Deep learning-based detection of green-ripe pineapples via bract wilting rate analysis

Green-ripe pineapples are ideal for long-term transportation and storage during summer. However, accurately identifying them during in-situ harvesting remains a challenge for farmers. To address this issue, this study proposes a deep learning-based YOLO[sbnd]NAS-L algorithm to detect green-ripe pineapples by analyzing the wilting rate of floral bracts at the fruit's base. An unmanned tracked vehicle equipped with an Intel D405 depth camera was used to traverse pineapple fields, capturing images from a distance of 300–400 mm. Each image covered approximately 20 floral bracts, with a detection resolution of around 30 × 30 pixels. The camera also provided three-dimensional coordinates of the pineapples to support automated harvesting. To mitigate ambient light variations, a white LED lighting system (24V/5A) was implemented for illumination enhancement. Experimental results indicate that analyzing floral bract wilting improves green-ripe pineapple recognition accuracy by 13.6 %, reaching 95.4 %, compared to solely identifying the pineapple's base. These findings demonstrate that deep learning-based floral bract wilting analysis significantly enhances recognition accuracy and provides robust support for automated harvesting.