Portable weighing system for monitoring pickerefficiency during manual harvest of sweet cherry

Yiannis G. Ampatzidis • Matthew D. Whiting • Bo Liu •

Patrick A. Scharf • Francis J. Pierce

Published online: 12 September 2012� Springer Science+Business Media, LLC 2012

Abstract Harvest costs are significant for tree fruit producers and, yet, not well studied.

In this paper a prototype system for measuring harvest worker efficiency in sweet cherry

(Prunus avium L.) is presented. This weighing system consists of a digital weighing

platform compatible with standard commercial fruit bins and a data logger interfaced to a

wireless radio. Weight data were transmitted every 5 s, filtered and stored to a laptop

computer (database). System functionality and reliability were evaluated in five orchards in

cooperation with three commercial growers. Preliminary tests showed that the system did

not interfere with normal harvest activities and that the efficiency of pickers and picking

teams varied within and across orchard blocks, (e.g. 0.75 to 2.87 kg/min), depending on

their experience and skills (e.g. 1.37 kg/min for skilled workers vs. 0.64 kg/min for

unskilled). Further, the mean picking rate of ‘Skeena’ without pedicels (stem-free) was

1.15 kg/min, almost 50 % greater than fruit with stems (mean = 0.75 kg/min) when

picked by the same crew and orchard.

Keywords Labor monitoring � Harvest efficiency � Yield map � Zigbee

Introduction

The efficiency of labor during sweet cherry (Prunus avium L.) harvest is affected by many

factors including orchard architecture, fruit yield and capability of the worker, among

others. For sweet cherry, harvest costs account for 50–60 % of annual production costs

(Seavert et al. 2008) and there is great interest to reduce these via horticultural and/or

mechanical means. A prototype cherry harvester that removes fruit at the fruit-pedicel

Y. G. Ampatzidis (&) � B. Liu � P. A. Scharf � F. J. PierceCenter for Precision and Automated Agricultural Systems, Washington State University,Prosser, WA 99350, USAe-mail: yiannis.ampatzidis@wsu.edu

M. D. WhitingDepartment of Horticulture and Landscape Architecture, Washington State University,Prosser, WA 99350, USA

123

Precision Agric (2013) 14:162–171DOI 10.1007/s11119-012-9284-3

junction was developed in the late 1990s and showed promise for improving labor effi-

ciency (Peterson and Wolford 2001). In general, however, the efficiencies of mechanical

harvest for tree fruit are not realized commercially due to key challenges including

selective harvest, tree and fruit damage and the cost of machinery (Holt 1999; Sarig et al.

1999). Hence, almost all specialty crops for fresh market consumption are harvested

manually (Seavert et al. 2008; Tsatsarelis 2003).

Tree stature and orchard architecture are important factors affecting harvest efficiency

(Strik et al. 2003; Strik and Buller 2002). New training systems, with simplified archi-

tecture (pedestrian and planar systems), for sweet cherry have been developed with the

goals of improving production efficiency and producing high quality fruit (Whiting et al.

2005; Sansavini and Lugli 1998). Systems that utilized size-controlling rootstocks planted

at high tree density have achieved earlier orchard productivity while still yielding good

quality fruit (Lang 2005; Whiting and Smith 2007) though there are no empirical reports on

their impact on harvest labor efficiency and safety. Whiting (2009) introduced a new

training system for sweet cherry, the Upright Fruiting Offshoots (UFO) architecture,

intended to improve worker safety and harvest efficiency by simplifying pruning/training

and creating a planar tree form compatible with automation technologies such as platforms

(e.g. mechanical/mechanical-assist harvest platforms, pruning platforms, etc.). Again, there

are no published data on harvest efficiency for this system.

