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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Christian Ovalle
Universidad Tecnologica del Perú
Computer Science & IT Electrical & Electronics Engineering Engineering

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Comparing global and variety-specific ensemble models for avocado maturity prediction with near-infrared Christian Ovalle; Jhon Garcia Jimenez; Jose Briones Zuñiga
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2300-2313

Abstract

Ensuring accurate, non-destructive maturity classification of avocados is critical to supply chain optimization in agro-industrial systems. This study presents a predictive framework that integrates near-infrared (NIR) spectroscopy with ensemble stacking machine learning (ML) to enhance the precision of avocado ripeness assessment. The proposed methodology compares global versus variety-specific models for 'Hass' and 'Fuerte' avocado types, leveraging spectral data (900–1,700 nm) and multiple base classifiers, including random forest (RF), gradient boosting (GB), support vector machines (SVMs), decision trees (DT), k-nearest neighbors (KNN), and categorical boosting (CatBoost), combined via linear regression as a meta-learner. Experimental results revealed that the stacking models outperformed individual learners, with variety-specific GB model achieving the highest performance (Matthews correlation coefficient (MCC) =0.679, area under the curve (AUC) =0.931). These findings highlight the critical importance of varietal specificity in model calibration and demonstrate how ensemble strategies can improve robustness, scalability, and interpretability in intelligent agricultural systems. The proposed model provides a computationally efficient solution for real-time quality control and supports the deployment of AI-powered systems within agricultural supply chains in developing regions.
Sustainable greenhouse using IoT and machine learning to optimize the microclimate for lettuce cultivation Rudy Ivan Jamjachi Yauri; Jorge Raul Herbozo Ramirez; Christian Ovalle
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.9877

Abstract

Sustainable agriculture faces increasing challenges due to climate variability, which affects crop productivity and resource efficiency. This study proposes a sustainable greenhouse system that integrates internet of things (IoT) sensors and machine learning models to optimize the microclimate for lettuce cultivation. Environmental data, including temperature, humidity, and light intensity, were collected through IoT sensors and processed using machine learning algorithms, specifically neural networks and support vector machines (SVM), implemented on the Orange data mining platform. The results indicate that the neural network model achieved superior performance, reaching an accuracy of 99.99% in predicting optimal greenhouse climate conditions, outperforming the SVM model. The best-performing model was subsequently implemented on an Arduino-based IoT system to automatically regulate greenhouse conditions. The proposed system improved resource efficiency and supported optimal lettuce growth while promoting sustainable agricultural practices. These findings demonstrate that integrating IoT and machine learning can enhance greenhouse management, contributing to climate-resilient agriculture and improved food production systems.
Co-Authors Jhon Garcia Jimenez Jorge Raul Herbozo Ramirez Jose Briones Zuñiga Rudy Ivan Jamjachi Yauri