Ayberguler, AzadArslan, EnisKayaarma, Selma Yilmazyildiz2026-02-122026-02-1220239798350306590https://doi.org/10.1109/ASYU58738.2023.10296697https://hdl.handle.net/20.500.12885/65362023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 2023-10-11 through 2023-10-13 -- Sivas -- 194153Strawberry cultivation can be susceptible to unforeseen diseases. For the prevention of Powdery Mildew and Gray Mold diseases prompt pesticide applications should be carried out during the disease development or periodically, aligned with strawberries' growth stages. In this study, a deep learning-based growth stage analysis and disease detection solution was developed. Three versions of the YOLO architecture (YOLOv5, YOLOv3, YOLOv3-tiny) have been trained on a dataset that was enhanced for this specific use case. YOLOv3-tiny version was also deployed on a simple unmanned ground vehicle in a real-life strawberry cultivation greenhouse. This study differs from others in the literature by training and deploying a model that would enable the detection of both powdery mildew disease and the growth stages of strawberries in a single model. With the deployment of this model, the strawberry growers can implement the appropriate spraying strategies that would control and prevent the formation and spread of these diseases. © 2023 IEEE.enComputer VisionGrowth and Disease AnalysisStrawberryYOLOConference Object10.1109/ASYU58738.2023.102966972-s2.0-85178307289N/A