Development of a cucumber leaf picking device for greenhouse production

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Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/issn/15375110

Research Paper: AE—Automation and Emerging Technologies

Development of a cucumber leaf picking device for greenhouse production T. Otaa,, J. Bontsemab, S. Hayashia, K. Kubotaa, E.J. Van Hentenb,c, E.A. Van Osb, K. Ajikia a

Bio-oriented Technology Research Advancement Institution, 1-40-2 Nisshin, Kita, Saitama 331-8537,Japan Wageningen UR Greenhouse Horticulture, P.O.Box 16, 6700 AA Wageningen, The Netherlands c Wageningen University, P.O. Box 17, 6700 AA Wageningen, The Netherlands b

art i cle info

A leaf picking device for cucumbers was designed and evaluated. The picking device is manually operated but can be used as a picking tool for a robot. The device consisted of a

Article history:

picking rotor composed from knives and brushes, a motor and a vacuum cleaner. The

Received 19 June 2006

performance of removal, cutting, torque and shredding was investigated in the laboratory

Accepted 19 September 2007

experiments. In the greenhouse experiments, the performance of picking and cutting was

Available online 9 November 2007

investigated. The results were as follows: (1) the highest removal success rate was achieved at a rotation speed of 1000 min1, a rotor configuration of ‘two knives and two brushes’ and an insertion speed of 50 mm s1; (2) in this mechanical setting, the percentage of the summation of the smooth cut surface of the leaf stalk and the smooth cut surface with small skin was 90%; (3) the required torque was 0.09–0.96 N m and the average particle size of the shredded leaves was 7.3–21.2 cm2; (4) the percentages of area of the dropped particles to the average area of leaves was 10–16%; (5) the average execution time per leaf was 1.1–2.3 s. & 2007 IAgrE. Published by Elsevier Ltd. All rights reserved.

1.

Introduction

Cucumber is an important fruit in the Netherlands and Japan. In 2003, the production of cucumbers in the Netherlands was 430,000 t on a production area of 639 ha (LEI & CBS, 2005) and in Japan it was 684,100 t on a production area of 14,100 ha (MAFF, 2007). Cucumbers are mainly grown in two training systems, the pinching training system and the high-wire training system. With the pinching training system and Dutch greenhouses larger than 1 ha, the top of the main stem is pinched after reaching the horizontal wire at 2–3 m, after which the first lateral branch and the second lateral branch are allowed to grow. After reaching the ground the two lateral shoots are topped and two new side shoots are allowed to grow,

sprouting from the wire area. In Japan with the pinching training system, the top of the main stem is pinched at the horizontal wire at a height of 1.5–2 m. Following pinching, the first lateral branch and the second lateral branches are allowed to grow whilst the other branches are removed. The purposes of the picking leaves are to improve light conditions, increase airflow and increase the vitality of the plants. In the high-wire training system used in the Netherlands, the main stem is grown continuously and the top of the stem is maintained at 0–40 cm below the wire, which hangs at 3.5–4 m. When the top of the plant is within 40 cm of the horizontal wire, the main stem is lowered by moving the stem sideways. Consequently, the main stem of the plant is moved for 30–40 cm through the row. The leaves at the lower part of the stem are picked before lowering the top of the stem.

Corresponding author.

E-mail address: [email protected] (T. Ota). 1537-5110/$ - see front matter & 2007 IAgrE. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.biosystemseng.2007.09.021

