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Design and field test of an automatic data acquisition system in a self-propelled forage harvester
c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 1 ( 2 0 0 8 ) 192–200
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journal homepage: www.elsevier.com/locate/compag
Design and field test of an automatic data acquisition system in a self-propelled forage harvester ´ Carlos Amiama ∗ , Javier Bueno, Carlos Jos´e Alvarez, Jos´e Manuel Pereira Department of Agroforestry Engineering, University of Santiago de Compostela, Escuela Polit´ecnica Superior, Campus Universitario, 27002 Lugo, Spain
a r t i c l e
i n f o
a b s t r a c t
Article history:
In this study an online information and documentation system for the performance data
Received 25 June 2007
of a forage harvester was developed and tested. A data acquisition system with positioning
Received in revised form
sensing and a communication module were integrated into the harvester. The data were
14 November 2007
transferred from the mobile equipment to the co-operative’s control centre in two ways:
Accepted 20 November 2007
short message service (SMS) and manually. The following online information was recorded: performance data (operation speed, location, harvested yield, . . .), machine settings (knife
Keywords:
drum speed, . . .) and machine warnings (oil levels, oil pressure, oil temperature, . . .). Har-
Vehicle tracking system (VTS)
vester position on the maps was displayed on a monitor installed in the cab. Harvested area
Telemetric system
was calculated from the field patterns registered by global positioning system (GPS). It was
Global positioning system (GPS)
necessary to adapt the existing cartography to the reality of the co-operative’s land. In the
Geographic information system
first design of the mounted prototype the operator’s ease of use and the reliability of the
(GIS)
system were analyzed. At this stage operation and ergonomic improvements were made.
Fleet management
An evaluation was done by comparing the costs of processing the current information with the costs following the implementation of the new system. In a second investigation a first analysis was done of the recorded time to harvest each field and then regression lines were plotted to compare the field capacity value collected by the system with the field size and the crop yield. Correlations between the field capacity of the forage harvester, the area of the plot and the crop yield were found in these first tests. © 2007 Elsevier B.V. All rights reserved.
1.
Introduction
˜ The agricultural co-operative “Os Irmandinos”, located in Ribadeo (Lugo) in northwest Spain, owns five self-propelled (SP) forage harvesters in its machinery fleet. These machines are the most expensive elements of the fleet and the ones that due to their technical complexity breakdown the most often. Subsequently these pieces of equipment have the greatest turnover rate and at the same time require the greatest planning effort. The SP forage harvesters have a large working area
∗
Corresponding author. Tel.: +34 982 25 22 31; fax: +34 982 28 59 26. E-mail address: [email protected] (C. Amiama). 0168-1699/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.compag.2007.11.006
and can harvest about 5000 ha per year in 3000 fields located at a distance of up to 90 km from the co-operative facilities. At present the only way to know the machine location, or any other incident related to it, is by means of a phone call between the driver and the co-operative. The information dealing with working times, harvested fields, incidents and other data is gathered in worksheets filled in by the driver. Finally in relation to the performance of the different parts of the harvester, the information received by the driver is not registered in any database.
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In agriculture the increasing use of positioning systems that use satellite signals is a reality. However, the use of these systems has mainly focused on precision agriculture techniques (Linseisen, 2001; Renschler et al., 2002; Zhang et al., 2002). This reality along with the appearance of general packet radio service (GPRS) communication systems (with wide coverage) and universal mobile telecommunications system (UMTS) (in expansion), and the affordable cost of computing systems, have lead to a rapid development of telemetry and vehicle location systems in sectors in which until now they had hardly been applied. This is the case of fleet management in agricultural machinery co-operatives. The implementation of these new technologies will allow a large amount of georeferenced information to be obtained and this will help to control the traceability of the process. This will result in a higher quality for the final products (Auernhammer, 2001). Traceability is becoming an important role in the agricultural production processes, not only from an economic point of view but also socially and enviromentally. Consequently there is more and more data flow from different facilities and machinery and at the same time, these data will require differential treatments for their evaluation. On the other hand the amount and potentiality of the sensors and microprocesors installed in the new vehicles is increasing daily (Akinci et al., 2003). This along with a tendency for standardization in the Data Bus used by the different manufacturers facilitates communication among the different equipment. It also allows for the automated data acquisition needed for fleet management (Grenier, 2001).). This will allow for the prevention of breakdowns by monitoring all of the important parameters of the machinery and by detecting the problems before they lead to a breakdown. As well this will
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allow for the maintenance of parts when they are worn out, instead of replacing them after a fixed interval which normally provides conservative factor of safety (Krallmann and Foelster, 2002). In the present study a system was developed, involving the following features: • Real time information of the position and working status of the harvesters; • Registration of the most important machine working parameters; • Registration of the machine activity status; • Automation of the invoicing and fleet control processes; • Warnings of malfunctions of key parts to the driver and the control centre; • Navigation assistance for the driver; and • Possibility of sending messages between driver and control centre. The validation of the cartography was done and the system once implemented was evaluated for its reliability, success for the user, maintenance requirements and cost savings. From the data recorded by the system the existence of a correlation between the field capacity value as compared with the field size and the crop yield was analyzed.
2.
Material and methods
Fig. 1 shows the schematic diagram of the system. To achieve the objectives described above, it was necessary to divide the project into several stages. Though complementary, different disciplines were needed to approach these stages.
Fig. 1 – Schematic diagram of the system.
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2.1.
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Digital map configuration
In reference to the studies carried out by Sikanen et al. (2005) for the location of forest storage piles, it was determined that the use of 1:10,000 scale would be insufficient for the proposed usage. Therefore, it became necessary to use a smaller scale (i.e., a larger map scale). It was also noted that it would be interesting to use interactive maps. Consequently cartography was divided into three different levels which were clearly differentiated. An upper level consisted of 1:250,000 scale cartography from the National Geographic Institute, with TIFF format. The second level used a 1:5000 scale cartography from the Department of Land Policy, Civil Works and Housing from the Xunta de Galicia (Autonomous Government), which had a DGN format. Finally, the base level of cartography, the one that was to be the most extensively used, had a scale of 1:2000 from the National Directorate of Cadastral, with a SHAPE format. Next it was necessary to verify “in situ” the 1:2000 scale cartography to check how this cartography fitted with the existing land divisions. For this purpose a GPS (Trimble pathfinder pocket), similar to the ones installed in the harvesters, was used. It provided a real time precision of around 3–5 m, which became between 1.5 and 3 m after making the differential correction. In order to collect data and configure the equipment this GPS equipment was connected to a personal digital assistant (PDA) computer (HP iPAQ Pocket PC h2210). The harvested fields were located in 21 municipalities, but at the initial stage the cartography was only verified in the municipality of Ribadeo, the municipality with the largest number of plots (614 plots). The verification was performed on a random sampling of 80 plots. This number was considered sufficient because 78% of the plots were on consolidated areas and had a relatively recent and reliable cartography. In addition a visual check was done by overlapping the cadastral vector data maps with the aerial photos provided by the graphical plot information system (SIGPAC), from the Ministry of Agriculture. All of this graphic information was incorporated into a geographic information system (GIS) that allowed different alphanumeric attributes to be associated with the resultant plots. This GIS will allow for future processing and analysis of the obtained information. Previously it was necessary for co-operative members to identify the location of the cropped plots. For this purpose contacts with the co-operative agricultural technicians and members were maintained and they identified the cropped plots on the maps.
