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

Mathew Legg¹, Baden Parr¹, Genevieve Pascual¹

¹Department of Mechanical and Electrical Engineering, Massey University, Auckland 0632, New Zealand.

Sensors (Basel, Switzerland)

|August 29, 2024

Summary

This summary is machine-generated.

Low-cost depth cameras, including time-of-flight and LiDAR, can estimate green table grape maturity postharvest. As grapes age, increased distance bias in depth scans accurately predicts their maturity non-destructively.

Keywords:

Intel L515 Kinect Azure depth cameras grapes maturity

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Area of Science:

Agricultural Technology
Computer Vision
Horticulture

Background:

Accurate postharvest maturity assessment is crucial for table grape quality.
Traditional methods can be destructive or labor-intensive.
Non-contact, non-destructive methods are needed for efficient quality control.

Purpose of the Study:

To evaluate the feasibility of using low-cost depth cameras for estimating green table grape maturity after harvest.
To investigate the relationship between depth scan data and grape aging.

Main Methods:

Utilized time-of-flight (Kinect Azure) and LiDAR (Intel L515) depth cameras to capture berry scans.
Analyzed depth scan data for changes in shape and distance bias over time.
Quantified the correlation between observed distance bias and grape maturity.

Main Results:

Depth scans showed increased distance bias as grapes aged due to light scattering.
The distance bias variation correlated well with time, with R2 values of 0.969 (Kinect Azure) and 0.904 (Intel L515).
A flattened peak in the depth scan data indicated increased grape maturity.

Conclusions:

Time-of-flight and LiDAR cameras show significant potential for non-contact, non-destructive grape maturity estimation.
Depth camera technology offers a promising avenue for postharvest quality assessment in viticulture.
This method could lead to improved quality control and reduced waste in the grape industry.