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This training system is relatively new for Dutch cucumber growers although it was studied in the mid-1990s for robotic harvesting and was introduced by a few growers. Subsequently, most of these growers returned to traditional growing systems because labour costs were too high. However, recently the high-wire training system for cucumbers was introduced again, this time for the purposes of increasing the quality of the fruits. In Japan there is a similar training system to the Dutch high-wire training system. The top of the stem is hung at 1.5–2 m because greenhouses are usually lower than in the Netherlands, then the top is moved sideways for 20–30 cm and the leaves at the lower part of the stem are removed. In the Netherlands, the leaves of the cucumber plants are removed to increase airflow within the crop canopy with the pinching training system. With the high-wire system, the lowest leaves are also removed to decrease the risk of diseases after lowering of the plants (stems and leaves touch the ground) and also to increase the airflow through the crop canopy. A cucumber plant can grow sufficiently with 18–20 leaves on the plant. In Japan, leaves are removed from the whole plant, both to increase the airflow around the plants and to improve the level of available light for the crop. In both countries, leaf picking is currently carried out by hand but labour is costly and qualified workers are difficult to source. More than 30% of the total production costs are spent on wages in the Netherlands (Van Henten et al., 2006) and leaf picking work consumes 30–50% of the total labour input of cucumber production in Japan (Ota et al., 2005). Research for the automation and mechanisation of fruit vegetable production started in the early 1980s (Sarig, 1993). Many basic robotic studies have been carried out for tomato fruit harvesting (Kondo et al., 1996; Hayashi & Sakaue, 1996) and cucumber fruit harvesting (Arima & Kondo, 1999). Some autonomous fruit picking robots were developed for cucumber harvesting (Van Henten et al., 2002, 2003a, 2003b), eggplant (aubergine) harvesting and tomato harvesting (Hayashi et al., 2005). Hemming et al. (2004) developed a leaf picking system that used image processing for leaf detection. A de-leafing robot for cucumber was also developed, based on the cucumber picking robot as a platform (Van Henten et al., 2006) by using an information-based design method (Van Tuijl et al., 2004). The de-leafing robot did not have any leaf collection function but dropped the leaves onto the ground after cutting a leaf stalk using an end-effector. The time required for the de-leafing robot to detect the leaf and cut the leaf stalk was approximately 140 s per leaf. Consequently, if efficiency is to be increased, an improvement in the automated leaf picking process is required. The desired time for the automated leaf picking should be in the same order of magnitude as the time for the manual picking time, which is 5–10 s per leaf. Following preliminary experiments in a Japanese greenhouse (Ota et al., 2005), a prototype of a cucumber leaf picking device was designed and evaluated in Dutch greenhouses using an adapted vehicle. The objectives of this research were (1) to develop a laboratory experimental apparatus for the leaf picking device; (2) to conduct picking tests in the laboratory to evaluate its performance; (3) to analyse the relationship between mechanical conditions and the removal

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performance; (4) to conduct a greenhouse experiment with cucumbers grown with the pinching training system and to investigate performance under practical conditions.

2.

Materials and methods

2.1.

Laboratory experimental apparatus

The picking rotor was composed of stainless-steel knives and/or brushes made from stainless steel wire (Fig. 1). The picking rotor was designed and manufactured in such a way that the configuration of knives and brushes could be changed. Knives and brushes were alternately spaced at equal distances around the circumference of the rotor at 901 intervals. Leaves were extracted into the housing by the pulling action of the rotating rotor and suction from the vacuum cleaner. Leaves were shredded by the rotor in the housing. The apparatus (Fig. 2) consisted of a picking rotor contained within a housing unit, an electric motor (model MSM590001C, Oriental Motor Co., Japan) and a torque transducer (model TP-10KCE, Kyowa Electronic Instruments Co., Japan), a linear motion actuator (model SA-S6AM, SUS Co, Japan), a pipe and a vacuum cleaner (model SPV-102ECH, Suiden Co., Japan). The torque transducer was connected between the motor and the picking rotor. During each test, signals of torque were recorded with 100 Hz using a data acquisition system. The motor and the linear motion actuator were operated by setting variable speeds. The vacuum cleaner had a flow rate of 3 m3 min1. At the base of the leaf stalk, a leaf was attached onto the linear motion actuator, which could be inserted into the centre of the opening of the rotor unit. The apparatus was designed to suck the leaf into the unit by the picking rotor while the leaf was approached by the linear motion actuator. Leaves were removed from the vacuum cleaner via a shutter, which, when opened, allowed the sucked leaves to drop into a hopper.

143

Picking rotor Rotation Insertion of leaf

Knife

Suction 111

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382

Brush

Fig. 1 – Schematic diagram of the leaf picking rotor; all dimensions in mm.

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Fig. 2 – Laboratory experimental apparatus; (a) picking rotor and housing; (b) electric motor and torque transducer; (c) linear motion actuator; (d) pipe; (e) computer and (f) vacuum cleaner.

Fig. 3 – Prototype of picking device; (a) picking rotor with motor; (b) vacuum cleaner; (c) container and (d) vehicle.

2.2.