2.2.
Mobile unit design
The mobile unit was designed by integrating the communication module, a data acquisition system and an industrial PC with a 1.4 MHz Pentium IV processor in the same compartment. The GPS was located on the outside of the vehicle at the top of the cab and both the receiver and the antenna were integrated in the same unit. The hardware was designed to resist adverse working conditions (high level of vibrations, dusty environments, etc. . . .). All equipment ran off of a 12 Vdc rechargeable battery placed in the harvester. The collected data can be divided into three clearly differentiated subgroups.
2.2.1.
GPS signal
The tracking of the harvester was carried out by a 12-channel C/A code GPS receiver with 1 Hz location frequency (Garmin GPS 17N), which was capable of receiving the European geostationary navigation overlay system (EGNOS) differential correction signal (DGPS). The accuracy of the position obtained varies (according to the manufacturer’s data) from below 15 m (95% 2DRMS) with no differential correction and below 3 m (95% 2DMRS) with correction. This position determination was considered sufficient for the objectives that were targeted. The GSA (GPS dilution of precision and active satellites) and RMC (recommended minimum specific GPS/TRANSIT data) standard NMEA (National Marine Electronics Association) sentences from the GPS receiver were used to provide PDOP (position dilution of precision) and location data.
2.2.2.
Data acquisition system
A multi-function data acquisition module, with the capacity for 20 simple analog channels, 16 digital and four frequency or pulses, was used. Thirteen additional sensors were installed on a New Holland FX58 self-propelled forage harvester and the signals from three already existent ones were used. In total in this first prototype 16 signals were collected; seven analogical, seven digital and two frequency ones. These signals were selected following the suggestions of the drivers, technicians and maintenance staff about the information that was most relevant for them. All these sensors were directly wired to the acquisition unit because it was not possible to use the existing controller area network (CAN) bus in the machine. It was necessary to perform the calibration of each analog and frequency channel in order to coordinate the signal received with the correspondent physical magnitude (pressure, speed, temperature . . .). The characteristics of the installed sensors are presented in Table 1. All sensors scanned at a frequency of 4 Hz to display the information on the screen. Sensor data were recorded at a sampling rate of 1 Hz on the PC hard drive.
2.2.3.
Activity status acquisition system
Sikanen et al. (2005) reported problems when using small sized mobile terminals. Small displays complicated the viewing of large areas on the map, reduced the size of the keys on the keyboard and made the navigation difficult while driving. Taking note of these bad experiences a 15 in. touch-screen monitor with resistive technology, designed to work in industrial environments, was used. The use of graphical user interfaces (GUI) is becoming more frequent in the cabs of agricultural vehicles. They were initially introduced in combine harvesters and their use was gradually extended to large tractors (Cox, 2002). Special attention was paid to the design of data entry and data monitoring interfaces since the user’s acceptance of the system is influenced by the interface ergonomics (Van Der Laan et al., 1997). Aspects such as data presentation, screen contrast, screen luminosity and screen vibration had to be considered. In addition the screen had to have a location as central as possible in the driver’s field of view. However, its position should not interfere with the harvester instrumentation and dials, and should also avoid interfering with the visibility of the working parts of the machine. In this study, a monitor with 1024 × 768 resolution and high brightness was implemented
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Table 1 – Sensors installed in the SP forage harvester Channel 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Type
Sensor
Measured parameter
Analog Analog Analog Analog Analog Analog Analog Digital Digital Digital Digital Digital Digital Digital Frequency Frequency
PT100 probe PT100 probe Pressure transducer Pressure transducer Pressure transducer Pressure transducer Pressure transducer Magnetic proximity switch Power supply—sharpening engine Power supply—magnetic clutch Vertical float switch Vertical float switch Vertical float switch Power supply switch Inductive proximity switch Inductive proximity switch
Oil temperature—hydraulic system Oil temperature—hydrostatic transmission Oil pressure—hydraulic system Oil pressure—hydrostatic transmission (1) Oil pressure—hydrostatic transmission (2) Oil pressure—hydraulic clutch Bunker weight Bunker position Sharpening number Header operation Oil level—hydraulic system tank Oil level—hydrostatic transmission tank Oil level—knife drum clutch tank Driver presence Operation speed Knife drum speed
to be able to be used while working in very luminous conditions. Computer software that allowed for the gathering of information provided by the driver for further evaluation was developed. This software included a graphics based user interface that simplified the operations, in order to encourage a quick reception of the system. Its design sought to minimize the possibility of making mistakes. The final design of this interface is shown in Fig. 2.
2.3.
Communication system
Several possibilities were explored for determining the signal transmission between the control centre and the remote equipment. Radiofrequency systems were discarded because they required an expensive network of repeaters in order to
Range 0–100 ◦ C 0–100 ◦ C 0–250 bar 0–600 bar 0–600 bar 0–40 bar 0–12 t
0–27 km/h 0–1220 rpm
cover a large geographic area. The options left to consider were the use of global system for mobile communications (GSM) or GPRS technology. The first element that would determine the use of one or the other system was the range of coverage and for our interests it was very similar in both systems. The GPRS technology uses the free time gaps of the GSM frequency cells in order to “insert” data packets at four times the speed of the conventional GSM. This technology is mainly designed for sending massive amounts of data. In our case the amount of data required to be sent to the control centre is minimal (position, machine status and GPS signal quality, as well as the warning messages that are required). The rest of the information could be stored in the computer inside of the harvester and later transferred to a removable storage unit (USB memory stick), in order to transfer it to the control centre.
Fig. 2 – Data entry interface.
