture services. The review reveals that several opportunities are available for utilizing big data in agriculture; however, there are still many issues and challenges to be addressed to achieve better utilization of this technology.

Annual Review of Resource Economics, 2018

Agriculture stands on the cusp of a digital revolution, and the same technologies that created the Internet and are transforming medicine are now being applied in our farms and on our fields. Overall, this digital agricultural revolution is being driven by the low cost of collecting data on everything from soil conditions to animal health and crop development along with weather station data and data collected by drones and satellites. The promise of these technologies is more food, produced on less land, with fewer inputs and a smaller environmental footprint. At present, however, barriers to realizing this potential include a lack of ability to aggregate and interpret data in such a way that it results in useful decision support tools for farmers and the need to train farmers in how to use new tools. This article reviews the state of the literature on the promise and barriers to realizing the potential for Big Data to revolutionize agriculture.

2018

Data are playing an important role making good planning and policies for agricultural growth and development. Population growth and climate change are worldwide trends that are increasing the importance of using big data science to improve agriculture. Add to that land degradation increasing marginal land and loss of biodiversity are better deals with study of big data science. Crop data can be break down into bits and bytes it will give better study about the crop development by using advance data analytics tools for betterment of agriculture. Here, talk about some important tools and techniques to handle and study the big data.

Journal of Agricultural & Food Information, 2019

In this paper, we provide a review of the research dedicated to applications of data science techniques, and especially machine learning techniques, in relevant agricultural systems. Big data technologies create new opportunities for data intensive decision-making. We review works in agriculture that employ the practice of big data analysis to solve various problems, which reveal opportunities and promising areas of use. The high volume and complexity of the data produced pose challenges in successfully implementing precision agriculture. Machine learning seems promising to cope with agricultural big data, but needs to reinvent itself to meet existing challenges.

IEEE Access

Sustainable agricultural development is a significant solution with fast population development through the use of information and communication (ICT) in precision agriculture, which produced new methods for making cultivation further productive, proficient, well-regulated while preserving the climate. Big data (machine learning, deep learning, etc.) is amongst the vital technologies of ICT employed in precision agriculture for their huge data analytical capabilities to abstract significant information and to assist agricultural practitioners to comprehend well farming practices and take precise decisions. The main goal of this article is to acquire an awareness of the Big Data latest applications in smart agriculture and be acquainted with related social and financial challenges to be concentrated on. This article features data creation methods, accessibility of technology, accessibility of devices, software tools, and data analytic methods, and appropriate applications of big data in precision agriculture. Besides, there are still a few challenges that come across the widespread implementation of big data technology in agriculture. INDEX TERMS Precision agriculture, big data analytics, machine learning, sustainable agriculture, smart farming, and digital agriculture.

Sustainability, 2021

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.

Precision Agriculture, 2020

Data-centric technology has not undergone widespread adoption in production agriculture but could address global needs for food security and farm profitability. Participants in the U.S. Department of Agriculture (USDA) National Institute for Food and Agriculture (NIFA) funded conference, "Identifying Obstacles to Applying Big Data in Agriculture," held in Houston, TX, in August 2018, defined detailed scenarios in which on-farm decisions could benefit from the application of Big Data. The participants came from multiple academic fields, agricultural industries and government organizations and, in addition to defining the scenarios, they identified obstacles to implementing Big Data in these scenarios as well as potential solutions. This communication is a report on the conference and its outcomes. Two scenarios are included to represent the overall key findings in commonly identified obstacles and solutions: "In-season yield prediction for real-time decision-making", and "Sow lameness." Common obstacles identified at the conference included error in the data, inaccessibility of the data, unusability of the data, incompatibility of data generation and processing systems, the inconvenience of handling the data, the lack of a clear return on investment (ROI) and unclear ownership. Less common but valuable solutions to common obstacles are also noted.

2018

Agriculture is the most significant sector in Nigeria in terms of employment and GDP. A number of the agricultural related business interest in Nigeria are constrained by a challenging business environment with defective and limited public infrastructure and overbearing government bureaucracies. Agricultural exports from Nigeria are not competitive because of phytosanitory concerns resulting from disease and pest attacks. Moreover, inefficient farming practices affects crop yield and increases waste in the production process. Dealing with these supply chain concerns can be complicated and overwhelming for industry players. As big data analytics tools are increasingly adopted for agricultural purposes and use cases in more developed countries, this study focuses on how it can be adopted in Nigeria and included in a holistic solution from a business interest perspective, to mitigate challenges and to become more competitive both locally and internationally.

International Journal of Advanced Computer Science and Applications, 2022

Agriculture is a typical contributor to the Egyptian economy, which could benefit from the comprehensive capabilities of Big Data (BD). In this work, we review the BD role in the agriculture sector in responding to two main questions: 1) Which technique, frameworks and data types were adopted. 2) Identification of the existing gap associated with the data sources, modeling, and analysis techniques. Therefore, the contribution in this paper can be outlined in three main aspects. 1) Popular BD frameworks were briefed, and a thorough comparison was conducted between them. 2) The potential data sources were described and characterized. 3) A Conceptual framework for Egyptian agriculture practice based on BD analytics was introduced. 4) Challenges and extensive recommendations have been provided, which could guide future development.

2021

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 rev...