Currently, agriculture and livestock management are 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 and livestock researchers, in common with other domains, have access to large collections of 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 issues in digital agriculture and livestock management.

To date, the application of text mining and semantics in the agricultural domain remains underexplored. 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 workshop is broad and seeks submissions on, but not limited to:

• Novel NLP methods for agriculture and/or livestock management

• Precision agriculture

• Multilingual agricultural and/or livestock text mining

• Agricultural and/or livestock management information retrieval

• Agricultural and/or livestock management information extraction

• Multi-modal Agricultural and/or livestock information extraction

• Agricultural/livestock named entity recognition and disambiguation

• Agricultural/livestock text visualization

• NLP agricultural/livestock applications

• Societal impacts of agricultural/livestock text mining, language resources, and datasets

• Agricultural/Livestock Image, Audio and Video Processing

• Computer Vision Applications on Agriculture and/or livestock management

• Neural Network Applications on Agriculture Image, Audio, Video

• Agricultural/livestock web crawling

• Agricultural/livestock ontologies

• Agricultural data analytics

• Agricultural observations and recommendations

• Novel agrisemantic resources

• Bias or gaps in existing agrisemantic resources

• Agrisemantic data integration

• Novel argisemantic backed agricultural applications and agribusiness industry case studies