Prototype leaf picking device

A prototype of the device (Fig. 3) comprised a picking rotor with a motor (model BD-120, Ryobi Co., Japan), a pipe, a vacuum cleaner and a vehicle. The device was based on the laboratory experimental apparatus. The device used the floormounted heating pipes for guidance and support while moving through the greenhouse. The operator moved the picking unit horizontally towards a leaf and inserted the edge of a leaf into the opening (Fig. 4). The size of the picking unit was such that it could be supported and operated by hand.

2.3.

Fig. 4 – Picking unit at the greenhouse experiments; (a) picking rotor in housing and (b) motor.

Leaf

Insertion Opening direction

Leaf picking unit

Fig. 5 – Schematic diagram of laboratory experiments.

Laboratory experiments

The laboratory experiments (Fig. 5) were carried out in October 2005 using the leaves obtained from a commercial greenhouse (3 ha) in the city of Loo, the Netherlands. The

leaves of cucumber (Cucumis sativus cv. Euphoria) to be tested were cut at the leaf stalk base at a height of 1–1.5 m in the canopy. The cut leaves from the plant stem were inserted into the opening of the picking housing, leaf tip first at the same

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centreline of the leaf and the opening until the leaf stalks were inserted. The following tests were carried out:

as ‘unable to extract the leaf because the removed leaf was clogged in the housing’.

(1) 5 different rotation speeds of 500, 750, 1000, 1250, 1500 min1; (2) 4 different configurations of the rotor: 2 knives, 4 knives, 2 knives and 2 brushes, 2 brushes; and (3) 5 different insertion speeds of 50, 75, 100, 125, 150 mm s1.

2.3.2.

In each test, 20 leaves were inserted individually. The tested leaves had a leaf width of 35–45 cm. The relationship between the mechanical condition and the picking performance was investigated.

2.3.1.

Removal performance

To investigate the performance of the device, the removal success rate was calculated by measuring the leaf weight before the insertion and the weight of those parts of the leaf that were not sucked into the vacuum cleaner. The removal success rate R in % was defined as the following equation: R ¼ 100 ðWa  Wb Þ=Wa ,

(1)

where Wa is a leaf weight in kg before the insertion trial and Wb is a remaining leaf weight in kg. Table 1 shows the failure categories observed during the insertion process. The source and number of the removal failures were observed and noted. The removal failures were classified into 3 categories: category 1 was defined as ‘unable to cut because the stem tangled around the rotor’, category 2 was defined as ‘unable to remove the full leaf because the rotor inserted only part of the leaf’ and category 3 was defined

Table 1 – Failure categories observed during the insertion process Failure category 1 2 3

Description

Unable to cut because the stem tangled around the rotor (tangled) Unable to remove the full leaf because the rotor inserts only part of the leaf (partial removal) Unable to extract the leaf because the removed leaf was clogged in the housing (clogging)

Table 2 – Cut surface types of the leaf stalk observed after the insertion Surface category 1 2 3 4

Description

Smooth Smooth with a small part of the skin remaining Rough Leaf stalk was not cut and part of the leaf was left on the stalk

Cutting performance

Table 2 shows different cut surface types of the leaf stalk observed after the insertion. The cut surfaces were classified into 4 categories: category 1 smooth; category 2 smooth with a small part of the skin remaining; category 3 rough; and category 4 leaf stalk not cut and part of the leaf left on the stalk.

2.3.3.

Torque performance

The torque was measured during the insertion of the leaves by the torque transducer. Data were transferred to the computer through an amplifier. The maximum torque of each leaf insertion was recorded.

2.3.4.

Shredding performance

The shredded particle area after the suction into the cleaner was measured by image-processing software (Cosmos32, Library Co., Japan). After picking a single leaf, the shredded leaf was removed from the vacuum cleaner. Images of the particles were taken by a digital camera with a calibration scale. The particles smaller than 1 cm2 area were eliminated as noise.

2.4.

Greenhouse experiments

2.4.1.

Experimental greenhouse

Greenhouse experiments were carried out in October and November 2005 in two commercial greenhouses in the cities of Loo (3 ha) and Asten (7 ha), the Netherlands. The cucumber variety in Loo and Asten was ‘Euphoria’ and ‘Confida RZ’, respectively. In both greenhouses, cucumbers were grown in the pinching training system. The vertical main stem of each plant was twisted into a string. The vertical crop strings were connected to the horizontal wire at approximately 2 m height from the ground. The horizontal wire supported the side stem of the plant. The horizontal crop wire was connected to the greenhouse construction at the sidewalls and supported at the trellis. Table 3 shows the leaf features during the greenhouse experiments. The picking device was tested by picking 135 leaves from the plants. The tested leaves were 35 large leaves from the main stem in Asten, 50 medium leaves in Loo and 50 small-sized leaves in Asten. The average leaf widths of the small leaf, the medium leaf and the large leaf were 219, 318 and 375 mm, respectively. The average leaf areas of the small leaf, the medium leaf and the large leaf were 309, 753 and 1004 cm2, respectively.

