Artificial Intelligence in Agricultural Mapping: A Review

Ramón Espinel (), Gricelda Herrera-Franco, José Luis Rivadeneira García and Paulo Escandón-Panchana
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Ramón Espinel: Rural Research Center (CIR), ESPOL Polytechnic University, Campus Gustavo Galindo Km 30.5 vía Perimetral, Guayaquil 090902, Ecuador
Gricelda Herrera-Franco: Faculty of Engineering Sciences, Universidad Estatal Península de Santa Elena UPSE, La Libertad 240204, Ecuador
José Luis Rivadeneira García: Unidad de Investigación, Desarrollo e Innovación, Instituto Nacional de Investigaciones Agropecuarias (INIAP), Quito 170518, Ecuador
Paulo Escandón-Panchana: Centre for Research and Projects Applied to Earth Sciences (CIPAT), Escuela Superior Politécnica del Litoral ESPOL, Guayaquil 09015863, Ecuador

Agriculture, 2024, vol. 14, issue 7, 1-36

Abstract: Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and time and increases efficiency in agricultural management activities, which improves the food industry. Agricultural mapping is necessary for resource management and requires technologies for farming challenges. The mapping in agricultural AI applications gives efficiency in mapping and its subsequent use in decision-making. This study analyses AI’s current state in agricultural mapping through bibliometric indicators and a literature review to identify methods, agricultural resources, geomatic tools, mapping types, and their applications in agricultural management. The methodology begins with a bibliographic search in Scopus and the Web of Science (WoS). Subsequently, a bibliographic data analysis and literature review establish the scientific contribution, collaboration, AI methods, and trends. The United States (USA), Spain, and Italy are countries that produce and collaborate more in this area of knowledge. Of the studies, 76% use machine learning (ML) and 24% use deep learning (DL) for agricultural mapping applications. Prevailing algorithms such as Random Forest (RF), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs) correlate mapping activities in agricultural management. In addition, AI contributes to agricultural mapping in activities associated with production, disease detection, crop classification, rural planning, forest dynamics, and irrigation system improvements.

Keywords: agricultural cartography; agricultural productivity; efficiency; intellectual structure; sustainability (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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