Melon Growth Detection Strategy Using Artificial Intellegence in Greenhouse Cultivation

The process of cultivating rock melons can be broken down into five key stages: germination, transplanting, flowering, fruiting, and harvesting. Rock melon plants are known for their rapid growth, which can vary depending on the quality of the seeds and the level of maintenance provided. Consequently, a substantial number of resources, including manpower, is necessary to monitor the growth of these plants, particularly in large-scale farming operations. Therefore, it is essential to implement a comprehensive system for tracking the growth characteristics of these trees. In this study, we introduce YOLO, an image processing model designed to categorize rock melon plants according to their growth stage. To ensure the reliability of this model, we conducted accuracy validation, comparing the manually calculated total number of flowers with those detected by the YOLO model. Subsequently, the results obtained from this detection process will be used to analyze the growth patterns of rock melons during the flowering and fruiting stages, providing valuable insights into the overall health and conditions of the plants.