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The communication costs are the biggest part of the costs in a vehicle location system (Sterzbach and Halang, 1996), so therefore the use of low cost systems should be pursued. The choice of the short message service (SMS) system on demand will significantly reduce the maintenance costs of the equipment (Koukourlis et al., 2002). GPRS costs in Spain were about 1.00D per MB of information sent plus 0.10D per hour of connection and 0.10D per establishment of each connection. Imagining a 10-h day with 1 MB of information sent and ten connections from cut-off calls the GPRS cost would be about 3.00D per day and per machine. With the SMS system up to 20 messages could be sent per day for the same cost of the GPRS, because the cost of SMS messages in Spain was about 0.15D per message. The SMS system on demand was selected for data transmission because the number of expected calls per machine and per day was fewer than 20. There were no fixed time intervals for data transmission.
2.4. Development of software for data collection and management The software developed for the mobile equipment had to be different than the one implemented in the control centre.
2.4.1.
Mobile unit software
While developing this software it was decided that it would be important to select the information given to the driver. An excess amount of information could lead the driver to stop paying attention to the screen. A correct management of the information provided to the driver would result in better equipment operation and therefore a lower cost. In addition if the driver is notified when a threshold value in one of the parameters has been exceeded, he can act immediately and avoid major damages (Cleary, 2000). The software to be installed in the harvester has been divided in five modules: • • • • •
Manual data entry module Sensor data acquisition module Location and navigation module Communication module Management module
2.4.2.
Control centre software
The software to be installed in the control centre was divided into three modules: • Location and status module • Communication module • Management module
2.5.
Field tests
The first prototype of the system was installed in one SP forage harvester in 2005. The SP forage harvester used in the tests was a New Holland FX58 with Racine 2025 bunker. Crop yield was measured by the bunker weight sensor (pressure transducer) installed on the harvester. Discharges were automatically recorded using a position sensor that detected the raising of the harvester bunker. The total value obtained (the
sum of the weights of all the discharges) was divided by the area of the field. Harvested area was calculated from the field patterns registered by GPS. The effective field capacity of the harvester was calculated by dividing the area of the field by the readings of harvesting times collected from the software installed on the harvester. This prototype was field tested between April and June of 2006 during the grassland harvesting season. A total of 425 ha of grass in 220 plots were harvested. The user acceptance, the maintenance, the reliability of the system and the cost were analyzed. The first analysis of the collected time to harvest each field was done with the data obtained during the corn harvest between September and October of 2006. Regression lines were plotted to relate the field capacity value collected by the system with the field size and the crop yield.
3.
Results and discussion
3.1.
Digital map configuration and positioning system
The results were satisfactory as no relevant mistakes were detected in the cartography that was used. In the verification of the plots from consolidated areas no mistakes were found, as was expected. However, in 12% of the plots from the nonconsolidated areas there were relevant differences in the plot areas. A difference was considered “relevant” when the plot areas differed by more than 10% due to the accurateness of the GPS that was used. The cadastral vector data maps fitted well with the aerial photos provided by the SIGPAC and no mistakes were found. To obtain the best possible correspondence, both the GPS signal and the cartography installed in the control centre and the harvester were referenced to the European datum from 1950. The distortions between the visualized positions with respect to the real ones were only due to the limited accuracy of the GPS receiver that was used. However, this accuracy was enough for a straightforward identification of the machine location, which was then only limited by the size of the screen. Although the system provided a correct location of the harvester, there was a marked variance in the land relief (from flat fields to steep ground) where the harvesters did their work. Therefore in order to discriminate incorrect location values it was necessary to use the PDOP values and the number of satellites for both the information viewed in the cab and for the information received in the control centre. One of the objectives of the introduction of this system was to analyze the effect that some factors, such as the plot surface, could have on the harvester’s productivity. For this purpose, it was necessary to group together the cadastral adjoining plots, which from the point of view of mechanization were considered as a single plot (since they belonged to the same owner or were cropped by the same one). It was frequent to find non cropped areas or areas used for other purposes. Considering the whole surface of the plot as harvested created errors in the results of the productivity obtained. Therefore, it was also necessary to define the surfaces that were actually cropped on the original plots. This was done with the aid of the information provided by the harvester trajectories, combined with the information provided by the aerial photos of the SIGPAC.
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Being able to view simultaneously the position of the harvester over the cartography and the location of the new plot to be harvested will allow for route planning. The introduction of a real system for route analysis will indicate the driver the sequence of plots to harvest as well as the best route to follow between them. This is a complex factor to be resolved mathematically. Researcher’s efforts are focused on finding practical and reliable heuristics rules for decision-making with a reasonable cost of programming time (Brotcorne et al., 2003; Ghiani et al., 2003). In addition and in order to reduce costs and enhance productivity, ideally it will be necessary to combine the harvesters and the transport vehicles. This should be done in a way to eliminate the waiting time of the harvesters for lorries or trailers and to minimize the wait time of the transport vehicles (Harrigan, 2003).
3.2.
User acceptance
The acceptance of the system by the users (drivers, technicians and maintenance staff) and their ability to operate it is relevant so special attention was paid to the system ergonomics. It was necessary to modify the placement of several elements in the cab in order to put the touch-screen monitor in a position that made it easier for the user to enter data and to view information. This was done in a way so as not to interfere with the visibility of the relevant mechanical elements of the harvester (see Fig. 3). Drivers were consulted on their preferences in order to determine the best position. It is important to manage the system carefully because entering an incorrect activity status could lead to wrong customer invoicing. For this reason a software that was easy to handle and very intuitive was created. A touch-screen monitor was used and to avoid wrong fingering, the buttons were created
Fig. 3 – Touch-screen monitor in the cab (top), New Holland InfoView monitor (bottom left) and hydrostatic transmission monitor (bottom right).
as large as possible. The distribution of the buttons on the different screens was decided in agreement with the users to create a thematic grouping of the functions. Problems detected with the entry of the activity status are commented in the next section. Drivers found it especially difficult to locate their position on the map. This problem persisted despite the introduction of tools that made placing themselves easier (zoom to field, zoom to GPS, etc. . . .) and adding helpful information to the cartography (names of places, roads, etc. . . .). The reason for this was the lack of experience in handling maps and the lack
Fig. 4 – Location and navigation interface. The plot in which the machine is placed is marked with the red triangle (center of the figure).
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of training time required for the driver to familiarize himself with the maps (see Fig. 4). The use of 15 in. monitors allowed more cartography to be viewed, assisted the positioning of the user and avoid problems cited by Sikanen et al. (2005).
3.3.
