Ciencias Exactas y Ciencias de la Salud

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Near-infrared-based capsicum counting algorithm using YOLO11
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025) Mendez Meraz, Armando Enrico; Escobedo Cabello, Jesús Arturo; emimmayorquin; Cantoral Ceballos, Jose Antonio; School of Engineering and Sciences; Campus Monterrey; Gómez Espinosa, Alfonso
This work presents a novel near-infrared-based approach to capsicum counting in greenhouses that uses the advantages of NIR imaging to enhance detection in challenging lighting condi- tions. The proposed algorithm integrates the YOLO11 detection model for capsicum iden-tification and the BoT-SORT multi-object tracker to track detections across a video stream, enabling accurate fruit counting. Trained on a dataset of 611 labeled images captured in a greenhouse, the detection model achieved an F1-score of 0.82, while the tracker obtained a multi-object tracking accuracy (MOTA) of 0.85. The results demonstrate the effectiveness of this NIR-based approach in automating fruit counting in greenhouse environments, offering potential applications in yield estimation.