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REVIEW article

Front. Agron.
Sec. Climate-Smart Agronomy
Volume 6 - 2024 | doi: 10.3389/fagro.2024.1482893

Dynamic perspectives into Tropical fruit production: A review of Modeling techniques

Provisionally accepted
Daniel Mancero-Castillo Daniel Mancero-Castillo 1*Yoansy García Yoansy García 2Maritza Aguirre-Munizaga Maritza Aguirre-Munizaga 1Daniel Ponce De Le Ón Daniel Ponce De Le Ón 3Diego Portalanza Diego Portalanza 1Jorge Avila-Santamaria Jorge Avila-Santamaria 4
  • 1 Agrarian University of Ecuador, Guayaquil, Ecuador
  • 2 Research Institute, Agrarian University of Ecuador, Guayaquil, Guayas, Ecuador
  • 3 Faculty of Agrarian Sciences, State University of Santa Elena Peninsula, Santa Elena, Ecuador
  • 4 Universidad San Francisco de Quito, Quito, Pichincha, Ecuador

The final, formatted version of the article will be published soon.

    Modeling the intricate interactions between fruit trees, their environments, soils, and economic factors continues to be a significant challenge in agricultural research globally, requiring a multidisciplinary approach. Despite advances in agricultural technology and algorithms, significant knowledge gaps persist in understanding and modeling these interactions. This review explores basic concepts related to modeling for tropical fruit production. It explains modeling development from sensor technologies, image analysis, databases, and algorithms for decision support systems while considering climate changes or edaphoclimatic limitations. We report the current fruit modeling tendencies showing a significant increase in publications on these topics starting in 2021, driven by the need for sustainable solutions and access to large agricultural databases.This study emphasizes inherent challenges in tropical fruit modeling, such as fruit tree cycles, costly and time-consuming experimentation, and the lack of standardized data. These limitations are evident in tropical fruit, where few models have been reported or validated for cocoa, avocado, durian, dragonfruit, banana, mango, or passion fruit. This study analyzes the classification of the algorithms related to tropical fruit into three main categories: supervised, unsupervised, and reinforcement learning, each with specific applications in agricultural management optimization.Crop classification and yield prediction use supervised models like neural networks and decision trees. Unsupervised models, like K-Means clustering, allow pattern identification without prior

    Keywords: Fruit modeling, Crop modeling, Agricultural data, Tropical Climate, Tropical agriculture

    Received: 19 Aug 2024; Accepted: 26 Nov 2024.

    Copyright: © 2024 Mancero-Castillo, García, Aguirre-Munizaga, Ponce De Le Ón, Portalanza and Avila-Santamaria. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Daniel Mancero-Castillo, Agrarian University of Ecuador, Guayaquil, Ecuador

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.