Natural Language Processing for Extraction of Agricultural Information from Web Applications

In the past decades, with the rapid increase in population and demand for the global food supply needs, the adoption of innovative technologies in agricultural practices is found essential across the universe. The quintessential transformations of traditional agriculture to smart agriculture are becoming increasingly wide to meet the higher crop yield with all nutritious values. Research envisaged and found the emerging technological trends in innovative agricultural practice to attain more efficiency with the assistance of agricultural information systems from web applications. The intervention of Natural Language Processing (NLP) techniques drives the agricultural information system easily accessible to all through the internet.

Many developing countries are undergoing a dramatic revolution in various sectors with agricultural information systems resulting in economic development. It is a well-known fact that this information is commonly available to users with information data in text documents, spreadsheets, tables, and points concerning the crops, products, and services. The unique attributes of natural language processing enabling the practical analysis, delineation, and human language apprehension of agricultural statistical data are recognized as essential applications in smart agriculture. Automated extraction of statistical data with the help of natural language processing techniques supports many users such as cultivators, agricultural analysts, potential investors, wholesalers, and international traders. Natural language processing for the extraction of agricultural information is the solution for the various challenging tasks regarding innovative concepts for ontology causation and ontology inhabitants. Furthermore, NLP is gaining more critical for its significant role in decision-making and interaction with users through knowledge-gaining methods by understanding human speech efficiently. The main objective of NLP for agricultural information is to generate considerable interest in the information indulging selection of crops, agricultural health, utilization of fertilizers, and plant pathology, which helps develop sustainable farming practices, possibly to a greater extent.

 

Despite the advantages of natural language processing in progression with the extraction of the domain and semantic agricultural information from the web application, it also holds certain noticed limitations. Such limitations are complex data information, adaptable for only a specific environment, lack of user interface. Researchers and practitioners are most welcome to submit a research framework on having the proper knowledge to overcome the pitfalls of successfully extracting agricultural information from the internet. The special issue provides various opportunities to academicians to discuss this study program. List of potential topics of the special issue includes, but are not limited to the following:

• Future perspectives of natural language processing for gaining knowledge for the enhancement of the society.
• Trends and opportunities in natural language processing for précised agriculture.
• Limitations and challenges in implementing natural language processing for agricultural information systems.
• Natural language processing: fundamentals and applications.
• Enabling the intelligent agricultural system with natural language processing techniques for understanding agricultural information.
• Need for new policies and programs for gaining adequate knowledge through NLP to expertise agriculture.
• NLP for agricultural information: advantages and disadvantages.
• Benefits of agricultural information system from web applications.
• The emergence of technologies in the extraction of agricultural knowledge from the internet
• Contribution of web applications in establishing the farm
• The risk associated with complexity and misinterpretation of data in natural language processing.
• Frontiers and applications of natural language processing techniques for innovative agricultural practices.

Timeline for this Special Issue:

Submission Deadline: 08.02.2024

Notification to Author: 16.04.2024

Revised Version Submission: 08.07.2024

Final Acceptance: 16.09.2024

 

Guest Editor Information

Dr. Joseph Bamidele Awotunde

Department of Computer Science, Faculty of Information and Communication Sciences,

University of Ilorin, Ilorin 240003, Kwara State, Nigeria.

E-mail: [email protected], [email protected]

Google Scholar: https://scholar.google.com/citations?user=3hiPMRAAAAAJ&hl=en

ORCID: https://orcid.org/0000-0002-1020-4432

 

Dr. Akash Kumar Bhoi

Directorate of Research, Sikkim Manipal University, Gangtok, Sikkim, 737102, India.

E-mail: [email protected]

Google Scholar: https://scholar.google.com/citations?user=1pFjkMsAAAAJ&hl=en

ORCID: https://orcid.org/0000-0003-2759-3224

 

Dr. Paolo Barsocchi

Institute of Information Science and Technologies, National Research Council, 56124, Pisa, Italy.

E-mail: [email protected]

Google Scholar: https://scholar.google.com/citations?user=3oaaXWUAAAAJ&hl=en

ORCID: https://orcid.org/0000-0002-6862-7593

 

Submission guidelines:

The journal's Guide for Authors on how to prepare a paper is available at: https://qascf.com/index.php/qas/guidelines  

The APC is followed by the Journal

Papers must be submitted electronically via https://qascf.com/index.php/qas/login

To ensure that all manuscripts are correctly identified for inclusion in the special issue, it is important to select the Section “Extraction of agricultural information from web”.

Any inquiries regarding the content of papers should be submitted to:

Dr. Joseph Bamidele Awotunde: awotunde.jb@unilorin.edu.org, [email protected]