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Hand-picking dynamic analysis for undersensed robotic apple harvesting
Article · January 2016
DOI: 10.13031/trans.59.11669
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Transactions of the ASABE
Vol. 59(4): 745-758 © 2016 American Society of Agricultural and Biological Engineers ISSN 2151-0032 DOI 10.13031/trans.59.11669 745
HAND-PICKING DYNAMIC ANALYSIS FOR UNDERSENSED
ROBOTIC APPLE HARVESTING
J. Davidson, A. Silwal, M. Karkee, C. Mo, Q. Zhang
ABSTRACT. This article evaluates hand-picking methods as candidate grasping techniques for implementation in a robotic
system designed to harvest apples. The standard method of hand-picking apples is highly selective to the apple’s orientation
and stem location. However, sensory detection of the fruit’s orientation and stem while the apple is on the tree is a chal-
lenging problem requiring significant computation time. In this study, four picking techniques that do not require knowledge
of fruit orientation were applied to five apple varieties growing in several different cultivation systems. The sensors used
during hand-picking included force sensors and an inertial measurement unit. Experimental results were obtained for nor-
mal contact forces during a three-fingered power grasp as well as the angle of rotation around the axis of the forearm. Field
data and controlled laboratory experiments show that fruit separation can be clearly detected. Accelerometer measurements
were also used to calculate the average distance to fruit separation, which varied from 3 to 7 cm. The optimum picking
method relative to stem attachment was identified for each apple variety.
Keywords. End-effector, Grasping, Machine vision, Robotic harvesting, Sensor fusion.
he production of fresh market apples is a major in-
dustry in Washington State. In 2014, Washington
produced 2.7 million metric tons of apples valued
at approximately $1.84 billion USD (USDA,
2014). The state accounts for approximately 70% of U.S. ap-
ple production and is a major international exporter. How-
ever, like many agricultural industries around the world, the
industry is struggling to cope with rising labor costs and in-
creasing uncertainty surrounding the availability of labor,
much of which is supplied by immigrant Latino populations.
A recent study by the Pew Research Center found that over
the past five years net migration from Mexico to the U.S. has
been negative (Gonzalez-Barrera, 2015). The most time and
labor-intensive task in apple production is harvesting, which
is physically demanding and highly repetitive work. In
Washington State alone, the apple and pear harvest requires
the employment of 30,000 additional workers (Galinato and
Gallardo, 2011; Gallardo et al., 2010). To address some of
these challenges, several researchers (Grand D’Esnon, 1985;
Baeten et al., 2008; Bulanon and Kataoka, 2010; Zhao et al.,
2011) have developed robotic systems for selective harvest-
ing of fresh market apples. Despite these efforts, there are no
known robotic systems in commercial use for harvesting of
any specialty crops, including fresh market apples (Wouter
Bac et al., 2014).
Some limitations of previous robotic harvesting work in-
clude insufficient harvesting speed and fruit detachment ef-
ficiencies (Wouter Bac et al., 2014). Prior research (Nguyen
et al., 2012; Tong et al., 2014) proposed that in order to im-
prove manipulation performance and reduce damage, the ro-
botic system should mimic the human hand-picking process.
The optimum picking technique recommended by both Ngu-
yen et al. (2012) and Tong et al. (2014) requires sensory
knowledge of the fruit’s orientation and stem/peduncle loca-
tion. While some work has been done using machine vision
techniques (Zhang et al., 2015; Jiang et al., 2009) to identify
the apple stem and calyx during post-harvest inspection and
grading operations, there is little in the literature about the
detection and localization of the fruit’s stem while it is on
the tree. There has been some research on trunk and branch
detection for autonomous navigation and obstacle avoidance
while apple harvesting (Jidong et al., 2012). In addition, Bac
et al. (2014) developed an algorithm that uses the support
wire to localize the stems of sweet peppers grown in green-
houses. Work also exists describing stem localization for a
leaf-picking robot (Van Henten et al., 2006). To the best of
our knowledge, the only research that describes localization
of stems of apples on the tree is the technique proposed by
Bulanon et al. (2001). They developed a machine vision sys-
tem that used a geometric approach to locate the fruit center
and abscission layer of the fruit’s stem. However, this
method is limited to the Fuji cultivar, which has a relatively
long stem, and requires a consistent fruit orientation with a
vertically oriented stem.
Some of the challenges of stem localization are evident in
Submitted for review in November 2015 as manuscript number ITSC
11669; approved for publication by the Information, Technology, Sensors,
& Control Systems Community of ASABE in May 2016.
The authors are Joseph Davidson, Graduate Student, School of
Mechanical and Materials Engineering, Abhisesh Silwal, ASABE
Member, Graduate Student, and Manoj Karkee, ASABE Member,
Assistant Professor, Department of Biological Systems Engineering and
Center for Precision and Automated Agricultural Systems, Changki Mo,
Assistant Professor, School of Mechanical and Materials Engineering and
Center for Precision and Automated Agricultural Systems, and Qin Zhang,
ASABE Member, Professor, Department of Biological Systems
Engineering and Center for Precision and Automated Agricultural Systems,
Washington State University, Prosser, Washington. Corresponding
author: Joseph Davidson, 2710 Crimson Way, Richland, WA 99354;
phone: 509-372-7356; e-mail: joseph.davidson@wsu.edu.
T

746 TRANSACTIONS OF THE ASABE
figure 1. The stem is often occluded by leaves and other fruit
and is difficult to segregate from adjacent branches. For ap-
ple cultivars with relatively short stems (table 2), such as
Jazz shown in figure 1, the stem may not be visible beyond
the top of what is commonly referred to as the fruit’s stem
cavity. Some of our preliminary research indicates that stem
detection and localization is computationally intensive and
may negatively impact overall harvest cycle time. Consider-
ing that efficient cycle time and effective fruit detachment
are fundamental design criteria for a robotic harvesting sys-
tem, the question becomes: are there effective ways to pick
apples irrespective of fruit orientation and stem location?
The purpose of this research was to conduct a dynamic
analysis of multiple hand-picking methods that do not con-
sider stem location or apple orientation. The overall goal was
to determine if there are effective techniques that could be
implemented in an undersensed robotic system that does not
dedicate computation time or resources to stem localization.
