Abstract
Non-destructive plant growth measurement is essential for plant growth and health research. As a 3D sensor, Kinect v2 has huge potentials in agriculture applications, benefited from its low price and strong robustness. The paper proposes a Kinect-based automatic system for non-destructive growth measurement of leafy vegetables. The system used a turntable to acquire multi-view point clouds of the measured plant. Then a series of suitable algorithms were applied to obtain a fine 3D reconstruction for the plant, while measuring the key growth parameters including relative/absolute height, total/projected leaf area and volume. In experiment, 63 pots of lettuce in different growth stages were measured. The result shows that the Kinect-measured height and projected area have fine linear relationship with reference measurements. While the measured total area and volume both follow power law distributions with reference data. All these data have shown good fitting goodness (R2 = 0.9457–0.9914). In the study of biomass correlations, the Kinect-measured volume was found to have a good power law relationship (R2 = 0.9281) with fresh weight. In addition, the system practicality was validated by performance and robustness analysis.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 5
Claim · model-written summary
Kinect height and projected area measurements had linear relationships with reference measurements.
- Taxon
- lettuce
Lactuca sativae1 - Outcome
- height and projected areae0
- Comparator
- reference measurementse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The result shows that the Kinect-measured height and projected area have fine linear relationship with reference measurements.”
e1“63 pots of lettuce in different growth stages”
Claim · model-written summary
Kinect total area and volume measurements followed power law relationships with reference data.
- Taxon
- lettuceLactuca sativae1
- Outcome
- total area and volumee0
- Comparator
- reference datae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“While the measured total area and volume both follow power law distributions with reference data.”
e1“63 pots of lettuce in different growth stages”
Claim · model-written summary
The growth measurement fits had R2 values of 0.9457–0.9914.
- Taxon
- lettuceLactuca sativae1
- Outcome
- fitting goodnesse0
- Value
- 0.9457–0.9914e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“All these data have shown good fitting goodness (R2 = 0.9457–0.9914).”
e1“63 pots of lettuce in different growth stages”
Claim · model-written summary
Kinect volume had a power law relationship with fresh weight, with R2 of 0.9281.
- Taxon
- lettuceLactuca sativae1
- Outcome
- power law relationshipe0
- Value
- 0.9281e0
- Comparator
- fresh weighte0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the Kinect-measured volume was found to have a good power law relationship (R2 = 0.9281) with fresh weight.”
e1“63 pots of lettuce in different growth stages”
Claim · model-written summary
Performance and robustness analysis validated system practicality.
- Taxon
- lettuceLactuca sativae1
- Outcome
- system practicalitye0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the system practicality was validated by performance and robustness analysis.”
e1“63 pots of lettuce in different growth stages”