Machine Learning-Based Strawberry Disease Detection and Classification for Sustainable Smart Farming

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dc.contributor.advisorGéza, Husi
dc.contributor.authorToalombo Chicaiza, Inti Rumiñahui
dc.contributor.departmentDE--Műszaki Kar
dc.date.accessioned2025-09-04T15:15:42Z
dc.date.available2025-09-04T15:15:42Z
dc.date.created2025
dc.description.abstractThis thesis project explores machine learning-based solutions for detecting and classifying seven common strawberry diseases to promote sustainable smart farming in Ecuador. ​ Using advanced computer vision techniques, the study fine-tunes state-of-the-art models like YOLOv10, FR-DETR, and SAM2 on a dataset of 4,900 images collected from Ecuadorian fields and open-source platforms. ​ YOLOv10 achieved a mAP@0.5 of 0.95 and an F1-score of 0.94, demonstrating strong real-time detection capabilities, while FR-DETR attained a comparable mAP@0.5 of 0.961 but exhibited higher computational demands. ​ SAM2 was fine-tuned for precise segmentation of diseased areas, enhancing detection accuracy. ​ The research highlights YOLO's suitability for practical deployment due to its speed and efficiency, while FR-DETR's transformer-based architecture excels in global context modeling for larger diseased regions. ​ Challenges such as dataset imbalance and class-specific detection issues were identified, with recommendations for future improvements through data augmentation and hyperparameter tuning. ​ The findings underscore the potential of AI-driven tools in transitioning Ecuadorian strawberry farming from traditional pesticide-heavy methods to more sustainable practices. ​
dc.description.courseMechatronikai mérnöki
dc.description.degreeMSc/MA
dc.format.extent69
dc.identifier.urihttps://hdl.handle.net/2437/397242
dc.language.isoen_US
dc.rights.infoHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectMachine Learning
dc.subjectSmart Farming
dc.subjectComputer Vision
dc.subject.dspaceMűszaki tudományok
dc.titleMachine Learning-Based Strawberry Disease Detection and Classification for Sustainable Smart Farming
dc.title.translatedMaschinelles Lernen zur Erkennung und Klassifizierung von Erdbeerkrankheiten für eine nachhaltige, intelligente Landwirtschaft
dc.title.translatedDetección y Clasificación de Enfermedades de la Fresa Basadas en Aprendizaje Automático para una Agricultura Inteligente y Sostenible
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