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A Hybrid Voting Ensemble Model for the Efficient Sorting and Classification of Date Fruit Varieties
Un modelo híbrido de votación por conjunto para la clasificación y ordenamiento eficiente de variedades de dátiles
DOI:
https://doi.org/10.15446/ing.investig.118469Keywords:
artificial intelligence, classification, machine learning, smart agriculture, date fruits (en)inteligencia artificial, clasificación, aprendizaje automático, agricultura inteligente, dátiles (es)
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Dates constitute one of Algeria's most important agricultural products, given their substantial health and economic advantages. Furthermore, they represent a vital export item beyond the hydrocarbon industry. The existing conventional techniques for classifying and sorting dates are ineffective, time-consuming, and labor-intensive, leading to a discrepancy between restricted exports and elevated production levels. This work presents an ensemble learning (EL) model that utilizes transfer learning (TL) strategies to overcome obstacles and improve date fruit classification. We assess the efficacy of four classifiers, i.e., MobileNetV2, EfficientNet, DenseNet201, and an ensemble soft voting classifier that employs TL, with a dataset of 1619 photos representing 20 distinct varieties of Algerian dates. The dataset used is one of the greatest benchmarks for varietal diversity. The suggested hybrid model exhibits exceptional performance, with a validation accuracy of 99.07% and a classification accuracy of 99.93%. It establishes a new benchmark in agricultural technology by exceeding all assessed models in terms of precision, recall, and F1-score. These findings demonstrate the potential of this approach to completely transform date sorting and markedly improve agricultural production and efficiency.
Los dátiles constituyen uno de los productos agrícolas más importantes de Argelia debido a sus significativos beneficios para la salud y la economía. Además, representan un artículo de exportación vital más allá de la industria de los hidrocarburos. Las técnicas convencionales que existen para clasificar y ordenar dátiles son ineficaces, consumen mucho tiempo y requieren mucha mano de obra, lo que genera una discrepancia entre las exportaciones limitadas y los elevados niveles de producción. Este trabajo presenta un modelo de aprendizaje en conjunto (EL) que utiliza estrategias de aprendizaje por transferencia (TL) para superar obstáculos y mejorar la clasificación de frutos de dátiles. Evaluamos la eficacia de cuatro clasificadores, i.e., MobileNetV2, EfficientNet y DenseNet201, y un clasificador de votación suave en conjunto que emplea TL, utilizando un conjunto de datos de 1619 imágenes que representan 20 variedades distintas de dátiles argelinos. El conjunto de datos utilizado es uno de los mayores referentes en diversidad varietal. El modelo híbrido propuesto presenta un rendimiento excepcional, con una precisión de validación del 99.07 % y una precisión de clasificación del 99.93 %, y establece un nuevo referente en la tecnología agrícola al superar a todos los modelos evaluados en términos de precisión, sensibilidad y puntuación F1. Estos hallazgos demuestran el potencial de este enfoque para transformar por completo la clasificación de dátiles y mejorar notablemente la producción y eficiencia agrícola.
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1. Ilvico Sonata, Yulyani Arifin. (2025). Deep Learning Approach for Palm Fruit Ripeness Classification Using MobileNet. 2025 4th International Conference on Creative Communication and Innovative Technology (ICCIT). , p.1. https://doi.org/10.1109/ICCIT65724.2025.11167699.
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Copyright (c) 2025 Sofiane Abden, Mostefa Bendjima, Soumia Benkrama
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