After providing a brief description of standard apple picking
methods used by professional pickers, a potential picking
method for an undersensed robotic system is proposed. Ex-
perimental results from dynamic analysis of four different
picking methods are then presented for multiple apple vari-
eties and tree cultivation systems. Previous work by Flood
(2006) used a manipulator with seven degrees of freedom
and a six-axis force/torque sensor to measure a variety of pa-
rameters, such as distance to separation and detachment
force, and their correlation to picking motions with known
angles relative to the stem axes of oranges. Rather than using
a machine with a single sensor to measure a total resultant
force, in this study a human operator with multiple sensors
on the hand was used to study the forces at several contact
points. Presented results include normal contact forces, an-
gles of rotation, rates of stem detachment, rates of spur de-
tachment, and propensity for fruit bruising. This work is be-
lieved to be the first effort that explores undersensed fruit
grasping and that also categorizes and compares the forces
involved in the picking process across multiple fruit varieties
and cultivation systems.
APPLE HARVESTING TECHNIQUES
STANDARD APPLE PICKING METHOD
The task requirements of apple picking dictate the use of
a prehensile, power grasp (Napier, 1956) where the fruit is
seized and held within the compass of the hand. Because the
hand must support and accelerate the fruit while resisting the
external disturbance of the stem/abscission joint, considera-
tions of stability and security supersede requirements for
dexterous manipulation. The grasp, which can be further cat-
egorized as a spherical power grasp using the Cutkosky tax-
onomy (Cutkosky, 1989), is characterized by large areas of
contact between the fruit and finger surfaces and palm that
help minimize bruising. Some research on optimal apple
picking methods has already been presented in the literature
(Tong et al., 2014; Nguyen et al., 2012). The standard tech-
nique is to apply pressure against the stem with the index
finger while grasping the fruit with the hand. To separate the
apple from the branch, the hand moves the apple in a pendu-
lum motion. There is no dexterous manipulation of the fruit
with the fingers. The tensile strength of the stem-abscission
joint is significantly higher than its shear strength. Pulling
while simultaneously rotating the fruit, and thereby bending
the stem, produces a combined pulling and pendulum motion
that induces shear forces. Based on our qualitative observa-
tions, application of stem pressure during hand-picking is
highly dependent on the apple cultivar. For apples with rel-
atively short, stiff stems, such as Jazz, we rarely observed
the worker applying pressure against the stem. While for va-
rieties with longer stems, such as Fuji (table 2), application
of force against the stem was typical. If the fruit is part of a
cluster or located on the end of a long, flexible branch, both
hands might be used during the picking process. The speed
of a professional apple picker is typically 1 to 3 s per fruit.
UNDERSENSED ROBOTIC APPLE HARVESTING
Due to numerous environmental and varietal factors, ap-
ples exhibit natural variation in fruit position, shape, size,
growing orientation, and stem length. The level of variation
depends to a large degree on the fruit cultivar, growing en-
vironment, and annual climate patterns. Even for the same
Figure 1. (left) Jazz apples have variable orientation, and the location of the stems is often obscured by leaves, branches, and other fruit; (right)
for cultivars with relatively short stems, such as Jazz, the stem may not be visible even though the fruit is completely visible.

59(4): 745-758 747
fruit cultivar, parameters such as fruit color, size, and stem
length will vary within a single tree. In this article, under-
sensed grasping scenarios are considered for modern orchard
systems in which the trees are supported by trellis wires. Ad-
ditional details about these cultivation systems are provided
in the next section of this article. The resulting two-dimen-
sional, planar canopy of this type of system produces a
“fruiting wall” architecture designed to increase yield, labor
productivity, and ease of machine use by enhancing visibil-
ity and accessibility of the fruit. Because fruit positions are
kept relatively close to the plane of the tree, obstacle avoid-
ance and motion planning requirements are reduced com-
pared to robotic systems operating in trees with conventional
three-dimensional canopies. In this article, “undersensed”
robotic apple harvesting also describes a design concept that
does not dedicate resources to visual detection of obstacles.
An advantage of undersensed robotic harvesting in a pla-
nar environment is relatively simple path planning. Avoid-
ing obstacle detection and motion planning reduces compu-
tation and benefits overall cycle time. For the robotic har-
vesting method considered, the end-effector’s normal vector
is collinear with an azimuth vector during the final approach
to the fruit’s position to ensure that the apple remains within
the workspace of the end-effector. The origin of the world
reference frame O (a right hand coordinate frame) with unit
vectors i, j, and k is co-located with the manipulator’s base
frame according to Denavit-Hartenberg (DH) convention
(Denavit and Hartenberg, 1955). Let p = ai ±bj ±ck be the
vector of coordinates of the fruit’s center with respect to
frame O. During approach to the fruit, the end-effector’s nor-
mal vector is partially defined by an azimuth angle θ, which
is determined from the projection of the fruit’s position in
the x-y plane (fig. 2). The proposed azimuth vector is well
suited for anthropomorphic manipulator configurations
where the link twist αi-1 between the first three links accord-
ing to DH convention is π/2, 0, and 0, respectively. Transi-
tioning between apples in a cycle may only require a joint
update in the base actuator for this kinematic configuration,
which is common in industrial manipulators.
The end-effector’s normal vector during approach and de-
tachment is fully defined by the azimuth angle θ and the de-
sired pitch. A 2D sketch (simplified) of the end-effector path
in two orchard systems is shown in figure 3. Considering that
obstacle avoidance is not proposed for undersensed harvest-
ing, an end-effector path perpendicular to the canopy may help
minimize unintended collisions with adjacent objects such as
neighboring fruit. Therefore, for the vertical orchard system,
the path would be horizontal. Because there is some orchard
variability in V-trellis architectures, an inclined angle of 45°
relative to the ground was considered in this study. It should
also be noted that standard pruning practices in some orchards
is to leave foliage only above the apple to reduce sunburn.
Therefore, an inclined approach could help minimize the like-
lihood of obstructions during fruit grasping.
In summary, for the robotic system executing under-
sensed apple picking in a fruiting wall architecture using a
grasping end-effector, the proposed method of manipulation
would follow this general sequence of four steps:
1. The manipulator guides the end-effector along an azi-
muth vector to the apple’s position, assuming that the
path along the azimuth vector is free of obstacles.
2. The end-effector grasps the apple using a spherical
power grasp.
3. The manipulator detaches the fruit using a sequence of
twisting and/or pulling actions while moving the end-
effector and the grasped fruit away from the canopy
along the azimuth vector.
4. The manipulator releases the fruit and then proceeds
to pick the next fruit in the cycle.
EXPERIMENTAL METHOD
ORCHARD ARCHITECTURE AND CULTIVAR SELECTION
As mentioned previously, in this research the hand-pick-
ing dynamic analysis was conducted using apple trees with
modern fruiting wall architectures growing in commercial
orchards in Washington State. These canopy architectures
included formally trained vertical and V-trellis fruiting wall
designs in which apples grow laterally along branches sup-
ported by trellis wires. Examples of the V-trellis and vertical
architectures are shown in figure 4.