Reliability
During this preliminary testing, no hardware failures were detected. All of the elements installed in the harvester worked correctly. The information provided by the sensors at the end of the spring season for grass harvesting was verified. The system provided correct data for the parameters indicated in Table 1. The inclusion of a CAN system (Craessaerts et al., 2005) had allowed for less wiring. The invoice data was collected using the specially designed software, although at the same time worksheets were filled in the traditional way. The reason for doing this was to analyze statistically to see if the number of mistakes made with the new system was significantly different than before. There were two start-stop buttons on the data entry interface (Fig. 2). One button was for starting and ending a field (Field End on Fig. 2), and another button was for starting and ending a customer (Client End on Fig. 2). Buttons were pressed by the driver to start and stop field invoice information. In addition it is planned to study the actions where the greater number of mistakes were made in order to adopt measures to decrease them (larger button size, confirmation messages, etc. . . .). In 88 of the 220 harvested plots the activity status recorded incorrect values. When the discrepancy in time collected between the beginning and the end of the harvesting on the plot was more than 15–40 min ha−1 it was considered incorrect. An analysis of the standardized residuals was made to establish this range. The Cook’s distance was used to identify the influential outliers on the regression fit. Comparing the time collected by the system with the information provided by the GPS (checking the time when the harvester comes into and goes out of the plot), 23 incorrect values were corrected. This high percentage of mistakes (40%) has recommended the introduction of confirmation messages for the orders in the software. It was observed that sometimes the driver did not notice the warning messages (false signals activated in order to study the driver’s reaction) until a few minutes after the activation of the signal. For this reason an acoustic signal, that was activated in an emergency situation or when a message was received, was implemented.
3.4.
Maintenance
The maintenance of the system was quite simple. Since all the systems were powered directly from a rechargeable battery placed inside the harvester it was not necessary to check the battery charge or any other element. It was necessary to clean the touch-screen monitor periodically, since it would get stained with grease after being handled by the driver and the maintenance and repair technicians. Dust also collected on the monitor’s surface making it difficult to view the information. In relation to the transfer of information from the harvester to the control centre, the installed equipment had enough
Table 2 – Summary of unit costs for the processing of service worksheets No. of annual processed worksheets
D /item
Worksheets Invoices Reports
586,00 586,00 586,00
4.57 2.40 3.95
Total
586,00
10.92
Activity
capacity to store all the information registered during the season on its hard drive. As a safety precaution the installation of a second disk drive on which periodic automatic backups could be performed has been considered.
3.5.
Cost analysis
With the data compiled from the co-operative’s technical department activity reports and the annual reports from the machinery service, an analysis was done of the current management costs for service worksheets, sales, work time reports and field capacities. Table 2 presents a summary of the unit costs for the five self-propelled forage harvesters owned by the co-operative, and focuses only on the staff involved with the above-mentioned tasks. To determine the costs that the change to the new system would imply, both the software and the hardware costs were taken into account (see Table 3). The calculation of the annual software costs was based on an estimated 4-year work life, and for the hardware investments it was estimated that they would be paid off in a period of five years. The hardware costs for the control centre were not calculated as their use could be reconciled for other tasks. These calculations then take into account the costs of the implementation of a management system for the five forage harvesters. The study demonstrates that with the introduction of this system, along with other kinds of advantages there would be a cost saving of 1902D /year.
3.6.
Field tests data analysis
As was made clear in Section 3.5, during the grassland harvesting season a large number of harvest time readings were erroneous due essentially to the lack of user skill and experience. The training period and the testing of the applications resulted in an insignificant number of errors, which meant that the data obtained from the silage corn-harvesting season of 2006 had a sufficient degree of reliability to start being utilized. The existence of a correlation between the field capacity values collected by the system with the field size and the crop yield was analyzed. The field capacity value was calculated by
Table 3 – Summary of management system costs Management system costs Mobile unit hardware Mobile unit/control centre software
3.256,25D 1.241,04D
Annual system costs
4.497,29D
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Fig. 5 – Relationship between field size and field capacity.
Fig. 6 – Relationship between crop yield and field capacity.
dividing the area of the plot with the samples of harvest time. It was necessary to verify the values, and in this case correct the atypical output results (those below 0.5 or above 2.5 ha/h) resulting from errors in the operation of the system. To make the corrections the position data from the GPS was meticulously analyzed during the periods of activity and standstill on the plots with anomalous readings. A total of 155 plots were analyzed. Fig. 5 shows the relationship between the field capacity and plot size. These observations show a low correlation between the field size and time invested in harvesting the plot. Taylor et al. (2001) reported similar findings in their trials. In spite of these results in all the lines, the slope showed that field capacity tends to increase with plot size. Fig. 6 shows the relationship between field capacity and crop yield. There is an evident correlation between these two variables and observations note a proportional decrease in the work capacity of the forage harvesters as the crop production increases. However, it is necessary to include more variables in the model to explain this high variability in the results.
4.
Conclusions
The system allowed the co-operative to record and collect for later operations data, which was up until now neither recorded nor well-gathered in worksheets filled in by the driver. This information has been referred to in the forage harvester activity reports, harvester trajectories and in the values recorded
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by the installed sensors. In addition a communication system was set up between the SP forage harvester and the control centre, which through the use of SMS messages allowed the control centre to track and alert the forage harvesters in real time. The preliminary trials that were carried out during the grassland harvesting season of 2006 indicated the need to update the current cartography, adapting it to the reality of the co-operative. Whereas no relevant mistakes were detected in the cartography used, it was necessary to group together the cadastral adjoining plots, which from the point of view of mechanization had been considered as a unique plot. It was also necessary to define the surfaces that were truly cropped in the original plots. The use of a 15 in. touch-screen monitor along with simple operational and intuitive software, were key aspects of the success of the system for the user. Special attention was paid to the system ergonomics. No errors were detected in the hardware during the testing. The errors in the obtained data were due to the poor usage by the driver. Prior to use as a management tool a period of time for training and adjustment is required. However, the maintenance requirements are minimal. The implementation of the system would imply savings of 1902D /year for the co-operative adapting it to the procedures currently used. The production data taken during the corn silageharvesting season shows the existence of correlations between the field capacity of the forage harvester, the area of the plot and to a greater degree with the crop yield. The inclusion of more variables would likely improve the results obtained.
Acknowledgements Financial support for the research was provided by the co˜ ´ Xeral de operative “Os Irmandinos S.C.G.” and the “Direccion ´ e Desenvolvemento da Xunta de Galicia” under Investigacion project no. PGIDIT03RAG14E.
