Biosensing method of growth diagnosis in the forced culture of strawberries ―Development of crop-identification algorithms―

An image-processing algorithm for identifying individual crops is developed for labor-savings and time-series biological information collection. Information including the leaf development frequency are diagnostic indicators of strawberry growth. The algorithm is designed for drones in greenhouses that cannot acquire location information using the global navigation satellite system (GNSS). Drones fly over crop rows and sequentially assign identification numbers (IDs) to crops. Object-detection artificial intelligence (AI) is used to estimate the crop zone, and the ID is based on the crops number difference between frames. The previous misdetection rate was 1.06 %, failing to identify crops, which decreases to 0.31 % using the proposed algorithm. Furthermore, because there are no failures in consecutive frames, IDs are assigned to all crops correctly.