Escobedo Cabello, Jesús ArturoMendez Meraz, Armando Enrico2025-06-182025Mendez Meraz, A. E. (2025). Near-infrared-based capsicum counting algorithm using YOLO11. [Tesis de maestría] Instituto Tecnológico y de Estudios Superiores de Monterrey. Recuperado de: https://hdl.handle.net/11285/703761https://hdl.handle.net/11285/703761This 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.TextoengopenAccesshttp://creativecommons.org/licenses/by/4.0INGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS::TECNOLOGÍA DE LOS ORDENADORES::LENGUAJES ALGORÍTMICOSTechnologyTesis de maestríaPeppersYOLOFruit detectionFruit countingYield estimationCapsicums1314069