Studies of labor efficiency during harvest of tree fruit crops are limited. Acquiring reliable

data in the field is necessary for spatial-variability studies (e.g. precision agriculture). Data

monitoring systems would help farmers understand and evaluate crop production, and inform

their decision-making process (Ampatzidis 2010; Ampatzidis et al. 2008). One of the main

difficulties when implementing an automatic monitoring system for specialty crop products

is the acquisition of relevant data from the first link of the production chain to the farm gate

(Ampatzidis and Vougioukas 2009). A significant roadblock to acquiring position data in

orchards is that GPS data are typically unavailable under dense canopies (Ampatzidis et al.

2009; Heidman and Rosa 2005). Other techniques for data collection in the orchards, which

utilize combinations of radio frequency identification (RFID), GPS and barcode registration

technologies (Ampatzidis and Vougioukas 2009; Ampatzidis et al. 2009; Triolo et al. 2007;

Lee et al. 2010), mobile-based solutions (Cunha et al. 2010; Kuflik et al. 2009; Luvisi et al.

2009; Morais et al. 2008) and yield monitoring systems (Whitney et al. 1999; Schueller et al.

1999; Salehi et al. 2000) have been evaluated in the past.

Ampatzidis et al. (2011) described a wearable position recording system for orchard

workers, based on a smart inertial measurement unit and a small barcode reader that

established worker’s position in relation to trees. All of these applications have been used

for traceability and precision management purposes in specialty crops, yet little research

has investigated worker efficiency during harvest. As a prelude to comprehensive study of

factors affecting harvest efficiency in sweet cherry, our research program developed and

tested a prototype system for recording the weight of standard orchard bins during harvest

in commercial sweet cherry orchards.

Materials and methods

Sweet cherry harvest process

The annual sweet cherry harvest is one of the most labor-intensive of all agricultural

endeavors due to large tree size and high number of fruit per tree. During the harvesting

Precision Agric (2013) 14:162–171 163

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procedure, laborers move along rows picking fruit manually with a slight twisting-snap-

ping motion. Typically U.S. laborers, pick fruit by the pedicel in clusters of 2–4, and place

fruit into a metal or plastic bucket with a capacity of about 7–12 kg, secured over their

shoulders with straps. The pickers carry and use aluminum ladders ranging in height from

2.4 to 4 m to access fruit. Once the bucket is filled, pickers dump fruit into a larger

receptacle (either a plastic lug or bin designed to hold ca 13.60 or 180 kg, respectively). If

used, full lugs are dumped subsequently into bins and bins are collected by tractor, loaded

on a trailer, and delivered to a local packing shed for sorting, cleaning and packaging.

Weighing and data collection system

The system consists of a digital commercial 1.5 9 1.5 m platform scale (model: DWP

11KR, Floor Scale, DigiWeigh, Chino, CA, USA) mounted on a custom steel frame that

was fitted into a standard Macroplastics� cherry harvest bin (1.22 9 1.22 9 0.3 m) that

facilitated transport of the system (Fig. 1a). The digital scale had a capacity of 4 530 kg

with a resolution of 0.5 kg. A standard harvest bin was centered on the scale to collect fruit

as pickers emptied their harvest bags. The digital interrogator controlling the platform

scales and receiving weight data was interfaced to a wireless radio networked to a remote

coordinator consisting of a Zigbee wireless radio and a custom data logger that collected,

filtered and stored the data (Fig. 1b). The wireless system automatically transmitted weight

data every 5 s to a remote radio (server) connected to a laptop computer (program was

developed in C??). The data were filtered (processing and smoothing) and stored to a

database. Three similar weighing systems (Fig. 1) were developed and used to calculate

the harvest rate per crew during manual harvest of cherry trees.

Experimental design

System functionality and reliability were evaluated in five commercial sweet cherry

orchards in cooperation with three growers. On each test day, two or three systems were

deployed in the orchard with one to five of the growers’ picking crew per system. The bin

weighing systems were integrated into the standard harvest operation using standard sweet

cherry harvest bins. The bin weighing systems were transported by forklift and placed in

the orchard alleyways near picking crews. In every orchard the picking crews harvested

fruit from only two rows of trees (those on either side of the bin). When the bins were full

they were removed by forklift and transported to a central collection area. The weighing

system was then moved along the alleyway and an empty bin was placed on the scale.

