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Development and Improvement of Parallel Yield Sensors for Measuring Individual Weights of Onion Bulbs:
Development and Improvement of Parallel Yield Sensors for Measuring Individual Weights of Onion Bulbs: Arimura R*. Shoji K*. Kawamura T*.
*Graduate School of Agricultural Science, Kobe University, 1-1, Rokkodai-cho, Nada-ku, Kobe, Hyogo, 657-8501, Japan (Tel:+81-78-803-5913; e-mail: [email protected])
Abstract: Measuring the weights of individual tubers, bulbs, and fruits on the harvester may bring progress to Precision Agriculture and enhance their market value. In order to measure the individual weights on an onion picker in situ, we developed two impact-based parallel yield sensors and carried out the experiments in different cropping years. Both sensors (the indirect sensor and the direct sensor) consisted of plural load cells and sponge rubber cushions. The indirect sensor was set near the end of the conveyor of the picker and a bounce plate consisting of an acrylic plate and polyurethane cushion was set between the indirect sensor and the conveyor. On the other hand, the direct sensor was set directly under the end of the conveyor. Calibration were first carried out off the field to relate the impulses received by the sensor to the individual weights of the bulbs, resulted in the standard error of 26.3g and 16.1g, the root-mean squared relative error of 8.8% and 12.2% for the indirect sensor and the direct sensor, respectively. As the validation, field measurements were carried out. The bulbs were harvested in 12 and 26 containers (20kg each) for the indirect sensor and the direct sensor, respectively. After the harvest, we weighed all the bulbs in each container manually and performed the Kolmogorov-Smirnov tests of the distributions of the actual and the estimated weights at the significance level of 5 %. Using the indirect sensor, the similarity between the actual and estimated weight distributions was accepted for 9 of 12 containers. The difference in the estimated number of bulbs was about ±3 per container, caused mainly by the same bulb colliding twice or by no bulb contacting the sensor at all. On the other hand, using the direct sensor, the similarity of the distributions was accepted for 23 of 26 containers. The overestimation of the number of bulbs was a maximum of 40 bulbs per container containing about 100 bulbs. Keywords: Precision Agriculture, Yield sensor, Individual weights, Onion bulbs, Weight distribution
1. INTRODUCTION Implementation of Precision Agriculture may enhance the yield and the market value of the products. Yield monitoring or mapping, is one of the important techniques for Precision Agriculture, for which sensor technique is essential to measure mass flow of the products. When handling heavy products, however, measuring individual weights of them will be important for yield information, because the weight distribution is useful for minimizing the portion of their irregular sizes through proper field practice. We set the final objective as yield mapping in the fields of heavy products, and in this paper, we report two yield sensors developed for measuring the individual weights in situ. Onion, a typical bulbar crop, was selected for the investigation because its irregular shape including the roots and the apex is a challenge in the development of such yield sensors.
2.1 Onion Picker An onion picker (YANMAR, TP90) was used in this study (Fig. 1). The speed of the conveyor was 0.15 m s-1, and we assumed that the onion bulbs didn’t drop on the conveyor while they were lifted to be placed into containers. Although the width of the input end of the conveyor (operating width) was 820 mm, the output end was 470 mm to fit into the Output end of containers. the conveyor
2. MATERIAL AND METHOD Input end of the conveyor Fig. 1. The onion picker (YANMAR, TP90)
2.2 Concept of the Yield Sensor The yield sensors developed in this study consisted of plural load cells and impact plates in parallel as progress type of impact-based yield sensor described by Qarallah et al. (2008). The width of impact plates was set to that of the smallest onion bulbs (about 40 mm) which can be carried by the conveyor and the plates were allocated in parallel with the clearance of 1 mm with each other to distinguish simultaneous impacts. The sensors enabled signal separation when plural onion bulbs collided at the same time as in Fig. 2
The bounce plate was designed so that the bulbs accidentally enrolled into the conveyor would not collide to the sensors. This is because the enrolment of the long stalk or roots of bulbs into the conveyor was observed and sensors were broken when the sensor was placed directly under the end of the conveyor (Fig. 4).