2.4.2.

Dropped leaves

In the greenhouse experiments, it was observed that several parts of the leaves dropped onto the ground during picking. The area of the dropped leaf particles was measured to evaluate the picking performance in the greenhouse by the image processing.

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Table 3 – Leaf features during the greenhouse experiments Type

Small Medium Large

Variety

Average leaf width, mm

Average leaf length, mm

Average leaf area, cm2

Attached stem

Confida RZ Euphria Confida RZ

219 318 375

188 316 336

309 753 1004

Side stem Side stem Main stem

2.4.4.

Execution time

The execution time for each picking test was measured by video analysis. The start of the execution was defined as the time when the leaf was inserted. The end of execution was defined as the time when nothing of the leaf was seen anymore. The number of insertions per leaf was counted.

3.

Results and discussion

3.1.

Laboratory experiments

3.1.1.

Removal performance

The average value of R was the highest at 1000 min1 (Fig. 6). At the rotation speed of 500 min1, both the average and the minimum values of R were the lowest. As for the different configurations of knives and brushes, the data were measured at the rotation speed of 1000 min1 and at the insertion speed of 50 mm s1 based on the result of the value of R at the various rotation speeds (Fig. 6). The configuration of two knives and two brushes had the highest average and the highest minimum value of R. As for the various insertion speeds, the average value of R tended to decrease slightly with increasing insertion speed (Fig. 6). The minimum value of R also tended to decrease with an increasing insertion speed. It was seen that faster insertion speeds increased the amount of leaf that could not be removed. The relative number of failures was the lowest at 1000 min1 (Fig. 7). If the rotation speed was higher or lower than 1000 min1, the number of failures increased. Category 2 failures were seen with all rotation conditions and category 1 failures were seen at 500, 750 and 1500 min1. It appears that at lower speeds the device was unable to cut and shred the leaf stalk as well as the leaf. As a result, the uncut leaf stalk and the leaf became tangled around the rotor. Considering the different configurations, the number of failures with the configuration of two knives and two brushes at 10%, was the lowest for all configurations (Fig. 7). The relative number of failures with the configuration of two or four knives was larger than that of the configuration of knives and brushes. It is considered that brushes increase the

80 60 40 20 0 0

500

1000

1500

2000

Rotation speed, min−1

(a)

Removal success rate, %

Cutting performance

Cut surfaces after picking were measured to evaluate the cut performance by the same method in the laboratory experiments (Section 2.3.2).

100 80 60 40 20 0 2 knives

2 knives 2 brushes 2 brushes Configuration of knives and brushes

(b)

4 knives

100 Removal success rate, %

2.4.3.

Removal success rate, %

100

80 60 40 20 0 0

(c)

50

100

150

200

Insertion speed, mm s−1

Fig. 6 – Removal success rate (a) at various rotation speeds, configuration of 2 knives and insertion speed 50 mm s1; (b) at different configurations, rotation speed 1000 min1 and insertion speed 50 mm s1 and (c) at various insertion speeds, rotation speed 1000 min1 and configuration of 2 knifes and 2 brushes; K, average success rate; J, minimum success rate.

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50 40 30 20 10 0 500

750

1250

1500

100 80 60 40 20 0 750 1000 1250 Rotation speed, min−1

500 (a)

10 2 brushes

2 knives 2 brushes

0

Configuration of knives and brushes

Relative number of cut surface type, %

20

(b)

100 80 60 40 20 0 2 knives

2 knives 2 brushes 2 brushes Configuration of knives and brushes

(b)

50 40 30 20 10 0 50

75 100 125 Insertion speed, mm s−1

150

Fig. 7 – Relative number of failure (a) at various rotation speeds, configuration of 2 knives and insertion speed 50 mm s1; (b) at different configurations, rotation speed 1000 min1 and insertion speed 50 mm s1; (c) at various insertion speeds, rotation speed 1000 min1, and configuration of 2 knives and 2 brushes; ’, tangled; , partial removal; &, clogging.

tension on leaves. However, using brushes only reduced cutting performance and caused clogging. As insertion speed was increased, the number of failures increased (Fig. 7). Only when insertion speed was higher than 100 mm s1, clogging was observed. At higher insertion speeds, more leaves were removed per second but the increased quantity caused clogging at the connection between the housing and the suction pipe. In case of the ‘partial removal’, only the central part of the leaf was inserted and removed, which was probably due to the feature that the opening is too small. Increasing the insertion speed did not solve the problem of partial removal.