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survey and six alternative elevation data sources: watershed topography and delineation. Trans. ASAE 45 (6), 1883–1895. Sikanen, L., Asikainen, A., Lehikoinen, M., 2005. Transport control of forest fuels by fleet manager, mobile terminals and GPS. Biomass Bioenergy 28, 183–191. Sterzbach, B., Halang, W.A., 1996. A mobile vehicle on-board computing and communication system. Comput. Graph. 20 (4), 659–667. Taylor, R.K., Schrock, M.D., Staggenborg, S.A., 2001. Using GPS Technology to assist machinery management decisions. Presented at the 2001 Mid-Central Conference of the ASAE, St. Joseph, Missouri, March 30–31, 2001, Paper No. MC01-204, ASAE, 2950 Niles Rd., St. Joseph, MI 49085–9659 USA. Van Der Laan, J.D., Heino, A., De Waard, 1997. A simple procedure for the assessment of acceptance of advanced transport telematics. Transp. Res., Part C Emerg. Technol. 5 (1), 1–10. Zhang, N., Wang, M., Wang, N., 2002. Precision agriculture—a worldwide overview. Comput. Electron. Agric. 36, 113–132.
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/compag
Design and field test of an automatic data acquisition system in a self-propelled forage harvester ´ Carlos Amiama ∗ , Javier Bueno, Carlos Jos´e Alvarez, Jos´e Manuel Pereira Department of Agroforestry Engineering, University of Santiago de Compostela, Escuela Polit´ecnica Superior, Campus Universitario, 27002 Lugo, Spain
a r t i c l e
i n f o
a b s t r a c t
Article history:
In this study an online information and documentation system for the performance data
Received 25 June 2007
of a forage harvester was developed and tested. A data acquisition system with positioning
Received in revised form
sensing and a communication module were integrated into the harvester. The data were
14 November 2007
transferred from the mobile equipment to the co-operative’s control centre in two ways:
Accepted 20 November 2007
short message service (SMS) and manually. The following online information was recorded: performance data (operation speed, location, harvested yield, . . .), machine settings (knife
Keywords:
drum speed, . . .) and machine warnings (oil levels, oil pressure, oil temperature, . . .). Har-
Vehicle tracking system (VTS)
vester position on the maps was displayed on a monitor installed in the cab. Harvested area
Telemetric system
was calculated from the field patterns registered by global positioning system (GPS). It was
Global positioning system (GPS)
necessary to adapt the existing cartography to the reality of the co-operative’s land. In the
Geographic information system
first design of the mounted prototype the operator’s ease of use and the reliability of the
(GIS)
system were analyzed. At this stage operation and ergonomic improvements were made.
Fleet management
An evaluation was done by comparing the costs of processing the current information with the costs following the implementation of the new system. In a second investigation a first analysis was done of the recorded time to harvest each field and then regression lines were plotted to compare the field capacity value collected by the system with the field size and the crop yield. Correlations between the field capacity of the forage harvester, the area of the plot and the crop yield were found in these first tests. © 2007 Elsevier B.V. All rights reserved.
1.
Introduction
˜ The agricultural co-operative “Os Irmandinos”, located in Ribadeo (Lugo) in northwest Spain, owns five self-propelled (SP) forage harvesters in its machinery fleet. These machines are the most expensive elements of the fleet and the ones that due to their technical complexity breakdown the most often. Subsequently these pieces of equipment have the greatest turnover rate and at the same time require the greatest planning effort. The SP forage harvesters have a large working area
∗
Corresponding author. Tel.: +34 982 25 22 31; fax: +34 982 28 59 26. E-mail address: [email protected] (C. Amiama). 0168-1699/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.compag.2007.11.006
and can harvest about 5000 ha per year in 3000 fields located at a distance of up to 90 km from the co-operative facilities. At present the only way to know the machine location, or any other incident related to it, is by means of a phone call between the driver and the co-operative. The information dealing with working times, harvested fields, incidents and other data is gathered in worksheets filled in by the driver. Finally in relation to the performance of the different parts of the harvester, the information received by the driver is not registered in any database.
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In agriculture the increasing use of positioning systems that use satellite signals is a reality. However, the use of these systems has mainly focused on precision agriculture techniques (Linseisen, 2001; Renschler et al., 2002; Zhang et al., 2002). This reality along with the appearance of general packet radio service (GPRS) communication systems (with wide coverage) and universal mobile telecommunications system (UMTS) (in expansion), and the affordable cost of computing systems, have lead to a rapid development of telemetry and vehicle location systems in sectors in which until now they had hardly been applied. This is the case of fleet management in agricultural machinery co-operatives. The implementation of these new technologies will allow a large amount of georeferenced information to be obtained and this will help to control the traceability of the process. This will result in a higher quality for the final products (Auernhammer, 2001). Traceability is becoming an important role in the agricultural production processes, not only from an economic point of view but also socially and enviromentally. Consequently there is more and more data flow from different facilities and machinery and at the same time, these data will require differential treatments for their evaluation. On the other hand the amount and potentiality of the sensors and microprocesors installed in the new vehicles is increasing daily (Akinci et al., 2003). This along with a tendency for standardization in the Data Bus used by the different manufacturers facilitates communication among the different equipment. It also allows for the automated data acquisition needed for fleet management (Grenier, 2001).). This will allow for the prevention of breakdowns by monitoring all of the important parameters of the machinery and by detecting the problems before they lead to a breakdown. As well this will
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allow for the maintenance of parts when they are worn out, instead of replacing them after a fixed interval which normally provides conservative factor of safety (Krallmann and Foelster, 2002). In the present study a system was developed, involving the following features: • Real time information of the position and working status of the harvesters; • Registration of the most important machine working parameters; • Registration of the machine activity status; • Automation of the invoicing and fleet control processes; • Warnings of malfunctions of key parts to the driver and the control centre; • Navigation assistance for the driver; and • Possibility of sending messages between driver and control centre. The validation of the cartography was done and the system once implemented was evaluated for its reliability, success for the user, maintenance requirements and cost savings. From the data recorded by the system the existence of a correlation between the field capacity value as compared with the field size and the crop yield was analyzed.
2.
Material and methods
Fig. 1 shows the schematic diagram of the system. To achieve the objectives described above, it was necessary to divide the project into several stages. Though complementary, different disciplines were needed to approach these stages.
Fig. 1 – Schematic diagram of the system.
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2.1.