Fig. 4. A case of bulbs being caught between the output end of the conveyor and the sensor directly placed under it
(a)
In the current design, the simultaneous impacts of a maximum of three bulbs was supposed be distinguished by the seven channels, whereas the maximum number of bulbs which can be carried by in one ladder of the conveyor was four. Therefore, a guide, consisting two pipes and a rubber plate, was set on the conveyor to limit the number of simultaneous impacts (Fig. 5). ：Touched area of onion bulbs (b) Fig. 2. Concept of the yield sensor: (a) A two-channel yield sensor incapable of distinguishing simultaneous impacts; (b) A multi-channel yield sensor capable of distinguishing simultaneous impacts
Guide
2.2.1 Indirect Sensor As in Fig. 3, the indirect sensor, consisting of seven sets of load cells (TEAC, TUCR-500N), impacts plates (38 mm 130 mm 5 mm, aluminium) and sponge rubber cushions (10 mm in thickness), were placed near the end of the conveyor. A bounce plate, consisting of an acrylic plate (400 mm 200 mm 5 mm) and polyurethane cushion (50 mm in thickness), was placed between the sensor and the conveyor.
Bounce plate
Onion bulb
Indirect sensor
Conveyor
Fig. 3. The indirect sensor and the bounce plate
Fig. 5. A guide to limit the number of the bulbs at the end of the conveyor 2.2.2 Direct Sensor As an alternative, as in Fig. 6, the direct sensor, consisting of ten sets of load cells (hard steel cantilevers of 38 mm × 208 mm with 4 strain gauges each near the fixed end) and sponge rubber cushions (10 mm in thickness), was placed under the end of the conveyor.
The outputs of each impact were integrated to calculate impulse p to relate to the weight of the bulb as below:
p
Conveyor
t1
0
F (t )dt
1 fs
t1 f s
F
i
(1)
i 0
where F is the force on the sensor, fs the sampling rate, t1 the integral time, i the sample number counted from the start. Both positive and negative signals were integrated, as this procedure had resulted in better calibration than integrating either side of the signal. The integral time was set at 0.2 s when the amplitude of the output securely extinct.
Onion bulbs Direct sensor Fig. 6. The direct sensor In order to avoid the sensor potentially broken due to enrolment of long stalks or roots of the bulbs into the conveyor, the fixed end of the cantilever was set under the conveyor and covered with a protection plate. (Fig. 7).
2.4 Calibration Calibration was carried out with a set of 25 onion bulbs, weighing between 119 g and 528 g and between 83 g and 393 g for the indirect sensor and the direct sensor, respectively, freshly harvested in a field of Hyogo Prefectural Institute for Agriculture, Forestry and Fisheries. Each onion bulb was weighed by electronic balance and regularly arranged on the conveyor. Simple proportional regression was performed to relate the impulses received by the sensor to the individual weights of the bulbs as below:
w p Protection plate
where p is the impulses received by the sensor, w the individual weights of the bulbs,α the calibrated parameter by the least-squares method. 2.5 Field Measurement
Fig. 7. Setting style of the direct sensor 2.3 Data Acquisition and Processing The output was recorded into a PC-card recorder (TEAC, DR-C1MK2). Using the indirect sensor, the sampling rate of 500 Hz and a digital low-pass filter was set at the cut-off frequency of 50 Hz as the natural frequency of the sensor was about 100 Hz. Mechanical vibration was compensated for by an accelerometer (TEAC, 708) as described by Shoji et al. (2002). Using the direct sensor, the sampling rate of 200 Hz and the filter was set at 20 Hz as the natural frequency of the sensor was about 50 Hz. The output of the sensor showed a typical damped oscillation (Fig. 8). 50
Sensor output (N)
Channel 1 Channel 2
30 20 10 1 7. 28
17 . 3
17 . 32
1 7 . 3 4
1 7. 36
1 7. 3 8
17 . 4
17 . 4 2
17 . 4 4
1 7 . 4 6
0.2
-10 -20
As the validation, field experiments were carried out, where the output of the sensor was recorded and the bulbs were harvested in 12 and 26 containers (ca. 60 bulbs and 20 kg each) for the indirect sensor (June 2011) and the direct sensor (June 2012), respectively. As over 60 bulbs were stored into each container, relating each impulse received by the sensor to each weight of the bulb was not feasible even with video judgments. Therefore, after the harvest, all the bulbs in each container were weighed manually, and the Kolmogorov – Smirnov tests (Schröer et al., 1995) were performed of the actual and the estimated weight distribution. 3. RESULTS AND DISCUSSION 3.1 Calibration
40
0
(2)
Time (s)
Fig. 8. A typical output of the indirect sensor
The calibration of the indirect sensor showed standard error of 26.3 g, the root-mean squared relative error of 8.8 % and the coefficient of determination of 0.92 (n=25). On the other hand, the calibration of the direct sensor showed standard error of 16.1 g, the root-mean squared relative error of 12.2 % and the coefficient of determination of 0.77 (n=25). The coefficient of the direct sensor was smaller than that of the indirect sensor due to narrower range of the weights of the bulbs.
