Maturity recognition and fruit counting for sweet peppers in greenhouses using deep Learning neural networks

Gómez Espinosa, AlfonsoViveros Escamilla, Luis David2025-10-112024-01-05Viveros Escamilla, L. D. (2024). Maturity recognition and fruit counting for sweet peppers in greenhouses using deep Learning neural networks [Tesis maestría]. Instituto Tecnológico y de Estudios Superiores de Monterrey. Recuperado de: https://hdl.handle.net/11285/704273https://hdl.handle.net/11285/704273https://orcid.org/0000-0001-5657-380XThis study presents an approach to address the challenges involved in recognizing the maturity stage and counting sweet peppers of varying colors (green, yellow, orange, and red) within greenhouse environments. The methodology leverages the YOLOv5 model for real-time object detection, classification, and localization, coupled with the DeepSORT algorithm for efficient tracking. The system was successfully implemented to monitor sweet pepper production, and some challenges related to this environment, namely occlusions and the presence of leaves and branches, were effectively overcome. The algorithm was evaluated using real-world data collected in a sweet pepper greenhouse. A dataset comprising 1863 images was meticulously compiled to enhance the study, incorporating diverse sweet pepper vari eties and maturity levels. Additionally, the study emphasized the role of confidence levels in object recognition, achieving a confidence level of 0.973. Furthermore, the DeepSORT algo rithm was successfully applied for counting sweet peppers, demonstrating an accuracy level of 85.7% in two simulated environments under challenging conditions, such as varied lighting and inaccuracies in maturity level assessment.TextoengopenAccesshttp://creativecommons.org/licenses/by/4.0INGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS::TECNOLOGÍA DE LOS ORDENADORES::INTELIGENCIA ARTIFICIALINGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS::TECNOLOGÍA DE LOS ORDENADORES::SISTEMAS AUTOMATIZADOS DE PRODUCCIÓNCIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA::CIENCIAS AGRARIAS::HORTICULTURA::PRODUCCIÓN DE CULTIVOSCIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA::MATEMÁTICAS::CIENCIA DE LOS ORDENADORES::CONTROL DE INVENTARIOSCIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA::MATEMÁTICAS::CIENCIA DE LOS ORDENADORES::LENGUAJES ALGORÍTMICOSScienceTechnologyAgricultureMaturity recognition and fruit counting for sweet peppers in greenhouses using deep Learning neural networksTesis de Maestría / master Thesishttps://orcid.org/0009-0006-7161-4562Yield estimationSweet peppersPrecision agricultureComputer visionDeep learningMaturity detection1239363