In the 1990s, Red Delicious accounted for 70% of the
bearing acreage in Washington State, followed by Golden
Delicious at 20% (USDA, 2015). Over the past 25 years, cul-
Figure 2. For undersensed robotic harvesting, the end-effector is par-
tially constrained by an azimuth angle during its approach to the fruit.
Figure 3. 2D diagrams of end-effector approaches to orchard canopies.

tivar preferences have changed dramatically, and by 2010
the bearing acreage of Red Delicious had dropped to 30% of
production due to growing consumer demand for varieties
like Gala and Fuji. To account for current trends in consumer
preference, five popular fresh market apple varieties were
chosen for this study. These varieties were Fuji, Jazz, Envy,
Cripps Pink (Pink Lady), and Pacific Rose. Figure 5 shows
the differences between the five varieties in color, size, and
geometry, including stem length.
Physical parameters of the orchard, such as tree height,
row spacing, trellis wire spacing, and fruit density, were
measured for all five apple varieties. However, the architec-
tures had variations in these parameters that were established
according to the requirements of their respective designers.
On average, there were seven to eight trellis wires bearing
20 to 80 fruit between trees spaced approximately 180 to
350 cm apart. The distance between trees in the V-trellis was
approximately 0.5 to 1.5 m. All measured architecture pa-
rameters are summarized in table 1.
Additionally, qualitative observation of the shape, size,
and weight of individual apples in the orchards indicated sig-
nificant variations within these parameters. A variety of ap-
ple sizes were considered to incorporate variability in the
normal forces required to detach fruit. The only apples ex-
cluded from picking were those with noticeable color differ-
ences that indicated immaturity. The weight, major axis, mi-
nor axis, and stem length were recorded for each sample of
all varieties. The major axis (fig. 6) is the maximum length
of the apple measured from the bottom of the calyx to the tip
of the stem, whereas the minor axis is the maximum length
across the equator of the apple. Similarly, the stem length
was measured from the bottom of the stem cavity to its de-
tachment at the abscission layer. Table 2 summarizes the av-
erage size, weight, and stem length of 30 samples of each
cultivar. The harvesting dates for each variety were 9 and
14 October 2015 for Jazz, 14 October 2015 for Envy, 22 Oc-
tober 2015 for Pacific Rose, 3 November 2015 for Cripps
Pink, and 3 November 2015 for Fuji.
V-trellis Vertical
Figure 4. Formal apple tree canopy architectures.
Figure 5. Five apple cultivars used in this research.
Table 1. Physical parameters of five cultivation systems.
Variety Architecture
No. of
Trellis
Wires
Trellis Wire
Spacing
(cm)
Tree
Spacing
(cm)
Row
Width
(cm)
Tree
Height
(cm)
Fruit Density
(fruit per linear meter
of a single branch)
Jazz Vertical 7 46 ±2 140 ±11 262 ±6 427 31 ±4
Envy V-trellis 7 46 ±1 142 ±19 353 ±3 366 19 ±6
Pacific Rose V-trellis 7 61 ±15 46 ±6 180 ±5 366 33 ±8
Fuji V-trellis 8 48 ±6 107 ±9 381 ±3 356 10 ±1
Pink Lady V-trellis 7 61 ±2 117 ±14 422 ±2 366 18 ±2

59(4): 745-758 749
Figure 6. Major and minor axis measurement of apples.
Table 2. Physical measurements of apples and stems. Values are means
± standard deviations.
Variety
Major
Axis
(mm)
Minor
Axis
(mm)
Weight
(g)
Stem
Length
(mm)
Envy 76.4 ±5.7 80.0 ±4.4 254.7 ±45.3 22.0 ±4.2
Jazz 69.9 ±4.1 66.1 ±3.6 165.5 ±29.8 13.3 ±4.2
Pacific Rose 71.6 ±5.5 77.5 ±5.6 218.9 ±41.5 16.2 ±5.1
Fuji 77.5 ±5.9 79.5 ±6.2 247.5 ±54.9 27.1 ±4.6
Cripps Pink 72.8 ±4.1 75.2 ±3.2 195.4 ±23.8 24.2 ±4.0
GRASP REPEATABILITY ANALYSIS
During picking, the fruit was grasped with a three-fin-
gered power grasp using the thumb, index finger, and middle
finger with the hand fully encompassing the fruit against the
palm. To measure normal forces between the fingers and
fruit surface during the picking process, three flexible force
sensors (Tekscan, South Boston, Mass.) were installed on
the distal phalanges of the thumb, index finger, and middle
finger. The range of the force sensors reported by the manu-
facturer was 0 to 111 N with ±3% linearity and ±2.5% re-
peatability. Prior to each use, the sensors were calibrated to
convert raw voltage values into physical units of force (N).
Dummy weights that generated forces equivalent to the an-
ticipated forces required for picking apples were used for
calibration. Three masses of 0.2, 0.5, and 1.0 kg were used
to fit a linear regression line for all three sensors separately.
For each sensor, the R2
value was approximately 0.99, which
indicated that the sensor was well calibrated and could be
used with reasonable confidence to measure grasping forces.
An experiment conducted in the laboratory with a con-
trolled resistance was used to determine repeatability of nor-
mal contact forces between grasps. The experimental setup
and force sensor installation are shown in figure 7. A tension
and compression force gauge (Wagner Instruments, Green-
wich, Conn.) with 30 kgf range and ±1% accuracy was fas-
tened to an optics table with a custom mount produced with
a 3D printer. To replicate an apple pick, the operator grasped
a plastic sphere (80 mm diameter) attached to the gauge with
high-strength fishing line. The operator then applied a ramp
input until the tensile force measured 45 N, at which point
static equilibrium was maintained. The total duration of each
test was 5 s, and the hand was removed from the sphere be-
tween grasps. The experiment was completed over two days
to produce variability from sensor installation. The middle
finger contact force measurements of 25 grasps by two dif-
ferent operators are shown in figure 8. Both operators were
adult males with substantial differences in hand size. Each
operator reached static equilibrium after approximately
1.5 s. The confidence interval for each finger’s contact force
measurement at steady-state conditions was determined us-
ing Matlab’s Statistics and Machine Learning toolbox (The
Mathworks, Inc., Natick, Mass.). Assuming a normal distri-
bution of variance, the 95% confidence interval for the
thumb, index finger, and middle finger were, respectively,
26.9 ±1.7 N, 13.6 ±1.0 N, and 10.0 ±0.8 N. Because operator
1 conducted the field trials, only his confidence intervals are
reported.