Average weight of bulbs (g)
The indirect sensor showed the deference between the actual and estimated weight distributions for 3out of 12 containers at the significant level of 5 %. Underestimating of the average weights of bulbs was outstanding, caused mainly by the decrease in the momentum of bulbs as soil clod and weed on the bounce plate (Fig. 9). 400 350 300 250 200 150 100 50 0
Actual Estimated
As in Fig. 12, overestimation of the number of bulbs, maximum of 40 bulbs, was outstanding, caused mainly by collisions of soil clod and weed to the sensors. On the other hand, underestimation of the numbers, maximum of 6 bulbs, was the main cause of the no impact, which was rarely observed with video judgment. Estimated number of bulbs
3.2 Field Measurement
160 140 120 100 80 60 40 20 0
Actual Estimated 0
5
10
15
20
25
30
Experimental number (container #) 0
5
10
15
Fig. 12. Estimated number of bulbs using the direct sensor
Experimental number (container #)
4. CONCLUSIONS
As in Fig. 10, the number of miscounted bulbs was about ±3 per container with the indirect sensor, caused mainly by the same bulb striking twice between the bounce plate and the sensor (double impact) or by not contacting the sensor at all (no impact). Most of the no impacts were observed upon the enrolments of the long stalk or roots into the conveyor, or upon the insufficient rebound of the bulb with the stalk on the bounce plate, whereas the double impacts occurred due to irregular or oversized shape of the bulbs.
When plural onion bulbs collided at the same time, by installing a parallel sensor, separation of the signal and the estimation of individual weights were possible. With the indirect sensor, using a bounce plate, no impact or double impact were observed and underestimation of the average weights of the bulbs was conspicuous. With the direct sensor, not using the bounce plate, the collisions of soil clod and weed to the sensors were observed and overestimation of the number was not negligible.
Estimated number of bulbs
Fig. 9. Average weights of bulbs using the indirect sensor
REFERENCES
80 70 60 50 40 30 20 10 0
Actual Estimated
0
5
10
15
Experimental number (container #)
Fig. 10. Estimated number of bulbs using the indirect sensor
Average weight of bulbs (g)
The direct sensor showed the deference between the actual and estimated weight distributions for 3out of 26 containers at the significant level of 5 % (Fig. 11). 300 250 200 150 100
Actual Estimated
50 0 0
5
10
15
20
25
30
Experimental number (container #)
Fig. 11. Average weights of bulbs using the direct sensor
Qarallah, B., Shoji, K., Kawamura, T. (2008). Development of a yield sensor for measuring individual weights of onion bulbs. Biosystems Engineering, 100, 511-515. Schröer, G., Trenkler, D. (1995). Exact and randomization distributions of Kolmogorov-Smirnov tests two or three samples. Computational Statistics & Data Analysis, 20, 185-202. Shoji, K., Kawamura, T., Horio H. (2002). Impact-based grain yield sensor with compensation for vibration and drift. Journal of Japanese Society of Agricultural Machinery, 64 (5), 105-115.