There were no apparent delays to the harvest process during the replacement of bins.

The first orchard near Moxee, WA, USA, was a mature (20? year) ‘Bing’/Mazzard

orchard with trees trained to a multiple leader, open-center architecture comprised of 4 or 5

main scaffolds per tree (Fig. 2a). Trees were spaced 3 and 5 m within and between rows,

respectively. On 25 June, 2010 three bin scale systems were used and there were four

workers per system. The pay rate was $40 per bin.

The second orchard near Moxee, WA, USA, contained 7-year-old ‘Benton’/‘Gisela�6’

trees that were spaced 5 m between rows and 3.3 m within a row. The trees were trained in

N–S rows to a central leader architecture (Fig. 2b) with two tiers of near-horizontal fruiting

wood. The experiments were conducted on 1 July, 2010, using two weighing systems (two

pickers for one system and one for another). The pay rate was $60.00 per bin.

The third orchard, in Zillah, WA, USA, was comprised of 40? year old ‘Bing’/Mazzard

trees planted at 6 9 6 m and trained to a multiple leader, open-center architecture with 4 or

164 Precision Agric (2013) 14:162–171

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5 main uprights per tree bearing weaker lateral shoots where most of the fruit was borne

(Fig. 2c). Two weighing systems were used in this experiment on 2 July 2010, one with

four and the other with five pickers. The pay rate was $60.00 per bin.

The fourth orchard was located near Buena, WA, USA, with 5-year-old ‘Cowiche’/

‘Gisela�6’ trees planted at an inter-row spacing of 2 m and intra-row spacing of 3 m. The

trees were trained to the UFO system, a planar architecture comprised of unbranched

vertical fruiting wood (Fig. 2d). Trees had filled about 70 % of their allotted space. We

deployed two weighing systems with four pickers each on 5 July, 2010. The pay rate was

$45.00 per bin.

The fifth orchard located near Pasco, WA, USA, was comprised of 10-year-old ‘Ske-

ena’/Mazzard trees trained to a Y-trellised architecture with 4 m between trees and 6 m

Fig. 1 a Digital commercial platform scale mounted on a custom steel frame fitted into a bin; b the wholesystem: digital weighing scale, interrogator and data logger with Zigbee wireless radio

Precision Agric (2013) 14:162–171 165

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between rows (Fig. 2e). The efficiency of harvesting ‘Skeena’ with or without pedicels (i.e.

stem-free), when picked by the same crew in the same orchard, was compared on 9 and 10

July, 2010. Two weighing systems were used each day. Initially, four pickers for the first

and three for the second system collected fruit with pedicels and then three pickers per

system harvested the fruit without pedicels (stem-free). The pay rate was $65.00 for

cherries picked with stems and $45.00 without stems.

Results and discussion

It is intuitive that orchard architecture, fruit load, and other factors will affect the efficiency

of the harvest process yet there are no reports studying these for tree fruit. As a prelude to

Fig. 2 Commercial sweet cherry orchards in which the weighing system was evaluated: a ‘Bing’ at Moxee,b ‘Benton’ at Moxee, c ‘Bing’ at Zillah, d ‘Cowiche’ at Zillah and e ‘Skeena’ at Pasco

166 Precision Agric (2013) 14:162–171

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further investigations into key factors affecting harvest efficiency, we designed, built, and

tested a weighing system during harvest in commercial sweet cherry orchards. The

weighing system accurately recorded the weight of fruit and timing of every fruit drop as

pickers emptied their buckets directly into bins (Fig. 3). The output from the weighing/

recording system of a typical single-bin shows a clear step-like trend in bin ? fruit weight

over time, though there was obvious noise (Fig. 3a). The sharp peaks were caused gen-

erally by the picker leaning on the bin or scale when unloading fruit to the bin. This noise

was eliminated after processing and smoothing (Fig. 3b). Recording weight data every 5 s

appears to be too frequent, causing significant noise. It would be advantageous to record

weight data after stabilization of the scale following every dump of fruit. Our data show

that this stabilization occurs generally within 10 s of every dump.

