NIS Sorter - Initial Accuracy Testing

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I have started calibrating and characterising the machine recently. The following table lists the traits I am aiming for and their current status.

These figures are still a bit rubbery as I’m still going through the process so these will be updated from time to time as new data becomes available. Stats in these tables is based on P=0.05, N=20.

Raw Measurement Accuracy

This first table shows the error for the various raw measurements the machine makes.

Parameter
Measure
Unit
Measure
Error (+/-)
Single Nut
Measure
Error (+/-)
20 Nut Batch
Average*
Comment
Whole Nut - Colour 8 bit RGB 2.5 (2%) 0.5 Needs transform to standard colour scale
Whole Nut - Size
Average Diameter
Minimum Diameter
Maximum Diameter
Min Enclosing Diam

mm
mm
mm
mm

0.20
0.50
0.50
0.30

0.05
0.15
0.15
0.10


= required slot opening for separation

= required circle opening for separation
Whole Nut - Mass
Whole Nut - Moisture Mass
gr
gr
0.15
0.08
0.05
0.02
+/-0.1gr is possible in a half-speed high resolution mode.
Experimental
Whole Nut - Calculated Metrics
Volume
Specific Gravity
Shape
Moisture Content

cm^3
gr/cm^3
mm/mm
w/n

0.25
0.05
0.02
2%

0.05
0.02
0.01
0.5%


Volume / Mass
Min/Max, Prolateness, Convex Deviation
Experimental
  • Indicative of single source batch. Depends on the in-batch variability, some scenarios are higher than others.
Classification & Severity Accuracy

This tables shows the accuracy for various high level categories of defect.

Category % False
Positives
% False
Negatives
Severity
Error
Comment
Whole Nut - Categories
Normal
Immature
Sun Faded
Aged/Rotten

<1%*
-
-
<1%

<1%*
-
-
<1%

5%
-
-
5%

* these can increase as other categories are added
calibrating
calibrating
Age Faded & Rotten mix a bit
Defect - Categories



calibrating
* Perfectly 'Normal' nuts are Severity = 0

Rapid KR

The machine is also capable of doing a Rapid Kernel Recovery test, where either wet or dry nuts are cracked and placed in the machine, and KR is calculated a few seconds later. This can be done as additional function of the sorter with minor modifications, or a compact single purpose version can be built for in-field use.

This table shows accuracy for five NIS traits that the Rapid KR mode can measure.

Trait
Single Nut
Accuracy
In-Tree
Variability
20 Nut Batch
Accuracy*
100 Nut Batch
Accuracy*
Nut Diameter (mm) 0.6 3.5 0.8 0.4
Nut Mass (gr) 0.5 3.0 0.7 0.3
Kernel Diameter (mm) 0.5 2.7 0.6 0.3
Kernel Mass (gr) 0.2 1.0 0.2 0.1
Kernel Recovery 1.5% 4.4% 1.0% 0.5%
* There is substantial in-tree variability for these traits and it is the dominant source of error in the batch values. It can be ameliorated by increasing sample size. It is worth noting that these are roughly the same values you would expect from the standard 48hr test because it is also affected by the in-tree variability.

Visual Separation of Varieties

Finally the machine can also separate between different varieties of nuts based on the NIS appearance.

This table shows the separation accuracy for four common varieties.

Mixing
From\To
A4 A16 A38 H246 Overall
Error
A4 - < 1% < 1% < 1% 1%
A16 < 1% - 2% 11% 10%
A38 < 1% 2% - 5% 7%
H246 < 1% 14% 4% - 11%

This is very much a work in progress at this stage, I will be looking at a larger group of varieties in the near future.