*Graduate School of Agricultural Science, Kobe University, 1-1, Rokkodai-cho, Nada-ku, Kobe, Hyogo, 657-8501, Japan (Tel:+81-78-803-5913; e-mail: [email protected])
Abstract: Measuring the weights of individual tubers, bulbs, and fruits on the harvester may bring progress to Precision Agriculture and enhance their market value. In order to measure the individual weights on an onion picker in situ, we developed two impact-based parallel yield sensors and carried out the experiments in different cropping years. Both sensors (the indirect sensor and the direct sensor) consisted of plural load cells and sponge rubber cushions. The indirect sensor was set near the end of the conveyor of the picker and a bounce plate consisting of an acrylic plate and polyurethane cushion was set between the indirect sensor and the conveyor. On the other hand, the direct sensor was set directly under the end of the conveyor. Calibration were first carried out off the field to relate the impulses received by the sensor to the individual weights of the bulbs, resulted in the standard error of 26.3g and 16.1g, the root-mean squared relative error of 8.8% and 12.2% for the indirect sensor and the direct sensor, respectively. As the validation, field measurements were carried out. The bulbs were harvested in 12 and 26 containers (20kg each) for the indirect sensor and the direct sensor, respectively. After the harvest, we weighed all the bulbs in each container manually and performed the Kolmogorov-Smirnov tests of the distributions of the actual and the estimated weights at the significance level of 5 %. Using the indirect sensor, the similarity between the actual and estimated weight distributions was accepted for 9 of 12 containers. The difference in the estimated number of bulbs was about ±3 per container, caused mainly by the same bulb colliding twice or by no bulb contacting the sensor at all. On the other hand, using the direct sensor, the similarity of the distributions was accepted for 23 of 26 containers. The overestimation of the number of bulbs was a maximum of 40 bulbs per container containing about 100 bulbs. Keywords: Precision Agriculture, Yield sensor, Individual weights, Onion bulbs, Weight distribution
1. INTRODUCTION Implementation of Precision Agriculture may enhance the yield and the market value of the products. Yield monitoring or mapping, is one of the important techniques for Precision Agriculture, for which sensor technique is essential to measure mass flow of the products. When handling heavy products, however, measuring individual weights of them will be important for yield information, because the weight distribution is useful for minimizing the portion of their irregular sizes through proper field practice. We set the final objective as yield mapping in the fields of heavy products, and in this paper, we report two yield sensors developed for measuring the individual weights in situ. Onion, a typical bulbar crop, was selected for the investigation because its irregular shape including the roots and the apex is a challenge in the development of such yield sensors.
2.1 Onion Picker An onion picker (YANMAR, TP90) was used in this study (Fig. 1). The speed of the conveyor was 0.15 m s-1, and we assumed that the onion bulbs didn’t drop on the conveyor while they were lifted to be placed into containers. Although the width of the input end of the conveyor (operating width) was 820 mm, the output end was 470 mm to fit into the Output end of containers. the conveyor
2. MATERIAL AND METHOD Input end of the conveyor Fig. 1. The onion picker (YANMAR, TP90)
2.2 Concept of the Yield Sensor The yield sensors developed in this study consisted of plural load cells and impact plates in parallel as progress type of impact-based yield sensor described by Qarallah et al. (2008). The width of impact plates was set to that of the smallest onion bulbs (about 40 mm) which can be carried by the conveyor and the plates were allocated in parallel with the clearance of 1 mm with each other to distinguish simultaneous impacts. The sensors enabled signal separation when plural onion bulbs collided at the same time as in Fig. 2
The bounce plate was designed so that the bulbs accidentally enrolled into the conveyor would not collide to the sensors. This is because the enrolment of the long stalk or roots of bulbs into the conveyor was observed and sensors were broken when the sensor was placed directly under the end of the conveyor (Fig. 4).