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Digital map configuration
In reference to the studies carried out by Sikanen et al. (2005) for the location of forest storage piles, it was determined that the use of 1:10,000 scale would be insufficient for the proposed usage. Therefore, it became necessary to use a smaller scale (i.e., a larger map scale). It was also noted that it would be interesting to use interactive maps. Consequently cartography was divided into three different levels which were clearly differentiated. An upper level consisted of 1:250,000 scale cartography from the National Geographic Institute, with TIFF format. The second level used a 1:5000 scale cartography from the Department of Land Policy, Civil Works and Housing from the Xunta de Galicia (Autonomous Government), which had a DGN format. Finally, the base level of cartography, the one that was to be the most extensively used, had a scale of 1:2000 from the National Directorate of Cadastral, with a SHAPE format. Next it was necessary to verify “in situ” the 1:2000 scale cartography to check how this cartography fitted with the existing land divisions. For this purpose a GPS (Trimble pathfinder pocket), similar to the ones installed in the harvesters, was used. It provided a real time precision of around 3–5 m, which became between 1.5 and 3 m after making the differential correction. In order to collect data and configure the equipment this GPS equipment was connected to a personal digital assistant (PDA) computer (HP iPAQ Pocket PC h2210). The harvested fields were located in 21 municipalities, but at the initial stage the cartography was only verified in the municipality of Ribadeo, the municipality with the largest number of plots (614 plots). The verification was performed on a random sampling of 80 plots. This number was considered sufficient because 78% of the plots were on consolidated areas and had a relatively recent and reliable cartography. In addition a visual check was done by overlapping the cadastral vector data maps with the aerial photos provided by the graphical plot information system (SIGPAC), from the Ministry of Agriculture. All of this graphic information was incorporated into a geographic information system (GIS) that allowed different alphanumeric attributes to be associated with the resultant plots. This GIS will allow for future processing and analysis of the obtained information. Previously it was necessary for co-operative members to identify the location of the cropped plots. For this purpose contacts with the co-operative agricultural technicians and members were maintained and they identified the cropped plots on the maps.
2.2.
Mobile unit design
The mobile unit was designed by integrating the communication module, a data acquisition system and an industrial PC with a 1.4 MHz Pentium IV processor in the same compartment. The GPS was located on the outside of the vehicle at the top of the cab and both the receiver and the antenna were integrated in the same unit. The hardware was designed to resist adverse working conditions (high level of vibrations, dusty environments, etc. . . .). All equipment ran off of a 12 Vdc rechargeable battery placed in the harvester. The collected data can be divided into three clearly differentiated subgroups.
2.2.1.
GPS signal
The tracking of the harvester was carried out by a 12-channel C/A code GPS receiver with 1 Hz location frequency (Garmin GPS 17N), which was capable of receiving the European geostationary navigation overlay system (EGNOS) differential correction signal (DGPS). The accuracy of the position obtained varies (according to the manufacturer’s data) from below 15 m (95% 2DRMS) with no differential correction and below 3 m (95% 2DMRS) with correction. This position determination was considered sufficient for the objectives that were targeted. The GSA (GPS dilution of precision and active satellites) and RMC (recommended minimum specific GPS/TRANSIT data) standard NMEA (National Marine Electronics Association) sentences from the GPS receiver were used to provide PDOP (position dilution of precision) and location data.
2.2.2.
Data acquisition system
A multi-function data acquisition module, with the capacity for 20 simple analog channels, 16 digital and four frequency or pulses, was used. Thirteen additional sensors were installed on a New Holland FX58 self-propelled forage harvester and the signals from three already existent ones were used. In total in this first prototype 16 signals were collected; seven analogical, seven digital and two frequency ones. These signals were selected following the suggestions of the drivers, technicians and maintenance staff about the information that was most relevant for them. All these sensors were directly wired to the acquisition unit because it was not possible to use the existing controller area network (CAN) bus in the machine. It was necessary to perform the calibration of each analog and frequency channel in order to coordinate the signal received with the correspondent physical magnitude (pressure, speed, temperature . . .). The characteristics of the installed sensors are presented in Table 1. All sensors scanned at a frequency of 4 Hz to display the information on the screen. Sensor data were recorded at a sampling rate of 1 Hz on the PC hard drive.
2.2.3.
Activity status acquisition system
Sikanen et al. (2005) reported problems when using small sized mobile terminals. Small displays complicated the viewing of large areas on the map, reduced the size of the keys on the keyboard and made the navigation difficult while driving. Taking note of these bad experiences a 15 in. touch-screen monitor with resistive technology, designed to work in industrial environments, was used. The use of graphical user interfaces (GUI) is becoming more frequent in the cabs of agricultural vehicles. They were initially introduced in combine harvesters and their use was gradually extended to large tractors (Cox, 2002). Special attention was paid to the design of data entry and data monitoring interfaces since the user’s acceptance of the system is influenced by the interface ergonomics (Van Der Laan et al., 1997). Aspects such as data presentation, screen contrast, screen luminosity and screen vibration had to be considered. In addition the screen had to have a location as central as possible in the driver’s field of view. However, its position should not interfere with the harvester instrumentation and dials, and should also avoid interfering with the visibility of the working parts of the machine. In this study, a monitor with 1024 × 768 resolution and high brightness was implemented
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Table 1 – Sensors installed in the SP forage harvester Channel 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Type
Sensor
Measured parameter
Analog Analog Analog Analog Analog Analog Analog Digital Digital Digital Digital Digital Digital Digital Frequency Frequency
PT100 probe PT100 probe Pressure transducer Pressure transducer Pressure transducer Pressure transducer Pressure transducer Magnetic proximity switch Power supply—sharpening engine Power supply—magnetic clutch Vertical float switch Vertical float switch Vertical float switch Power supply switch Inductive proximity switch Inductive proximity switch
Oil temperature—hydraulic system Oil temperature—hydrostatic transmission Oil pressure—hydraulic system Oil pressure—hydrostatic transmission (1) Oil pressure—hydrostatic transmission (2) Oil pressure—hydraulic clutch Bunker weight Bunker position Sharpening number Header operation Oil level—hydraulic system tank Oil level—hydrostatic transmission tank Oil level—knife drum clutch tank Driver presence Operation speed Knife drum speed
to be able to be used while working in very luminous conditions. Computer software that allowed for the gathering of information provided by the driver for further evaluation was developed. This software included a graphics based user interface that simplified the operations, in order to encourage a quick reception of the system. Its design sought to minimize the possibility of making mistakes. The final design of this interface is shown in Fig. 2.
2.3.
Communication system
Several possibilities were explored for determining the signal transmission between the control centre and the remote equipment. Radiofrequency systems were discarded because they required an expensive network of repeaters in order to
Range 0–100 ◦ C 0–100 ◦ C 0–250 bar 0–600 bar 0–600 bar 0–40 bar 0–12 t
0–27 km/h 0–1220 rpm
cover a large geographic area. The options left to consider were the use of global system for mobile communications (GSM) or GPRS technology. The first element that would determine the use of one or the other system was the range of coverage and for our interests it was very similar in both systems. The GPRS technology uses the free time gaps of the GSM frequency cells in order to “insert” data packets at four times the speed of the conventional GSM. This technology is mainly designed for sending massive amounts of data. In our case the amount of data required to be sent to the control centre is minimal (position, machine status and GPS signal quality, as well as the warning messages that are required). The rest of the information could be stored in the computer inside of the harvester and later transferred to a removable storage unit (USB memory stick), in order to transfer it to the control centre.