We assessed key attributes of harvest efficiency including total bin weight when full,

average harvest rate per picker, mean weight of fruit from every dump into the bin, and the

Fig. 3 Example of a cherry harvest bin weight over time to assess harvest efficiency. a Raw output from theweighing system and b the same data after filtering (processing and smoothing)

Precision Agric (2013) 14:162–171 167

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average labor cost for picking from fruit weight and time data. There was significant

variability among orchards for each element of harvest efficiency (e.g. Tables 1, 2). This

may be attributed to several factors including the pickers’ abilities (different crews were

used at each orchard), orchard architecture, crop load, etc. Our preliminary evaluations

show a range in average bin weight of more than 40 kg across all orchards. This was

surprising considering each orchard used the same bins and ostensibly filled them to a

similar degree. Fruit size may have contributed to this range since smaller fruit settle and

pack better in the bins, leading to heavier bins when full. Fruit pedicels clearly reduce

product density within bins—we found that bins of stem-free fruit were about 22 % heavier

than bins with stemmed fruit of similar size. There is no industry standard for determining

when bins are full, it is judged visually by the orchard manager. In every orchard studied,

the picking crew was reimbursed per bin so it was in their best interest to minimize the

quantity of fruit needed to fill the bin. In fact, over-filling bins is undesirable and likely

would damage fruit. Orchard managers found the accurate fruit weight data from our

system useful for determining when bins were full.

Picking rate (kg/picker/min) also varied within and particularly, across orchards

(Tables 1, 2). For example, within orchard 1 (‘Bing’/Mazzard), harvest rate varied between

0.66 and 1.04 kg/picker/min, a 1.6-fold range from the same picking crew, that is likely

due to variability in fruit load (i.e. light crop density causing low efficiency). There

appeared to be little role of picker fatigue on harvest efficiency because rates at the

beginning and end of the day were similar. We hypothesize that variability in harvest rate

within an orchard was due primarily to differences in fruit load and tree size. The vari-

ability among orchards is likely due to picker skill, fruit load and canopy architecture. We

observed that a picker with excellent ladder placement skills picked fruit at 1.37 kg/min in

the same orchard that two less skilled workers picked fruit at 0.64 kg/min, (‘Benton’/

‘Gisela6’, 2nd orchard). Our current studies of harvest efficiency deploy a consistent

harvest crew to eliminate this variable. Across orchards, we documented a range in picker

efficiency of 0.75–2.87 kg/picker/min (Table 2). The high picking rates in the ‘Cowiche’

orchard were not surprising—this orchard was trained to a pedestrian (i.e. most fruit

accessible from the ground) and planar architecture. Yield was not particularly high in this

orchard, estimated at ca. 9 tons/hectare, but the fruit was highly accessible and no ladders

were required. The orchards with low picking efficiency (orchards 1, 2 and 5) were similar

by having large tree size though architecture was very different. In each of these orchards

pickers carried 3–4 m ladders to access fruit and this ladder time reduced efficiency.

Table 1 Harvest data for a ‘Bing’/Mazzard orchard picked on 25 June, 2010

Pickingcrew #

Bin#

Fruitweight(kg/bin)

Harvest rate(kg/person/min)

Mean fruitweight perbucket (kg)

Range in fruitweight perbucket (kg)

Fruit harvestedper picker perbin (kg)