Fig. 4. A case of bulbs being caught between the output end of the conveyor and the sensor directly placed under it
(a)
In the current design, the simultaneous impacts of a maximum of three bulbs was supposed be distinguished by the seven channels, whereas the maximum number of bulbs which can be carried by in one ladder of the conveyor was four. Therefore, a guide, consisting two pipes and a rubber plate, was set on the conveyor to limit the number of simultaneous impacts (Fig. 5). ：Touched area of onion bulbs (b) Fig. 2. Concept of the yield sensor: (a) A two-channel yield sensor incapable of distinguishing simultaneous impacts; (b) A multi-channel yield sensor capable of distinguishing simultaneous impacts
Guide
2.2.1 Indirect Sensor As in Fig. 3, the indirect sensor, consisting of seven sets of load cells (TEAC, TUCR-500N), impacts plates (38 mm 130 mm 5 mm, aluminium) and sponge rubber cushions (10 mm in thickness), were placed near the end of the conveyor. A bounce plate, consisting of an acrylic plate (400 mm 200 mm 5 mm) and polyurethane cushion (50 mm in thickness), was placed between the sensor and the conveyor.
Bounce plate
Onion bulb
Indirect sensor
Conveyor
Fig. 3. The indirect sensor and the bounce plate
Fig. 5. A guide to limit the number of the bulbs at the end of the conveyor 2.2.2 Direct Sensor As an alternative, as in Fig. 6, the direct sensor, consisting of ten sets of load cells (hard steel cantilevers of 38 mm × 208 mm with 4 strain gauges each near the fixed end) and sponge rubber cushions (10 mm in thickness), was placed under the end of the conveyor.
The outputs of each impact were integrated to calculate impulse p to relate to the weight of the bulb as below:
p
Conveyor
t1
0
F (t )dt
1 fs
t1 f s
F
i
(1)
i 0
where F is the force on the sensor, fs the sampling rate, t1 the integral time, i the sample number counted from the start. Both positive and negative signals were integrated, as this procedure had resulted in better calibration than integrating either side of the signal. The integral time was set at 0.2 s when the amplitude of the output securely extinct.
Onion bulbs Direct sensor Fig. 6. The direct sensor In order to avoid the sensor potentially broken due to enrolment of long stalks or roots of the bulbs into the conveyor, the fixed end of the cantilever was set under the conveyor and covered with a protection plate. (Fig. 7).
2.4 Calibration Calibration was carried out with a set of 25 onion bulbs, weighing between 119 g and 528 g and between 83 g and 393 g for the indirect sensor and the direct sensor, respectively, freshly harvested in a field of Hyogo Prefectural Institute for Agriculture, Forestry and Fisheries. Each onion bulb was weighed by electronic balance and regularly arranged on the conveyor. Simple proportional regression was performed to relate the impulses received by the sensor to the individual weights of the bulbs as below:
w p Protection plate
where p is the impulses received by the sensor, w the individual weights of the bulbs,α the calibrated parameter by the least-squares method. 2.5 Field Measurement
Fig. 7. Setting style of the direct sensor 2.3 Data Acquisition and Processing The output was recorded into a PC-card recorder (TEAC, DR-C1MK2). Using the indirect sensor, the sampling rate of 500 Hz and a digital low-pass filter was set at the cut-off frequency of 50 Hz as the natural frequency of the sensor was about 100 Hz. Mechanical vibration was compensated for by an accelerometer (TEAC, 708) as described by Shoji et al. (2002). Using the direct sensor, the sampling rate of 200 Hz and the filter was set at 20 Hz as the natural frequency of the sensor was about 50 Hz. The output of the sensor showed a typical damped oscillation (Fig. 8). 50
Sensor output (N)
Channel 1 Channel 2
30 20 10 1 7. 28
17 . 3
17 . 32
1 7 . 3 4
1 7. 36
1 7. 3 8
17 . 4
17 . 4 2
17 . 4 4
1 7 . 4 6
0.2
-10 -20
As the validation, field experiments were carried out, where the output of the sensor was recorded and the bulbs were harvested in 12 and 26 containers (ca. 60 bulbs and 20 kg each) for the indirect sensor (June 2011) and the direct sensor (June 2012), respectively. As over 60 bulbs were stored into each container, relating each impulse received by the sensor to each weight of the bulb was not feasible even with video judgments. Therefore, after the harvest, all the bulbs in each container were weighed manually, and the Kolmogorov – Smirnov tests (Schröer et al., 1995) were performed of the actual and the estimated weight distribution. 3. RESULTS AND DISCUSSION 3.1 Calibration
40
0
(2)
Time (s)
Fig. 8. A typical output of the indirect sensor
The calibration of the indirect sensor showed standard error of 26.3 g, the root-mean squared relative error of 8.8 % and the coefficient of determination of 0.92 (n=25). On the other hand, the calibration of the direct sensor showed standard error of 16.1 g, the root-mean squared relative error of 12.2 % and the coefficient of determination of 0.77 (n=25). The coefficient of the direct sensor was smaller than that of the indirect sensor due to narrower range of the weights of the bulbs.
