Prediction Model for Soybean Productivity

Ion Ganea
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Ion Ganea: Moldova State University, Chisinau, Republic of Moldova

Database Systems Journal, 2023, vol. 14, issue 1, 1-15

Abstract: This paper presents a holistic approach to biological and agricultural research focused on the use of interconnected technologies in the context of climate change. Researchers from different countries have analyzed how smart technologies can help agriculture adapt to these changes. The most representative works in the field are analyzed. Among these technologies are graph database systems such as Neo4j, which have demonstrated success in predicting the studied phenomena. The paper describes the development of a soybean crop productivity prediction model using monthly and annual data of meteorological phenomena such as precipitation, air temperature, hydrothermal coefficient, soil moisture, and others. Some of the results of this promising research are also presented.

Keywords: Holistic; Knowledge; Models; Prediction; Graph; Neo4j; Graph Data Science (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:aes:dbjour:v:14:y:2023:i:1:p:1-15

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