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Article

Contribution of Artificial Neural Networks (ANNs) in Analyzing and Modeling Phenological Synchronization of Fig and Caprifig in Northern Morocco

1
TEDAEEP Team Research, Abdelmalek Essaadi University–(UAE-FPL), Larache 93004, Morocco
2
Institute of Olive Tree, Subtropical Crops and Viticulture (IOSV), Hellenic Agricultural Organization (ELGO-Dimitra), 14123 Athens, Greece
*
Author to whom correspondence should be addressed.
Horticulturae 2025, 11(10), 1235; https://doi.org/10.3390/horticulturae11101235 (registering DOI)
Submission received: 25 August 2025 / Revised: 1 October 2025 / Accepted: 10 October 2025 / Published: 13 October 2025
(This article belongs to the Section Fruit Production Systems)

Abstract

The Mediterranean fig (Ficus carica L.) is a dioecious fruit tree of high nutritional and economic value in the Mediterranean basin. In northern Morocco, phenological desynchronization between male and female fig trees limits pollination and production. This study aimed to characterize the phenological stages of indigenous fig and caprifig varieties using the BBCH scale and to evaluate the predictive capacity of artificial neural networks (ANNs). This study was conducted in the Bni Ahmed region over two consecutive years (2021 and 2022) at two sites. At each site, a total of 80 female fig trees were selected. Caprifig trees were selected in accordance with their availability (37 trees/site 1; 24 trees/site 2). Local meteorological data were incorporated into the analysis to evaluate the influence of climatic conditions on phenological stages. Our results revealed significant effects of temperature, humidity, and rainfall on phenological dynamics, along with a clear inter-varietal variability and pronounced desynchronization between male and female fig trees. Early-ripening caprifig varieties showed limited pollination efficiency, whereas late-ripening varieties were better synchronized with the longer receptivity period of female fig trees. Importantly, the ANN model demonstrated exceptional predictive performance (R2 up to 0.985, RMSE < 1 day), serving as a robust and practical tool for forecasting key phenological stages and minimizing potential yield losses. These findings demonstrate the value of combining phenological monitoring with AI-based modeling to improve adaptive management of fig orchards under Mediterranean climate change. This is the first study in Morocco to implement such an integrated approach to fig and caprifig trees.
Keywords: Ficus carica L.; caprifig; fig tree; phenology; ANN; artificial intelligence Ficus carica L.; caprifig; fig tree; phenology; ANN; artificial intelligence

Share and Cite

MDPI and ACS Style

Chmarkhi, A.; El Fatehi, S.; Mehdi, I.; Benziane, W.; Dihaz, N.; El Khatib, K.; Kapazoglou, A.; Hmimsa, Y. Contribution of Artificial Neural Networks (ANNs) in Analyzing and Modeling Phenological Synchronization of Fig and Caprifig in Northern Morocco. Horticulturae 2025, 11, 1235. https://doi.org/10.3390/horticulturae11101235

AMA Style

Chmarkhi A, El Fatehi S, Mehdi I, Benziane W, Dihaz N, El Khatib K, Kapazoglou A, Hmimsa Y. Contribution of Artificial Neural Networks (ANNs) in Analyzing and Modeling Phenological Synchronization of Fig and Caprifig in Northern Morocco. Horticulturae. 2025; 11(10):1235. https://doi.org/10.3390/horticulturae11101235

Chicago/Turabian Style

Chmarkhi, Abdelhalim, Salama El Fatehi, Imane Mehdi, Widad Benziane, Nouhaila Dihaz, Khaoula El Khatib, Aliki Kapazoglou, and Younes Hmimsa. 2025. "Contribution of Artificial Neural Networks (ANNs) in Analyzing and Modeling Phenological Synchronization of Fig and Caprifig in Northern Morocco" Horticulturae 11, no. 10: 1235. https://doi.org/10.3390/horticulturae11101235

APA Style

Chmarkhi, A., El Fatehi, S., Mehdi, I., Benziane, W., Dihaz, N., El Khatib, K., Kapazoglou, A., & Hmimsa, Y. (2025). Contribution of Artificial Neural Networks (ANNs) in Analyzing and Modeling Phenological Synchronization of Fig and Caprifig in Northern Morocco. Horticulturae, 11(10), 1235. https://doi.org/10.3390/horticulturae11101235

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