Grape Maturity Estimation Using Time-of-Flight and LiDAR Depth Cameras

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 16; p. 5109
• Main Authors: Legg, Mathew, Parr, Baden, Pascual, Genevieve, Alam, Fakhrul
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07-08-2024; MDPI
• Subjects: Berries; Bias; Cameras; depth cameras; grapes; Intel L515; Kinect Azure; Light; Machine learning; maturity; Protective coatings; Spectrum analysis; Sugar; United States > US; depth cameras; Kinect Azure; maturity; Intel L515; grapes
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24165109

This article investigates the potential for using low-cost depth cameras to estimate the maturity of green table grapes after they have been harvested. Time-of-flight (Kinect Azure) and LiDAR (Intel L515) depth cameras were used to capture depth scans of green table grape berries over time. The depth scans of the grapes are distorted due to the diffused scattering of the light emitted from the cameras within the berries. This causes a distance bias where a grape berry appears to be further from the camera than it is. As the grape aged, the shape of the peak corresponding to the grape became increasingly flattened in shape, resulting in an increased distance bias over time. The distance bias variation with time was able to be fitted with an R2 value of 0.969 for the Kinect Azure and an average of 0.904 for the Intel L515. This work shows that there is potential to use time-of-flight and LIDAR cameras for estimating grape maturity postharvest in a non-contact and nondestructive manner.