PICKING PATTERNS AND INERTIAL MEASUREMENT UNIT
During this research, four different hand-picking patterns
were considered: (1) horizontal pull, (2) inclined pull,
(3) horizontal pull and twist, and (4) inclined pull and twist.
In each scenario, the operator stood in the orchard row ap-
proximately half a meter away from the canopy. To maintain
consistency in the grasping process, the same adult male
completed all fruit picks. The operator performing the exper-
iments had observed professional pickers and spent consid-
erable time harvesting fruit but was not a professional apple
picker. The fruit was grasped with the same three-fingered
Figure 7. (left) Experimental setup showing force gauge and direction of simulated apple pick and (right) force sensor installation on the distal
phalanges of the thumb, index finger, and middle finger.

power grasp described in the preceding section and then
pulled in the direction of the forearm axis. During test pat-
terns 3 and 4, the hand was rotated counterclockwise while
simultaneously pulling the fruit. Figure 9 shows the start of
both a horizontal and inclined grasp; the three force sensors
can be seen on the distal phalanges.
For all test patterns, the grasp of the fruit was completed
in a manner such that the plane formed by the index finger
and thumb was oriented along the axis of the forearm. To
measure rotation around the forearm, a nine-axis attitude
sensor (Sunkee, Amazon, Inc., Seattle, Wash.) with a three-
axis gyro, accelerometer, and magnetometer was placed be-
tween the index finger and thumb on top of the muscle re-
ferred to as the first dorsal interosseous (WUSTL, 2010).
This area of the hand was selected for sensor installation be-
cause it provided a relatively large, flat mounting surface.
The location of the sensor during picking is clearly shown in
figure 10. Starting from the onset of motion away from the
fruiting wall, the operator attempted to complete each apple
pick in approximately 1 s. The gyroscope’s range and accu-
racy at the selected measurement range were, respectively,
250° s-1
and ±1%.
DATA ACQUISITION
The general block diagram of data acquisition is shown
in figure 11. As described in the two preceding sections,
three resistive force sensors were placed on the distal pha-
langes of the thumb, index finger, and middle finger. These
analog sensors converted applied force to analog voltages
that were passed through an excitation circuit (Phidgets, Inc.,
Calgary, Alberta, Canada) and then measured using the ana-
log port of an Arduino Uno board. Although the inertial
Figure 8. Middle finger normal force measurements for 25 grasps com-
pleted by (top) operator 1 and (bottom) operator 2.
Figure 9. (left) Hand approaching the fruit during a horizontal grasp (the raised portion of the glove behind and above the thumb is the inertial
measurement unit), and (right) all three force sensors are visible during the start of an inclined grasp.
Figure 10. The nine-axis inertial measurement unit shown with the
glove and force sensors removed. The sensor rests between the thumb
and index finger on top of the first dorsal interosseous and measures
rotation of the fruit around the axis of the forearm. The sensor was
taped to a rubber block and securely held in place by pressure from the
outer rubber glove.

59(4): 745-758 751
measurement unit (IMU) had gyro, accelerometer, magne-
tometer, and barometric sensors, only the gyro and accel-
erometer were used to measure the twist angle. An I2C
sketch from the Arduino library was used to access the val-
ues of both sensors from the IMU’s I2C bus. The sampling
frequency for all sensors was set at 8 kHz. Because gyro out-
put is actually angular velocity (degrees s-1
), a high sampling
frequency was selected to increase the accuracy of the nu-
merical integration technique used to determine the total an-
gle of rotation. In addition, to ensure that the entire picking
sequence was captured, the duration of data collection dur-
ing each sample was set at 3 s. The raw data from the IMU
and force sensors were then sent via serial communication to
a computer with Matlab (rev. 2014a, The Mathworks, Inc.,
Natick, Mass.) for further analysis.
Data acquisition began when the hand was in a static po-
sition grasping the apple. While the gyro was relatively sta-
ble during the dynamic picking sequence, it tended to drift
and show offset error near the static position. On the other
hand, the accelerometer demonstrated stability at the rest po-
sition and produced more fluctuation in the dynamic envi-
ronment (Colton, 2007; MIT, 2004). These behaviors are
complementary to each other, and information from the two
sensors can be fused to produce more accurate results. Be-
cause it is an effective and robust technique, the complemen-
tary filter was selected for sensor fusion. The equation gov-
erning the complementary filter is shown in equation 1:
( )
( )
( ) ( )
k
k
k
k
k
k
acc
x
t
t
gyro
angle
angle
_
1
1
1
×
α
−
±
−
×
±
×
α
= +
+
(1)
where α is a time constant whose value was selected on trial
and error basis (0.9 in this study). Figure 12 compares the
results of the gyro, accelerometer, and fused sensor angular
outputs. As seen in figure 12 between the time stamps of 1
and 2 s, the gyro sensor starts to accumulate error while the
accelerometer data fluctuates during sharp angular changes.
As explained above, the complementary filter fused this in-
formation and produced results within an error range of ±2°.
RESULTS AND DISCUSSION
Thirty samples were taken for each of the four test pat-
terns, resulting in 600 total apple picks. During each pick,
accelerometer measurements obtained while the hand was
static were used to determine the pitch of the sensor relative
to the ground. For horizontal test conditions, the mean pitch
of the sensor at the onset of the picking motion was 14.8°
±5.9° above the ground. For inclined test conditions, the sen-
sor’s mean pitch at the onset of the picking motion was 52.7°
±6.9° above the ground. Deviation in sensor pitch can partly
be accounted for by variability between hand poses. Inclina-
tion of the sensor’s mounting position, which can be seen in
Figure 11. Data acquisition block diagram.
Figure 12. Sensor fusion results. The complementary filter produced more accurate results by addressing the drift and fluctuations present in the
gyro and accelerometer, respectively.
-40
-30
-20
-10
0
10
20
30
Gyro Accelerometer Complementary Filter
Time (sec)
Angle
(degree)
1 2 3 4

figure 9, also contributed to a mean pitch above 0° for hori-
zontal picking motions. While the implemented experi-
mental method provided sufficient approximation of the
end-effector pitches shown in figure 3, additional work is re-
quired to replicate robotic picking along an azimuth angle.
Because robustly measuring the yaw angle of the end-effec-
tor with respect to a world coordinate frame was problematic
with the selected sensor suite, the operator attempted to pro-
duce a picking motion that was perpendicular to the plane of
the tree. For future study, we are considering the develop-
ment of a mechanical method to robustly replicate apple
picking along an azimuth vector.
