Growth stage detection for food consumption management in smart cricket farming using a deep learning technique - Nutnoi, Nitipoom; Yindeesuk, Witoon; Kamoldilok, Surachart; Srinuanjan, Keerayoot; Limsuwan, Pichet (My University, 2025)
This research proposed a novel method for tracking and predicting the growth stages of two-spotted crickets, reared in a temperature-controlled box at different growth stages using the YOLOv5s model. The images of crickets feeding inside the rearing box were taken with an infrared camera above the feeding point every hour. Images of the cricket were used to train a YOLOv5s model to detect crickets for each growth stage in the rearing box. The experimental results showed that the trained deep learning had an average accuracy of 95.7%. The relationship between the ratio of crickets at each growth stage throughout the 45-day rearing period was plotted and discussed. The results also showed a clear relationship between the amount of food consumed by crickets per day and their growth stage, which could be useful for appropriately managing food consumption according to the growth stage of crickets.
Keywords: Deep learning; Growth trend; Object detection; Smart agriculture; Two-spotted cricket (Gryllus bimaculatus)