Average weight of bulbs (g)
The indirect sensor showed the deference between the actual and estimated weight distributions for 3out of 12 containers at the significant level of 5 %. Underestimating of the average weights of bulbs was outstanding, caused mainly by the decrease in the momentum of bulbs as soil clod and weed on the bounce plate (Fig. 9). 400 350 300 250 200 150 100 50 0
Actual Estimated
As in Fig. 12, overestimation of the number of bulbs, maximum of 40 bulbs, was outstanding, caused mainly by collisions of soil clod and weed to the sensors. On the other hand, underestimation of the numbers, maximum of 6 bulbs, was the main cause of the no impact, which was rarely observed with video judgment. Estimated number of bulbs
3.2 Field Measurement
160 140 120 100 80 60 40 20 0
Actual Estimated 0
5
10
15
20
25
30
Experimental number (container #) 0
5
10
15
Fig. 12. Estimated number of bulbs using the direct sensor
Experimental number (container #)
4. CONCLUSIONS
As in Fig. 10, the number of miscounted bulbs was about ±3 per container with the indirect sensor, caused mainly by the same bulb striking twice between the bounce plate and the sensor (double impact) or by not contacting the sensor at all (no impact). Most of the no impacts were observed upon the enrolments of the long stalk or roots into the conveyor, or upon the insufficient rebound of the bulb with the stalk on the bounce plate, whereas the double impacts occurred due to irregular or oversized shape of the bulbs.
When plural onion bulbs collided at the same time, by installing a parallel sensor, separation of the signal and the estimation of individual weights were possible. With the indirect sensor, using a bounce plate, no impact or double impact were observed and underestimation of the average weights of the bulbs was conspicuous. With the direct sensor, not using the bounce plate, the collisions of soil clod and weed to the sensors were observed and overestimation of the number was not negligible.
Estimated number of bulbs
Fig. 9. Average weights of bulbs using the indirect sensor
REFERENCES
80 70 60 50 40 30 20 10 0
Actual Estimated
0
5
10
15
Experimental number (container #)
Fig. 10. Estimated number of bulbs using the indirect sensor
Average weight of bulbs (g)
The direct sensor showed the deference between the actual and estimated weight distributions for 3out of 26 containers at the significant level of 5 % (Fig. 11). 300 250 200 150 100
Actual Estimated
50 0 0
5
10
15
20
25
30
Experimental number (container #)
Fig. 11. Average weights of bulbs using the direct sensor
Qarallah, B., Shoji, K., Kawamura, T. (2008). Development of a yield sensor for measuring individual weights of onion bulbs. Biosystems Engineering, 100, 511-515. Schröer, G., Trenkler, D. (1995). Exact and randomization distributions of Kolmogorov-Smirnov tests two or three samples. Computational Statistics & Data Analysis, 20, 185-202. Shoji, K., Kawamura, T., Horio H. (2002). Impact-based grain yield sensor with compensation for vibration and drift. Journal of Japanese Society of Agricultural Machinery, 64 (5), 105-115.