Normal contact forces and rotation angles were recorded
through the duration of the fruit pick for each sample. A me-
dian filter was then applied to the measurements for all test
conditions and varieties. The post-filtering results for the
two horizontal test conditions of the Envy variety are shown
in figure 13. The plot clearly displays the force profile during
the duration of picking. For the first ~0.8 s, the fruit is lightly
caged with a three-fingered power grasp, and the hand re-
mains static. To pick the fruit, the operator begins to accel-
erate the fruit away from the tree using a combined pulling
and/or twisting action. Normal contact forces rapidly in-
crease as the hand supports the mass of the fruit and begins
to feel the external disturbance of the stem’s attachment to
the tree. A prehensile grasp of the fruit is maintained, and the
coordinate frame of the apple is kept stationary relative to
the coordinate frame of the hand, implying no slipping of the
fruit. After approximately 1.1 s, the normal forces reach their
peak. The normal force on the thumb was usually highest
because it tended to oppose the contact forces of the index
finger and middle finger, which were located closer to one
another in terms of angular displacement. The median filter
results for all test conditions and varieties showed similar
force profiles.
The initial assumption in the field was that normal forces
peaked at the instant of fruit detachment. After field trials
were completed, a controlled experiment was conducted in
the laboratory to verify this assumption. The experimental
setup shown in figure 7 was slightly modified for this exper-
iment. When the operator’s pulling resistance reached static
equilibrium of 45 N, as measured on the force gauge, the
connecting cord was cut. As the line was cut, a switch was
simultaneously tripped and the time of activation was rec-
orded. The results of three trials are shown in figure 14. The
vertical red lines indicate when the cord was cut and the
switch was activated. In all instances, peak normal forces oc-
curred at the moment prior to removal of the grasp’s external
disturbance. The results from this experiment provide confi-
dence that peak normal forces during field trials occurred at
fruit detachment. Figures 13 and 14 show that contact forces
begin to rapidly decline after the external disturbance of the
stem’s attachment at the abscission joint is removed. The an-
gle of rotation is negative for counterclockwise rotation be-
cause the axis of rotation is considered positive when di-
rected from the picker toward the tree. Measurements indi-
cate that rotation around the axis of the forearm was minimal
for pure pulling motions. Because of its angular momentum,
(a)
(b)
Figure 13. Normal forces and rotation angles for (a) horizontal pull and (b) horizontal twist of Envy after noise suppression with median filter.
Force
(N)
Angle
(Degrees)
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Time (sec)
0
20
40 Thumb
Index Finger
Middle Finger
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Time (Sec)
-60
-40
-20
0

59(4): 745-758 753
the hand continues to rotate for a small amount of time after
the fruit is detached.
Figure 15 shows the average contact forces and angle of
rotation, including standard deviation (σ), at the point of
fruit detachment for each apple variety and all testing pat-
terns. The mean values presented are for all samples. The
recorded forces match the qualitative observations of the op-
erator performing the picking. That is, to the operator, hori-
zontal pulling of the Jazz and Envy varieties seemed more
difficult than picking the other three cultivars. Other than the
horizontal twisting of Jazz and Envy, twisting of the apple
did not reduce the peak forces at fruit detachment. Because
of the variable orientations of the fruit and lack of pressure
against the stem, twisting does not appear to induce more
shear in the stem/abscission joint than a straight pulling mo-
tion. The magnitude of the normal contact forces during the
initial static grasp of the apple is less important than the abil-
ity of the end-effector to maintain form closure during the
dynamic picking motion. Transmissions that can lock or
hold the joints of the device are required to keep the fruit
from pulling out of a completed grasp. The recorded forces
show that, depending on the size and arrangement of the fin-
gers, a three-fingered grasping end-effector would need to
resist significant external disturbance forces, as high as 30 N
for horizontal pulling of Jazz, to maintain form closure of the
apple.
The nature of the force profile during the picking process
indicates that tactile sensing could be used to determine
when the fruit is detached from the tree. For a robotic system
that grasps the fruit in an open-loop manner, unless addi-
tional sensing is used, the system must move the fruit a suf-
ficient distance away from the tree to ensure that the fruit is
successfully detached. In practice, this type of open-loop
grasping could lead to the system displacing the fruit as part
of its picking motion even after the apple was detached,
which would have a negative overall impact on cycle time.
However, with tactile sensing in the end-effector, the rapid
decrease in contact force that occurs at the point of detach-
ment could be detected and used to minimize fruit displace-
ment during the picking sequence, which would help reduce
overall cycle time. Another approach would be the method
implemented by Flood (2006) that used a six-axis force
torque sensor mounted on the wrist of the manipulator to
measure the change in the resultant force at fruit detachment.
A potential disadvantage of using a wrist sensor is that, with-
out additional sensing, the system may only know if a fruit
had not been successfully grasped after the end-effector was
moved away from the fruit’s position with no change in wrist
force. Tactile sensing provides immediate feedback about
the success of a grasp at the fruit’s position.
By linking peak forces with fruit detachment, dead reck-
oning can be used to estimate displacement at the point of
separation. While error accumulation with a low-cost IMU
during dead reckoning would usually be substantial, it is less
of a concern considering the relatively short displacement of
an apple pick. Figure 16 shows the algorithm used to deter-
mine the total duration from the beginning of a pick until the
point of separation. Linear acceleration measurements from
the sensor’s reference frame were numerically integrated us-
ing the composite trapezoid rule (Cheney and Kincaid, 2013)
to determine the total displacement. The algorithm was ap-
plied to each of the 600 sample picks. The means and stand-
ard deviations of duration and total displacement are pro-
vided in figure 17.
Figure 14. Results of controlled laboratory study of normal contact
forces at fruit separation. The red line indicates the moment the switch
was tripped and the cord cut. The lines for the thumb, index finger, and
middle finger are dotted black, green, and blue, respectively.
Figure 15. Results from contact force and rotation measurements for all apple varieties and testing patterns. The forces and angles are mean
values recorded at the point of fruit detachment from the tree.