Relative number of surface type, %

Relative number of failure, %

60

(c)

1500

30

2 knives

Relative number of failure, %

1000

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Rotation speed, min−1

(a)

4 knives

Relative number of failure, %

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Relative number of cut surface type, %

386

4 knives

100 80 60 40 20 0 50

(c)

75

100

125

Insertion speed, mm

150

s−1

Fig. 8 – Relative number of cut surface (a) at various rotation speeds, configuration of 2 knives and insertion speed 50 mm s1; (b) at different configurations, rotation speed 1000 min1 and insertion speed 50 mm s1 and (c) at various insertion speeds, rotation speed 1000 min1 and configuration of 2 knives and 2 brushes; ’, smooth; , smooth with a small part of skin remaining; &, rough; , leaf stalk was not cut.

3.1.2.

Cutting performance

The cutting performance at various rotation speeds was evaluated using the configuration of 2 knives and at an insertion speed of 50 mm s1 (Fig. 8). The sum of ‘smooth’ and ‘smooth with small skin’ cuts reached a maximum at 1000 and 1250 min1. The relative numbers of the surface type ‘rough’ reached a maximum at 500 min1. Therefore, it was

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3.1.3.

1 0.8 0.6 0.4 0.2 0 0

Shredding performance

The particle sizes at 500 min1 tended to be larger than the particle sizes at the other higher speeds (Fig. 10). The reason is probably that the rotor attacked the leaf too slowly, resulting in larger particles compared to those at higher speeds. At speeds from 750 to 1250 min1, leaf particle sizes were smaller in area than around 20 cm2. Collecting shredded leaves results in much smaller volumes being stored in the collection container compared to when whole leaves are collected. The volume of the leaves picked by the device was about 20% of the volume of the leaves picked by hand (Ota

250

(a)

500 750 1000 1250 Rotation speed, min−1

1500

1750

150

175

1 0.8 0.6 0.4 0.2 0

Torque performance

The maximum torque data, which had a removal success rate of 100%, were plotted (Fig. 9). The torque required appeared to be less than 1.0 N m. The ranges of torque at 1500 min1 were less than with the other speeds because data at 1500 min1 were less than that at the other speeds. It appears that variations in leaf size caused the variations in torque. At 500 and 1500 min1, torque data greater than 0.6 N m were not plotted, because at these speeds the large leaves, which might cause the high torque, resulted in removal failure. The measured torque was much lower than reported values for other agricultural field machinery. For example, the torque of a forage harvester was approximately 100–500 Nm (Savoie et al., 2002). It was therefore concluded that a small electric motor could be used for the picking device in greenhouse use. As for the relationship between the insertion speed and the torque, the range of the torque of the insertion speed 150 mm s1 was small (Fig. 9) and tended to decrease with the increasing insertion speed. Concerning the relationship between the configuration of the rotor and the torque, the torque was less than 1 N m. No clear difference between the configurations was observed.

3.1.4.

1.2 Maximum torque, Nm

concluded that the rotation speeds around 1000–1250 min1 were adequate for cutting the leaf stalks by a knife. A smooth surface cut by the knives of the rotor will produce a similar surface to that cut manually by using scissors or a knife. As a result, a smooth surface cut by the device will not increase the likelihood of plant diseases any more than that due to manual picking. Thin and ‘small skin’ cut surfaces will dry quickly and will also not increase fungal diseases. However, the effect of ‘rough’ cut surfaces on plant diseases has yet to be investigated. To investigate the type of cut surfaces with the different configurations, the rotation speed and the insertion speeds were set at 1000 min1 and 50 mm s1, respectively (Fig. 8). The sum of ‘smooth’ and ‘smooth with small skin’ cut surfaces with the configurations of 2 knives, 4 knives and 2 knives and 2 brushes were approximately 90%. However, more than 70% of the cut surfaces with 2 brushes were ‘rough’ or unable to be cut. The brushes could not cut the leaf stalk smoothly using their shearing action. To investigate the relative numbers of the types of cut surfaces at the various insertion speeds, the rotation speed was set at 1000 min1 and with 2 brushes and 2 knives, respectively (Fig. 8). It appeared that there was an optimum insertion speed between 75 and 125 mm s1.