Fig. 2 – Data entry interface.
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The communication costs are the biggest part of the costs in a vehicle location system (Sterzbach and Halang, 1996), so therefore the use of low cost systems should be pursued. The choice of the short message service (SMS) system on demand will significantly reduce the maintenance costs of the equipment (Koukourlis et al., 2002). GPRS costs in Spain were about 1.00D per MB of information sent plus 0.10D per hour of connection and 0.10D per establishment of each connection. Imagining a 10-h day with 1 MB of information sent and ten connections from cut-off calls the GPRS cost would be about 3.00D per day and per machine. With the SMS system up to 20 messages could be sent per day for the same cost of the GPRS, because the cost of SMS messages in Spain was about 0.15D per message. The SMS system on demand was selected for data transmission because the number of expected calls per machine and per day was fewer than 20. There were no fixed time intervals for data transmission.
2.4. Development of software for data collection and management The software developed for the mobile equipment had to be different than the one implemented in the control centre.
2.4.1.
Mobile unit software
While developing this software it was decided that it would be important to select the information given to the driver. An excess amount of information could lead the driver to stop paying attention to the screen. A correct management of the information provided to the driver would result in better equipment operation and therefore a lower cost. In addition if the driver is notified when a threshold value in one of the parameters has been exceeded, he can act immediately and avoid major damages (Cleary, 2000). The software to be installed in the harvester has been divided in five modules: • • • • •
Manual data entry module Sensor data acquisition module Location and navigation module Communication module Management module
2.4.2.
Control centre software
The software to be installed in the control centre was divided into three modules: • Location and status module • Communication module • Management module
2.5.
Field tests
The first prototype of the system was installed in one SP forage harvester in 2005. The SP forage harvester used in the tests was a New Holland FX58 with Racine 2025 bunker. Crop yield was measured by the bunker weight sensor (pressure transducer) installed on the harvester. Discharges were automatically recorded using a position sensor that detected the raising of the harvester bunker. The total value obtained (the
sum of the weights of all the discharges) was divided by the area of the field. Harvested area was calculated from the field patterns registered by GPS. The effective field capacity of the harvester was calculated by dividing the area of the field by the readings of harvesting times collected from the software installed on the harvester. This prototype was field tested between April and June of 2006 during the grassland harvesting season. A total of 425 ha of grass in 220 plots were harvested. The user acceptance, the maintenance, the reliability of the system and the cost were analyzed. The first analysis of the collected time to harvest each field was done with the data obtained during the corn harvest between September and October of 2006. Regression lines were plotted to relate the field capacity value collected by the system with the field size and the crop yield.
3.
Results and discussion
3.1.
Digital map configuration and positioning system
The results were satisfactory as no relevant mistakes were detected in the cartography that was used. In the verification of the plots from consolidated areas no mistakes were found, as was expected. However, in 12% of the plots from the nonconsolidated areas there were relevant differences in the plot areas. A difference was considered “relevant” when the plot areas differed by more than 10% due to the accurateness of the GPS that was used. The cadastral vector data maps fitted well with the aerial photos provided by the SIGPAC and no mistakes were found. To obtain the best possible correspondence, both the GPS signal and the cartography installed in the control centre and the harvester were referenced to the European datum from 1950. The distortions between the visualized positions with respect to the real ones were only due to the limited accuracy of the GPS receiver that was used. However, this accuracy was enough for a straightforward identification of the machine location, which was then only limited by the size of the screen. Although the system provided a correct location of the harvester, there was a marked variance in the land relief (from flat fields to steep ground) where the harvesters did their work. Therefore in order to discriminate incorrect location values it was necessary to use the PDOP values and the number of satellites for both the information viewed in the cab and for the information received in the control centre. One of the objectives of the introduction of this system was to analyze the effect that some factors, such as the plot surface, could have on the harvester’s productivity. For this purpose, it was necessary to group together the cadastral adjoining plots, which from the point of view of mechanization were considered as a single plot (since they belonged to the same owner or were cropped by the same one). It was frequent to find non cropped areas or areas used for other purposes. Considering the whole surface of the plot as harvested created errors in the results of the productivity obtained. Therefore, it was also necessary to define the surfaces that were actually cropped on the original plots. This was done with the aid of the information provided by the harvester trajectories, combined with the information provided by the aerial photos of the SIGPAC.
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Being able to view simultaneously the position of the harvester over the cartography and the location of the new plot to be harvested will allow for route planning. The introduction of a real system for route analysis will indicate the driver the sequence of plots to harvest as well as the best route to follow between them. This is a complex factor to be resolved mathematically. Researcher’s efforts are focused on finding practical and reliable heuristics rules for decision-making with a reasonable cost of programming time (Brotcorne et al., 2003; Ghiani et al., 2003). In addition and in order to reduce costs and enhance productivity, ideally it will be necessary to combine the harvesters and the transport vehicles. This should be done in a way to eliminate the waiting time of the harvesters for lorries or trailers and to minimize the wait time of the transport vehicles (Harrigan, 2003).
3.2.
User acceptance
The acceptance of the system by the users (drivers, technicians and maintenance staff) and their ability to operate it is relevant so special attention was paid to the system ergonomics. It was necessary to modify the placement of several elements in the cab in order to put the touch-screen monitor in a position that made it easier for the user to enter data and to view information. This was done in a way so as not to interfere with the visibility of the relevant mechanical elements of the harvester (see Fig. 3). Drivers were consulted on their preferences in order to determine the best position. It is important to manage the system carefully because entering an incorrect activity status could lead to wrong customer invoicing. For this reason a software that was easy to handle and very intuitive was created. A touch-screen monitor was used and to avoid wrong fingering, the buttons were created
Fig. 3 – Touch-screen monitor in the cab (top), New Holland InfoView monitor (bottom left) and hydrostatic transmission monitor (bottom right).
as large as possible. The distribution of the buttons on the different screens was decided in agreement with the users to create a thematic grouping of the functions. Problems detected with the entry of the activity status are commented in the next section. Drivers found it especially difficult to locate their position on the map. This problem persisted despite the introduction of tools that made placing themselves easier (zoom to field, zoom to GPS, etc. . . .) and adding helpful information to the cartography (names of places, roads, etc. . . .). The reason for this was the lack of experience in handling maps and the lack
Fig. 4 – Location and navigation interface. The plot in which the machine is placed is marked with the red triangle (center of the figure).
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of training time required for the driver to familiarize himself with the maps (see Fig. 4). The use of 15 in. monitors allowed more cartography to be viewed, assisted the positioning of the user and avoid problems cited by Sikanen et al. (2005).
