Understanding the Agribusiness Model and Agricultural Value Chain with AI-Driven Technologies

Adrian Stancu () and Cosmina-Mihaela Rosca ()
Additional contact information
Adrian Stancu: Petroleum-Gas University of Ploiesti
Cosmina-Mihaela Rosca: Petroleum-Gas University of Ploiesti

A chapter in Agripreneurship and the Dynamic Agribusiness Value Chain, 2024, pp 329-344 from Springer

Abstract: Abstract The importance of the agriculture sector in national economies highlights its deep impact on ensuring food for people. The agribusiness model has some particular traits as compared to traditional models concerning the biological nature of the products, product seasonality, uncertainty of environmental and biological factors, types and sizes of firms involved, market conditions, location of agribusinesses, and government involvement. Every single agri-food product has its value chain. However, the main stages of the agricultural value chain are production, processing, marketing and distribution, retailing, and consumption. Nowadays, the activities of the agribusiness model and agricultural value chain (AMAVC) integrate modern technologies such as artificial intelligence (AI). At first, the current implementations of machine learning (ML), natural language processing (NLP), computer vision (CV), and expert systems (ES) in AMAVC were underscored by providing the results of the main research. In addition, future application proposals for AI in AMAVC were emphasized. Finally, we designed the conceptual steps of the ML process adapted for AMAVC and exemplified its application for a farm that seeks to improve the yield of corn crops and for a company that aims to optimize the distribution of oranges. The integration of advanced technologies in the activities of agribusinesses will enhance productivity and sustainability.

Keywords: Agribusiness model; Agricultural value chain; Artificial intelligence; Machine learning (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-97-7429-6_19

Ordering information: This item can be ordered from
http://www.springer.com/9789819774296

DOI: 10.1007/978-981-97-7429-6_19

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().