387

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Maximum torque, Nm

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0

25

50

75

100

125

Insertion speed, mm s−1

(b)

Fig. 9 – Maximum torque (a) at various rotation speeds, configuration of 2 knives and insertion speed 50 mm s1 and (b) at various insertion speeds, rotation speed 1000 min1 and configuration of 2 knifes and 2 brushes.

et al., 2005). This has the advantage of enabling more leaves to be retrieved. No effect on the particles sizes caused by the different insertion speeds could be discerned (Fig. 10). Using the same rotation speed of 1000 min1 and the configuration of two knives and two brushes, the average particle sizes were 5–30 cm2 at the different insertion speeds. Concerning the relationship between the configurations of knives and brushes and particle size, there were no clear differences. Average particle sizes were 5–30 cm2 using the different rotor configurations, a rotation speed of 1000 min1 and an insertion speed of 50 mm s1.

3.2.

Greenhouse experiments

3.2.1.

Dropped leaves

On several occasions during the greenhouse experiments, parts of the leaves were dropped onto the ground from the opening of the housing when they were into the housing (Table 4). The relative numbers of the dropped leaves were 20–28%. It appeared that the relative number of the dropped leaves did not depend on the leaf size. The average areas of dropped leaves were 48–102 cm2 per leaf. The area of the dropped particles was 10–16% of the average area of the

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leaves. It is considered that these percentages of leaf particles will not effect disease development. In greenhouses in the Netherlands, the ground is usually covered with a synthetic liner. Most of the dropped particles will dry without causing any disease. In greenhouses in Japan, the floor usually consists of wet soil or a mulching film cover. In the case of a wet soil, even a small number of leaves have to be removed from the greenhouse.

70

Particle size, cm2

60 50 40 30 20 10

3.2.2.

0 0

500

(a)

1000 1500 Rotation speed, min−1

2000

30 25 Particle size, cm2

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20 15

Cutting performance

The relative numbers of ‘smooth’ cut surfaces for each leaf size were around the same value of 30% (Table 5). It is considered that the relative number of ‘smooth’ does not depend on the leaf size. The relative number of ‘rough’ cut surfaces for large leaves was higher than for the other 2 leaf sizes. The relative numbers of ‘rough’ cut surfaces for the greenhouse experiments were higher than that for the laboratory experiment. The reason is that in the laboratory experiment the leaf stalk was fixed on the slider of the linear motion actuator during the insertion, but in the greenhouse experiments, the leaf stalk was not fixed.

10 3.2.3.

5 0 0

50

(b)

100 150 −1 Insertion speed, mm s

200

Fig. 10 – Shredded particle size (a) at various rotation speeds, configuration of 2 knives and insertion speed 50 mm s1 and (b) at various insertion speeds, rotation speed 1000 min1 and configuration of 2 knives and 2 brushes.

Table 4 – Results of dropped particles of leaves for greenhouse experiments Leaf size

Number of dropped particles of leaf, piece

Relative number of dropped leaves, %

Average area of dropped particles per leaf, cm2

14 10 7

28 20 20

48 86 102

Small Medium Large

Execution time

The average number of insertions tended to increase with an increasing leaf size (Table 6). The average execution time per leaf was 1.2 s lower for smaller leaves than for larger leaves. The average execution times of medium- and large-sized leaves were around 2.3 s per leaf. The picking speed by the device was almost the same as the manual cutting speed of 1–3 s per leaf by using a knife, and faster than the manual cutting speed about 3-5 s per leaf when using scissors. Around 84% of the small leaves were picked using one insertion and 16% of leaves were fully picked by two insertions (Table 7). The average execution time for one insertion was 0.9 s. This time was much shorter than the time measured from the insertion speed of 50 mm s1 used in laboratory experiments. One reason is that the leaves used in

Table 6 – Results of execution time for leaf picking Leaf Size

Average number of insertions per leaf

Average execution time per leaf, s

1.2 1.9 2.9

1.1 2.3 2.3

Small Medium Large

Table 5 – Results of cut surfaces for greenhouse experiments Leaf Size

Small Medium Large

Table 7 – Results of execution time for leaf picking of small leaves and the number of insertions