Mean σ Mean σ Mean σ Mean σ Mean σ Mean σ Mean σ Mean σ
Jazz 30 10 17 7 6 3 NA NA Jazz 14 11 15 6 6 3 -40 18
Envy 30 9 14 4 8 3 NA NA Envy 18 6 12 5 6 2 -32 15
Pacific Rose 22 10 9 5 10 4 NA NA Pacific Rose 22 6 9 3 10 4 -58 15
Cripps Pink 20 7 9 3 10 3 NA NA Cripps Pink 19 6 9 3 8 4 -54 12
Fuji 11 6 10 4 6 3 NA NA Fuji 20 6 12 5 4 2 -54 15
Mean σ Mean σ Mean σ Mean σ Mean σ Mean σ Mean σ Mean σ
Jazz 22 8 13 5 6 2 NA NA Jazz 23 6 14 5 5 2 -37 13
Envy 24 6 15 4 7 3 NA NA Envy 19 6 16 8 6 4 -36 13
Pacific Rose 23 11 13 5 13 4 NA NA Pacific Rose 25 8 12 6 10 4 -45 14
Cripps Pink 18 4 8 3 8 3 NA NA Cripps Pink 18 5 11 4 8 4 -44 10
Fuji 19 9 8 4 4 2 NA NA Fuji 20 5 10 3 4 2 -51 13
INCLINED PULL WITH TWIST
Peak Force (N) Detachment
Angle (Deg)
Thumb Index Finger Middle Finger
INCLINED PULLING ONLY
Angle (Deg)
HORIZONTAL PULLING ONLY HORIZONTAL PULL WITH TWIST
Angle (Deg)
Angle (Deg)

Input: 1 × n array of time steps t, 1 × n array of thumb normal force
measurements f, and 3 × n matrix of three-axis acceleration measure-
ments A.
Step 1: Determine time index n of the thumb’s peak normal force →
nmax.
Step 2: Determine time index n when the thumb’s normal force begins
to rise → nstart. Duration of the apple pick is the difference in time
between these two indexes:
for n = 1:length(t)
if fn+1 > (1.25 × fn)
nstart = n
break
end
end
duration =
start
n
max
n t
t −
Step 3: Filter the acceleration matrix to measurements between
start
n
t
and
max
n
t . Set initial velocity (V0) and position (S0) vectors to 0.
Step 4: Implement dead reckoning using the composite trapezoid rule:
for m = 1:length(
start
n
max
n t
t − )
Vm+1 = Vm + 0.5 × (Am+1 + Am) × (tm+1 – tm)
Sm+1 = Sm + 0.5 × (Vm+1 + Vm) × (tm+1 – tm)
end
Step 5: Total displacement is the vector norm of the difference between
the starting position and the position of fruit detachment:
0
x
end
x S
S
x −
=
Δ
0
y
end
y S
S
y −
=
Δ
0
z
end
z S
S
z −
=
Δ
displacement = norm(Δx, Δy, Δz)
Figure 16. Algorithm used to determine the total duration from the be-
ginning of a pick until the point of separation
The average duration from the onset of linear acceleration
until fruit detachment varied from 0.28 to 0.41 s. The mean
displacement from the apple’s resting position until detach-
ment varied from 3 to 7 cm. The actual distance traveled by the
sensor during the pick is greater than the displacement because
of assumed deviations in the operator’s picking path from a
straight-line vector. The average linear velocities of the hori-
zontal pull and inclined pull picking motions were 0.14 ±0.17
m s-1
and 0.14 ±0.14 m s-1
, respectively. The average linear ve-
locities for the horizontal twist and inclined twist motions were
0.13 ±0.12 m s-1
and 0.13 ±0.11 m s-1
, respectively. Accounting
for uncertainty in both time and gyro angle at separation, for
the twisting motions the rotational velocity was approximately
130° ±70° s-1
. As was expected for human interaction with bi-
ological systems having significant variability in fruit shape,
size, orientation, and stem length, the standard deviations were
relatively high. Based on field observations, horticultural prac-
tices significantly influence the variability in duration and dis-
placement to fruit separation. For example, fruit located on
thinner and/or longer branches not secured to trellis wires had
to be pulled farther. Figure 18 shows time-lapse images of a
twisting pick. In figure 18a, the red rectangle outlines the trellis
wire, and the the pink shape shows that the apple’s supporting
branch and fruit spur are not secured to the wire. As the picking
motion is initiated (fig. 18b), the supporting branch is displaced
along with the apple. Compared to the average, this particular
apple was pulled a greater distance to detach it from the tree
(fig. 18c). Fruit distribution near main branches well secured
to trellis wires was ideal.
Figure 17. Means and standard deviations (σ) for fruit displacement
and time duration to the point of separation for all apple varieties.
To compare the four testing patterns and evaluate whether
they are feasible picking methods, it was also necessary to
determine rates of fruit bruising, stem pullouts, and adjoin-
ing spur detachment. In the fresh market apple industry, stem
pulls are considered undesirable because some studies have
shown that they may predispose certain apple cultivars to
disease (Janisiewicz and Peterson, 2004). Still, the im-
portance of stem attachment is a source of some debate
within the industry. More studies are needed to determine
conclusively if a stem pull that does not cause fruit damage
increases the likelihood of postharvest disease. If additional
testing shows that stem attachment is not critical, a signifi-
cant constraint of mechanical harvesting will be removed. A
picking sequence that severs the spur of the fruit is also prob-
lematic because it may remove next year’s fruiting structure
from the tree. Likewise, there would be a negative impact on
overall cycle time because an extra step would be required
to remove the spur from the fruit after picking to prevent fruit
punctures in the storage container. To ensure that the forces
applied to detach the fruit did not cause injury beyond USDA
fresh market standards, samples of each cultivar were scru-
tinized using USDA tolerances. Fifteen samples of each cul-
tivar were randomly selected and held at room temperature
for 24 h prior to inspection. Observations of the fruit using
the standards listed in table 3 revealed that none of the apples
showed signs of bruising.
Incidences of stem pulls and spur removal were also rec-
orded for each sample and are shown in figure 19. For Envy,
Fuji, and Cripps Pink, which are the varieties with the long-
est stems, there was a picking pattern that resulted in stem
attachment on approximately 85% of the fruit. However, re-
Displacement (m) σ Time (sec) σ
Envy 0.048 0.054 0.330 0.156
Fuji 0.033 0.025 0.313 0.116
Pacific Rose 0.059 0.058 0.362 0.182
Pink Lady 0.054 0.083 0.336 0.189
Envy 0.046 0.052 0.322 0.179
Fuji 0.051 0.036 0.377 0.119
Jazz 0.056 0.049 0.374 0.157
Pacific Rose 0.040 0.039 0.337 0.134
Pink Lady 0.034 0.017 0.293 0.080
Envy 0.041 0.027 0.328 0.095
Fuji 0.027 0.022 0.264 0.104
Pacific Rose 0.067 0.060 0.405 0.165
Pink Lady 0.046 0.060 0.303 0.192
Envy 0.055 0.038 0.378 0.125
Fuji 0.037 0.040 0.273 0.317
Jazz 0.035 0.024 0.307 0.106
Pacific Rose 0.041 0.030 0.337 0.119
Pink Lady 0.029 0.021 0.279 0.095
Horizontal Pull
Horizontal Twist
Inclined Pull
Inclined Twist

59(4): 745-758 755
Figure 18. Time-lapse images of a horizontal twist apple pick: (a) start of grasp, (b) midpoint of pick, and (c) instant of fruit detachment.