3.3.
Reliability
During this preliminary testing, no hardware failures were detected. All of the elements installed in the harvester worked correctly. The information provided by the sensors at the end of the spring season for grass harvesting was verified. The system provided correct data for the parameters indicated in Table 1. The inclusion of a CAN system (Craessaerts et al., 2005) had allowed for less wiring. The invoice data was collected using the specially designed software, although at the same time worksheets were filled in the traditional way. The reason for doing this was to analyze statistically to see if the number of mistakes made with the new system was significantly different than before. There were two start-stop buttons on the data entry interface (Fig. 2). One button was for starting and ending a field (Field End on Fig. 2), and another button was for starting and ending a customer (Client End on Fig. 2). Buttons were pressed by the driver to start and stop field invoice information. In addition it is planned to study the actions where the greater number of mistakes were made in order to adopt measures to decrease them (larger button size, confirmation messages, etc. . . .). In 88 of the 220 harvested plots the activity status recorded incorrect values. When the discrepancy in time collected between the beginning and the end of the harvesting on the plot was more than 15–40 min ha−1 it was considered incorrect. An analysis of the standardized residuals was made to establish this range. The Cook’s distance was used to identify the influential outliers on the regression fit. Comparing the time collected by the system with the information provided by the GPS (checking the time when the harvester comes into and goes out of the plot), 23 incorrect values were corrected. This high percentage of mistakes (40%) has recommended the introduction of confirmation messages for the orders in the software. It was observed that sometimes the driver did not notice the warning messages (false signals activated in order to study the driver’s reaction) until a few minutes after the activation of the signal. For this reason an acoustic signal, that was activated in an emergency situation or when a message was received, was implemented.
3.4.
Maintenance
The maintenance of the system was quite simple. Since all the systems were powered directly from a rechargeable battery placed inside the harvester it was not necessary to check the battery charge or any other element. It was necessary to clean the touch-screen monitor periodically, since it would get stained with grease after being handled by the driver and the maintenance and repair technicians. Dust also collected on the monitor’s surface making it difficult to view the information. In relation to the transfer of information from the harvester to the control centre, the installed equipment had enough
Table 2 – Summary of unit costs for the processing of service worksheets No. of annual processed worksheets
D /item
Worksheets Invoices Reports
586,00 586,00 586,00
4.57 2.40 3.95
Total
586,00
10.92
Activity
capacity to store all the information registered during the season on its hard drive. As a safety precaution the installation of a second disk drive on which periodic automatic backups could be performed has been considered.
3.5.
Cost analysis
With the data compiled from the co-operative’s technical department activity reports and the annual reports from the machinery service, an analysis was done of the current management costs for service worksheets, sales, work time reports and field capacities. Table 2 presents a summary of the unit costs for the five self-propelled forage harvesters owned by the co-operative, and focuses only on the staff involved with the above-mentioned tasks. To determine the costs that the change to the new system would imply, both the software and the hardware costs were taken into account (see Table 3). The calculation of the annual software costs was based on an estimated 4-year work life, and for the hardware investments it was estimated that they would be paid off in a period of five years. The hardware costs for the control centre were not calculated as their use could be reconciled for other tasks. These calculations then take into account the costs of the implementation of a management system for the five forage harvesters. The study demonstrates that with the introduction of this system, along with other kinds of advantages there would be a cost saving of 1902D /year.
3.6.
Field tests data analysis
As was made clear in Section 3.5, during the grassland harvesting season a large number of harvest time readings were erroneous due essentially to the lack of user skill and experience. The training period and the testing of the applications resulted in an insignificant number of errors, which meant that the data obtained from the silage corn-harvesting season of 2006 had a sufficient degree of reliability to start being utilized. The existence of a correlation between the field capacity values collected by the system with the field size and the crop yield was analyzed. The field capacity value was calculated by
Table 3 – Summary of management system costs Management system costs Mobile unit hardware Mobile unit/control centre software
3.256,25D 1.241,04D
Annual system costs
4.497,29D
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Fig. 5 – Relationship between field size and field capacity.
Fig. 6 – Relationship between crop yield and field capacity.
dividing the area of the plot with the samples of harvest time. It was necessary to verify the values, and in this case correct the atypical output results (those below 0.5 or above 2.5 ha/h) resulting from errors in the operation of the system. To make the corrections the position data from the GPS was meticulously analyzed during the periods of activity and standstill on the plots with anomalous readings. A total of 155 plots were analyzed. Fig. 5 shows the relationship between the field capacity and plot size. These observations show a low correlation between the field size and time invested in harvesting the plot. Taylor et al. (2001) reported similar findings in their trials. In spite of these results in all the lines, the slope showed that field capacity tends to increase with plot size. Fig. 6 shows the relationship between field capacity and crop yield. There is an evident correlation between these two variables and observations note a proportional decrease in the work capacity of the forage harvesters as the crop production increases. However, it is necessary to include more variables in the model to explain this high variability in the results.
4.
Conclusions
The system allowed the co-operative to record and collect for later operations data, which was up until now neither recorded nor well-gathered in worksheets filled in by the driver. This information has been referred to in the forage harvester activity reports, harvester trajectories and in the values recorded
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by the installed sensors. In addition a communication system was set up between the SP forage harvester and the control centre, which through the use of SMS messages allowed the control centre to track and alert the forage harvesters in real time. The preliminary trials that were carried out during the grassland harvesting season of 2006 indicated the need to update the current cartography, adapting it to the reality of the co-operative. Whereas no relevant mistakes were detected in the cartography used, it was necessary to group together the cadastral adjoining plots, which from the point of view of mechanization had been considered as a unique plot. It was also necessary to define the surfaces that were truly cropped in the original plots. The use of a 15 in. touch-screen monitor along with simple operational and intuitive software, were key aspects of the success of the system for the user. Special attention was paid to the system ergonomics. No errors were detected in the hardware during the testing. The errors in the obtained data were due to the poor usage by the driver. Prior to use as a management tool a period of time for training and adjustment is required. However, the maintenance requirements are minimal. The implementation of the system would imply savings of 1902D /year for the co-operative adapting it to the procedures currently used. The production data taken during the corn silageharvesting season shows the existence of correlations between the field capacity of the forage harvester, the area of the plot and to a greater degree with the crop yield. The inclusion of more variables would likely improve the results obtained.
Acknowledgements Financial support for the research was provided by the co˜ ´ Xeral de operative “Os Irmandinos S.C.G.” and the “Direccion ´ e Desenvolvemento da Xunta de Galicia” under Investigacion project no. PGIDIT03RAG14E.
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