Pickingcost/kg

1 1 128.37 0.80 9.17 7.50–13.50 32.09 $0.31

1 2 139.71 0.78 9.98 6.00–13.50 34.93 $0.29

2 3 141.97 0.94 8.87 7.00–14.50 35.49 $0.28

2 4 141.52 1.04 9.43 8.50–14.00 35.38 $0.28

2 6 142.43 0.66 9.49 7.50–14.50 35.61 $0.28

3 5 141.42 1.04 9.43 8.50–14.00 35.35 $0.28

3 7 138.34 0.93 9.88 8.50–13.50 34.59 $0.29

There were four pickers picking into the bin and the pay rate was $40 per bin

168 Precision Agric (2013) 14:162–171

123

Tab

le2

Har

ves

td

ata

(mea

n±

SD

)fo

ral

lth

eex

per

imen

ts

Orc

har

dC

ult

ivar

Bin

sh

arv

este

dP

ick

ers

nu

mb

erp

erw

eig

hin

gsy

stem

Fru

itw

eig

ht

(kg

/bin

)H

arv

est

rate

(kg

/per

son

/min

)M

ean

fru

itw

eig

ht

per

bu

cket

(kg

)

Ran

ge

infr

uit

wei

ght

per

bu

cket

(kg

)

Fru

ith

arv

este

dp

erp

ick

er(k

g)

Pic

kin

gco

st/k

g

1st

Bin

g7

4,

4,

41

39

.50

±4

.94

0.8

8±

0.1

49

.46

±0

.38

6.0

0–

14

.50

34

.78

±1

.24

0.2

9±

0.0

1

2n

dB

ento

n7

2,

11

32

.00

±6

.60

0.8

9±

0.4

09

.70

±1

.02

5.5

0–

13

.50

88

.45

±3

5.3

20

.36

±0

.20

3rd

Bin

g8

4,

51

54

.00

±7

.49

1.9

4±

0.2

81

2.6

5±

4.0

65

.50–

15

.50

35

.52

±3

.60

0.3

7±

0.1

5

4th

Co

wic

he

94

,4

14

2.0

0±

6.6

92

.87

±0

.64

12

.08

±1

.32

6.0

0–

14

.00

35

.47

±6

.42

0.2

6±

0.1

6

5th

Sk

een

a1

04

,3

15

1.5

0±

4.2

10

.75

±0

.16

10

.28

±0

.58

5.0

0–

14

.50

44

.37

±7

.37

0.4

3±

0.0

1

5th

Sk

een

aw

ith

out

stem

10

3,

31

85

.00

±6

.65

1.1

5±

0.1

41

2.7

5±

1.3

06

.00–

15

.00

56

.86

±5

.55

0.2

7±

0.0

7

Precision Agric (2013) 14:162–171 169

123

Interestingly the mean picking rate for stem-free ‘Skeena’ cherries was 1.15 kg/min,

more than 50 % greater than fruit with stems (mean = 0.75 kg/min) when picked by the

same crew in the same orchard. This may be attributed to the faster picking motion when

the pedicels remain on the tree–stem-free fruit are pulled off the pedicels without regard

for the stem. There is interest in developing mechanical solutions to sweet cherry harvest

(Whiting and Smith 2007) and this result suggests that growers could improve harvest

efficiency and profitability by harvesting appropriate cultivars (i.e. those with ease of

abscission from the pedicel) stem-free without need for mechanical assist. The increased

speed of harvest of stem-free cherries reduced the costs of picking by about $0.16 per kg

compared with fruit with stems.

Conclusion

For tree fruit crops, harvest is generally the most expensive process, accounting for half of

all expenses in some cases. Accurate data on the role of key variables such as orchard

design and crop load, among others, would improve the ability of growers to budget and

inform new orchard system developments. The system we present herein was useful for

studying harvest efficiency of commercial picking crews in sweet cherry orchards. There

was significant variability in harvest efficiency metrics within and among orchards that we

attribute to fruit load, tree architecture and stature, as well as picker skill.

Acknowledgments This research was supported in part by Washington State University AgriculturalResearch Center federal formula funds, Project No. WNP0745, No. WNP0728 and No. WNP0420 receivedfrom the U.S. Department of Agriculture National Institutes for Food and Agriculture, USDA-SpecialtyCrop Research Initiative project 2009-02559, Washington State University Center for Precision & Auto-mated Agricultural Systems. Any opinions, findings, conclusions, or recommendations expressed in thispublication are those of the author(s) and do not necessarily reflect the view of the U.S. Department ofAgriculture.

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