About this Research Topic
Currently, agriculture is at a crossroads. There has been an increase in the world’s population, a reduction in available farmland as well as competition for agricultural land from biofuels. Advances from traditional agricultural areas have been resisted by consumers and politicians, and consequently increases in productivity need to come from non-traditional areas to ensure that the world’s population has access to basic nutrition at an affordable price. The Semantic and Natural Language Processing (NLP) community can assist the agricultural domain by providing unique insights from data or by providing greater clarity to current agricultural processes.
Agricultural researchers, in common with other domains, have access to large collections of agricultural documents such as scientific papers, news, social media data, etc. These textual documents can be analyzed and processed with NLP methods, supported by semantic knowledge, to resolve agricultural issues in digital agriculture.
To date, the application of text mining and semantics in the agricultural domain remains under-explored. This Research Topic invites original research, surveys, and position papers that address issues in Agricultural Text Mining or Agri Semantics, in order to increase the visibility and application potential of this important and emerging research area. The scope of this article collection is broad and seeks submissions on, but not limited to:
- novel agricultural NLP methods
- multilingual agricultural text mining
- agricultural information retrieval
- agricultural information extraction
- agricultural named entity recognition and disambiguation
- agricultural text visualization
- NLP agricultural applications
- societal impacts of agricultural text mining, language resources, and datasets
- agricultural web crawling
- novel agrisemantic resources
- bias or gaps in existing agrisemantic resources
- agrisemantic data integration
- novel argisemantic backed agricultural applications
- and agribusiness industry case studies
Agricultural researchers, in common with other domains, have access to large collections of agricultural documents such as scientific papers, news, social media data, etc. These textual documents can be analyzed and processed with NLP methods, supported by semantic knowledge, to resolve agricultural issues in digital agriculture.
To date, the application of text mining and semantics in the agricultural domain remains under-explored. This Research Topic invites original research, surveys, and position papers that address issues in Agricultural Text Mining or Agri Semantics, in order to increase the visibility and application potential of this important and emerging research area. The scope of this article collection is broad and seeks submissions on, but not limited to:
- novel agricultural NLP methods
- multilingual agricultural text mining
- agricultural information retrieval
- agricultural information extraction
- agricultural named entity recognition and disambiguation
- agricultural text visualization
- NLP agricultural applications
- societal impacts of agricultural text mining, language resources, and datasets
- agricultural web crawling
- novel agrisemantic resources
- bias or gaps in existing agrisemantic resources
- agrisemantic data integration
- novel argisemantic backed agricultural applications
- and agribusiness industry case studies
Keywords: nlp, agriculture, Text Mining, Information Extraction, Agricultural Applications, Web Crawling, Agrisemantic, Natural Language Processing, Semantic
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