Table 3. U.S. standards for grades of apples (USDA, 2002)
Standards Section Specification
51.316: Injury • When any surface indentation exceeds 1/16 inch (1.5875 mm) in depth.
• When any surface indentation exceeds 1/8 inch (3.175 mm) in diameter; or
• When the aggregate affected area of such spots exceeds 1/2 inch (12.7 mm) in diameter.
• Bruises which are not slight and incident to proper handling and packing, and which are greater than:
o 1/8 inch (3.175 mm) in depth
o 5/8 inch (15.875 mm) in diameter
51.317: Damage • When any surface indentation exceeds 1/8 inch (3.175 mm) in depth.
• When the skin has not been broken and the aggregate affected area exceeds 1/2 inch (12.7 mm) in diameter; or
• When the skin has been broken and well healed, and the aggregate affected area exceeds 1/4 (6.35 mm) inch in diameter.
• Bruises which are not slight and incident to proper handling and packing, and which are greater than:
o 3/16 inch (4.7625 mm) in depth
o 7/8 inch (22.225 mm) in diameter
Figure 19. Fruit condition after picking. The number and percentages of apples with no stem, with stem, and with spur are shown for all 30 samples
of each cultivar by test condition. The legend for test conditions is shown in the lower right. The picking patterns that produced the highest rates
of stem attachment are shown in dark color.
(a) (b)
(c)

sults were more mixed for Pacific Rose. This variety grew in
an experimental orchard that was not as well maintained as
the commercial orchards. Many of the fruit grew on thin,
flexible branches distributed around the trunk of the tree,
which made it more difficult for the operator to apply con-
sistent picking motions. In addition, because the supporting
branches were thin, the entire branch was more likely to
break from the tree rather than just the fruit spur. A human
picker would most likely use both hands to pick a single fruit
in this system: one hand to hold the branch, and the other to
pick the fruit. Distribution of the fruit in relation to trellis
wires as well as differences in the strength of the supporting
vegetation will have a large influence on the success of ro-
botic apple harvesting.
The hand-picking dynamic analysis described in this arti-
cle was completed to aid in the development of effective ro-
botic apple picking methods that do not require sensory
knowledge of the apple’s orientation or stem location. Such
a system would perform what has been referred to as under-
sensed grasping. During the course of this study, it was de-
termined that stem length, fruit density, and pruning prac-
tices greatly influence the likelihood of stem localization.
The Cripps Pink and Fuji orchard used during testing em-
ploys a state-of-the-art eight-wire cultivation system and has
an international reputation for its pruning and thinning prac-
tices. A picture of a Cripps Pink row at this orchard is shown
in figure 20. Compared to the fruit shown in figure 1, these
fruit are evenly distributed, and vegetation has been pruned
from around the body of the fruit. The leaves remaining on
top of the apples are left to prevent sunburn. This system re-
duces the challenges associated with determining apple ori-
entation using visual sensing. Another parameter that may
help to visually determine the apple’s orientation is stem
length. Of the five cultivars analyzed in this article, Fuji had
the longest stem (fig. 21). While the complexities of robotic
manipulation remain the same, the extension of the stem be-
yond the stem cavity and a more consistent growing orienta-
tion make it more feasible to visually localize and apply
pressure against the stem during grasping. In addition, while
the focus of this article is undersensed grasping, it is noted
that longer stems may enable the use of harvesting end-ef-
fectors that detach the fruit with methods other than grasp-
ing. Zhao et al. (2011) developed an end-effector that cut the
stem of the Fuji variety in order to remove it from the tree.
This article does not describe visual localization of the stem,
so it is assumed that the stem was vertical and accessible to
the cutting device.
CONCLUSION
This work evaluated undersensed apple picking tech-
niques as candidates for potential implementation in a ro-
botic harvesting system that picks fruit with a grasping end-
effector. Using a human operator, four different picking pat-
terns were applied to five apple varieties growing in various
tree architectures. Experimental results included normal
contact forces on fingers, angle of rotation around the axis
of the forearm, and rates of stem detachment. For the Jazz
variety, the thumb experienced normal contact forces during
picking as high as 30 N. Design and analysis of a grasping
end-effector should ensure that the device can withstand sig-
Figure 20. Cripps Pink apples on a well-maintained, state-of-the-art
fruiting wall canopy architecture. The fruit are evenly distributed, and
occlusion from interfering vegetation is minimal.
Figure 21. (left) Fruit orientation was more consistent for fruit with longer stems; (right) Fuji variety has a relatively long stem that may be easier
to visually detect and locate.

59(4): 745-758 757
nificant external disturbances from the stem’s attachment to
the tree. Dynamic analysis of field data and controlled labor-
atory studies also indicated that peak normal forces occur at
the point of fruit detachment. Therefore, tactile sensors in a
robotic end-effector could potentially be used to determine
the point of fruit separation and minimize the path traveled
by the end-effector during harvesting. Dead reckoning
showed that each apple was picked in approximately 1/3 s.
For picking patterns with twisting, the average angle of fruit
rotation at the detachment point varied from 32° to 54°.
None of the picked fruit showed evidence of bruising. The
preferred picking method for each apple variety in terms of
stem attachment was also identified. Future work will in-
clude implementation of the studied picking patterns in a ro-
botic apple harvester.
ACKNOWLEDGEMENTS
This research was supported in part by USDA Hatch and
Multistate Project Funds (Accession Nos. 1005756 and
1001246), a USDA-NIFA competitive grant (Accession No.
1000339), and the Washington State University (WSU) Ag-
ricultural Research Center (ARC). Any opinions, findings,
and conclusions expressed in this publication are those of the
authors and do not necessarily reflect the view of the USDA
or Washington State University. The authors would also like
to acknowledge Allan Brothers, Inc., and the Auvil Fruit
Company for their support during field testing.
REFERENCES
Bac, C., Hemming, J., & Van Henten, E. (2014). Stem localization
of sweet-pepper plants using the support wire as a visual cue.
Comput. Electron. Agric., 105, 111-120.
http://dx.doi.org/10.1016/j.compag.2014.04.011
Baeten, J., Donne, K., Boedrij, S., Beckers, W., & Claesen, E.