Relative number of cut surface type, % Smooth

Smooth with small skin

Rough

Total

32 32 34

38 44 29

30 24 37

100 100 100

No. of insertions

1 2

No. of leaves, pieces

Relative no. of leaves, %

Average execution time per leaf, s

42 8

84 16

0.9 2.0

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Table 8 – Results of execution time for leaf picking of medium leaves and the number of insertions No. of insertions

1 2 3 4 5 Total

No. of leaves, Pieces

Leaves removed, %

Average execution time per leaf, s

23 15 9 2 1 50

46 30 18 4 2 100

1.1 2.2 4.1 7.2 7.2

Table 9 – Results of execution time for leaf picking of large leaves and the number of insertions No. of insertions

1 2 3 4 5 Total

No. of leaves, pieces

Leaves removed, %

Average execution time per leaf, s

8 13 11 2 1 35

23 37 31 6 3 100

0.9 1.9 4.4 5.6 8.6

the greenhouse experiments were smaller than the leaves used in the laboratory experiments. Another reason is that the side stems were not fixed on the wire and the leaves had flexible connections with side stems. As a result leaves were extracted into the housing by the rotation of the picking rotor and the vacuum at a greater speed. The peripheral speed of the picking rotor was calculated as 5.2 m s1, which is much faster than the adequate insertion speed of 50–100 mm s1. It was an advantage that the leaves could be moved into the housing at a greater rate, because the picking efficiency was better than at the optimum insertion speed. Nearly half of the leaves were picked in one insertion. Using one insertion leaves were picked in 1 s, which is almost the same as for the small leaf (Table 8). One to five actions per leaf picking were observed. When four and five insertions were necessary, the leaves were usually larger. The relative number of leaves removed for one insertion was 23% (Table 9). Large leaves were found on the main stem and leaves were tightly attached to the crop string. To pick a large leaf by one insertion, the width of the opening and the housing would have to be larger than 350 mm, which would make the operation handling of the head more difficult.

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389

ber leaf picking. Following the laboratory experiments, the prototype picking device for the greenhouse use was further developed and evaluated. In the laboratory experiments, it was found that the highest removal success rate was achieved at a rotation speed of 1000 min1, using a rotor with the configuration ‘two knives and two brushes’ and an insertion speed of 50 mm s1. With this design, the relative number of the ‘smooth’ cut surfaces of the leaf stalk together with the ‘smooth cut with small skin’ surfaces was 90%. At this percentage the torque required for leaf picking was from 0.09 to 0.96 N m. This torque is much lower than found in other field farming machines. The average area of the shredded particle after leaf picking varied from 7.3 to 21.2 cm2. In the greenhouse experiments of the pinching training system, it was found that percentages of area of the dropped particles to the average area of leaves were 10–16%. It is considered that this will not increase the risk of diseases outbreaks in practise. The average execution time for one small leaf was 1.1 s per leaf. The average execution time for medium-and large-sized leaves was 2.3 s. The device can easily be adapted for the high-wire training system where it will improve the picking efficiency more than with the pinching training system, since with the high-wire training system about three leaves per plant have to be picked each week, while with the pinching training system only one or two leaves per plant have to be picked. Only low torque is needed for leaf picking, which implies that only a small motor is necessary for the device, which will create a useful tool for manual operation. Because of its low weight, the device could be used as a picking tool for robotic systems. The picking speed of the new device is comparable with what is required for leaf picking currently carried out in the Netherlands and significantly faster than that currently found in Japan.

Acknowledgements The cooperation within this project is a result of the agreement on cooperation between the Wageningen University and Research Centre and the Bio-oriented Technology Research Advancement Institution for which the authors are very grateful. Further, the authors wish to thank the growers Toon van Sadelhoff, Willy and Gert van Bussel for providing plants, and especially leaves, and fruitful discussions on the development of the leaf picking device; the staff members of PPW De Haaff for their care for the young cucumber plants and DLV advisory worker Jan Boersma for fruitful discussions and his initiatives to find cooperative growers. R E F E R E N C E S

4.

Conclusions

A leaf picking device for cucumbers, grown in a greenhouse, was developed and tested in a laboratory setting and in greenhouses. The laboratory experimental apparatus was developed to determine the optimum mechanism for cucum-

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