(2008). Autonomous fruit picking machine: A robotic apple
harvester. In Field and Service Robotics (pp. 531-539). Springer
Tracts in Advanced Robotics, Vol. 42. Cham, Switzerland:
Springer International. http://dx.doi.org/10.1007/978-3-540-
75404-6_51
Bulanon, D. M., & Kataoka, T. (2010). Fruit detection system and
an end effector for robotic harvesting of Fuji apples. Agric. Eng.
Intl.: CIGR J., 12(1), 203-210.
Bulanon, D., Kataoka, T., Ota, Y., & Hiroma, T. (2001). A machine
vision system for the apple harvesting robot. Agric. Eng. Intl.:
CIGR J., 3, manuscript PM 01 006.
Cheney, W., & Kincaid, D. (2013). Numerical mathematics and
computing. (7th ed.) Boston, MA: Brooks/Cole.
Colton, S. (2007). The balance filter. Cambridge, Mass.:
Massachusetts Institute of Technology.
Cutkosky, M. R. (1989). On grasp choice, grasp models, and the
design of hands for manufacturing tasks. IEEE Trans. Robotics
Automat., 5(3), 269-279. http://dx.doi.org/10.1109/70.34763
Denavit, J., & Hartenberg, R. S. (1955). A kinematic notation for
lower-pair mechanisms based on matrices. Trans. ASME J. Appl.
Mech., 22, 215-221.
Flood, S. J. (2006). Design of a robotic citrus harvesting end effector
and force control model using physical properties and harvesting
motion tests. PhD diss. Gainesville: University of Florida.
Galinato, S., & Gallardo, R. K. (2011). 2010 Cost estimates of
producing pears in north central Washington. Extension Fact
Sheet FS031E. Pullman, WA: Washington State University.
Retrieved from
http://cru.cahe.wsu.edu/CEPublications/FS031E/FS031E.pdf
Gallardo, R. K., Taylor, M., & Hinman, H. (2010). 2009 Cost
esimates of establishing and producing Gala apples in
Washington. Extension Fact Sheet FS005E. Pullman, WA:
Washington State University. Retrieved from
http://cru.cahe.wsu.edu/CEPublications/FS005E/FS005E.pdf
Gonzalez-Barrera, A. (2015). More Mexicans leaving than coming
to the U.S. Washington, DC: Pew Research Center. Retrieved
from http://www.pewhispanic.org/files/2015/11/2015-11-
19_mexican-immigration__FINAL.pdf
Grand D’Esnon, A. (1985). Robotic harvesting of apples. ASAE
Paper No. 1-85. St. Joseph, MI: ASAE.
Janisiewicz, W. J., & Peterson, D. L. (2004). Susceptibility of the
stem pull area of mechanically harvested apples to blue mold
decay and its control with a biocontrol agent. Plant Disease,
88(6), 662-664. http://dx.doi.org/10.1094/PDIS.2004.88.6.662
Jiang, L., Zhu, B., Cheng, X., Luo, Y., & Tao, Y. (2009). 3D
surface reconstruction and analysis in automated apple stem-
end/calyx identification. Tran. ASABE, 52(5), 1775-1784.
Jidong, L., De-An, Z., Wei, J., Yu, C., & Ying, Z. (2012). Research
on trunk and branch recognition method of apple harvesting
robot. In Proc. Intl. Conf. Measurement, Information, and
Control (MIC) (pp. 474-478). Piscataway, NJ: IEEE.
http://dx.doi.org/10.1109/mic.2012.6273345
MIT. (2004). Inertial sensors, complementary filtering, and simple
Kalman filtering. Open courseware. Cambridge, Mass.:
Massachusetts Institute of Technology. Retrieved from
http://ocw.mit.edu/courses/aeronautics-and-astronautics/16-333-
aircraft-stability-and-control-fall-2004/lecture-
notes/lecture_15.pdf
Napier, J. R. (1956). The prehensile movements of the human hand.
J. Bone Joint Surgery, 38B(4), 902-913.
Nguyen, T. T., Van Eessen, D., Baerdemaeker, J., & Saeys, W.
(2012). Optimum detaching movement for apples-harvesting
robot. In Proc. Intl. Conf. Agric. Eng. (CIGR). Wallingford, UK:
CAB Direct.
Tong, J., Zhang, Q., Karkee, M., Jiang, H., & Zhou, J. (2014).
Understanding the dynamics of hand picking patterns of fresh
market apples. ASABE Paper No. 141898024. St. Joseph, MI:
ASABE.
USDA. (2002). United States standards for grades of apples:
Effective December 19, 2002. Washingon, DC: USDA
Agricultural Marketing Service. Retrieved from
http://www.ams.usda.gov/sites/default/files/media/Apple_Grade
_Standard%5B1%5D.pdf
USDA. (2014). Washington agriculture overview. Washingon, DC:
USDA National Agricultural Statistics Service. Retrieved from
http://www.nass.usda.gov/Quick_Stats/Ag_Overview/stateOver
view.php?state=WASHINGTON
USDA. (2015). Historic data on apples. Washingon, DC: USDA
National Agricultural Statistics Service. Retrieved from
http://www.nass.usda.gov/Statistics_by_State/Washington/Publi
cations/Fruit/Historic/93-wfsapples.pdf
Van Henten, E. J., Van Tuijl, B., Hoogakker, G.-J., Van Der Weerd,
M., Hemming, J., Kornet, J., & Bontsema, J. (2006). An
autonomous robot for de-leafing cucumber plants grown in a
high-wire cultivation system. Biosyst. Eng. 94(3), 317-323.
http://dx.doi.org/10.1016/j.biosystemseng.2006.03.005
Wouter Bac, C., van Henten, E. J., Hemming, J., & Edan, Y.
(2014). Harvesting robots for high-value crops: State-of-the-art
review and challenges ahead. J. Field Robotics, 31(6), 888-911.
http://dx.doi.org/10.1002/rob.21525
WUSTL. (2010). Peripheral nerve surgery: A resource for surgeons.
St. Louis, MO: Washington University School of Medicine in
St. Louis. Retrieved from
http://nervesurgery.wustl.edu/ev/hand/ulnar/Pages/DorsalInteros

seous.aspx
Zhang, B., Huang, W., Wang, C., Gong, L., Zhao, C., Liu, C., &
Huang, D. (2015). Computer vision recognition of stem and
calyx in apples using near-infrared linear-array structured light
and 3D reconstruction. Biosyst. Eng., 139, 25-34.
http://dx.doi.org/10.1016/j.biosystemseng.2015.07.011
Zhao, D., Lu, J., Ji, W., Zhang, Y., & Chen, Y. (2011). Design and
control of an apple harvesting robot. Biosyst. Eng., 110(2), 112-
122. http://dx.doi.org/10.1016/j.biosystemseng.2011.07.005
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