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Development and evaluation of forage yield measure sensors in a mowing-conditioning machine
Computers and Electronics in Agriculture 58 (2007) 154–163
Development and evaluation of forage yield measure sensors in a mowing-conditioning machine Frantiˇsek Kumh´ala ∗ , Milan Kroul´ık, Vaclav Proˇsek Department of Agricultural Machines, Technical Faculty, Czech University of Agriculture in Prague, Kamycka 129, 165 21 Prague, 6 Suchdol, Czech Republic Received 13 January 2006; received in revised form 8 March 2007; accepted 28 March 2007
Abstract The main aim of this research was to develop and evaluate sensor system to create forage yield maps on a three-point linkage type of rotary mower-conditioner. The method was based on the mowing machine conditioner’s power requirement measured by a torque sensor or on material change in momentum measured by a curved impact plate. Laboratory measurements were taken to determine the dependence of conditioner power input and signals from the impact plate on material mass flow. A mixture of grass and alfalfa was used. There was a very good linear relationship between the conditioner’s power, impact force from an impact plate, and material feed rate through the mower. The calculated coefficients of determination (R2 ) were about 0.95. It was possible to differentiate a material feed rate difference 0.5 kg s−1 using either method. The effects of material changes and mower parameters on the accuracy of feed rate measurement were then measured. It was observed that the results form torque sensor was influenced by crop variety, maturity and intensity of conditioning. The same influence was not observed for impact plate. A field of 0.54 ha was then harvested. A comparison of data from the torque sensor and impact plate with data from hand measurement were made by means of statistical and geostatistical analysis. Conditioner power input measurement and crop impact force were used to create grass yield maps. © 2007 Elsevier B.V. All rights reserved. Keywords: Yield maps; Grass; Alfalfa; Mowing machine; Conditioner; Impact plate; Torque measurement
1. Introduction 1.1. Forage harvesters Vansichen and De Baerdemaeker (1993) calculated yield from the torque of a forage harvester’s blower. Another possibility is to measure the distance between feeder rolls of the harvester (Ehlert and Schmidt, 1995; Auernhammer et al., 1996; Barnett and Shinners, 1998; Martel and Savoie, 1999; Schmittmann et al., 2001; Diekhans, 2002). A mass flow sensor for a pull type (trailed) forage harvester based on a reaction plate in the spout was constructed and tested by Missotten et al. (1997). Similar sensors were tested by other authors (Barnett and Shinners, 1998; Martel and Savoie, 1999; Schmittmann et al., 2001) for self-propelled forage harvesters. Martel and Savoie (1999) measured
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Corresponding author. Tel.: +420 224 383 135; fax: +420 22 383 122. E-mail address: [email protected] (F. Kumh´ala).
0168-1699/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.compag.2007.03.013
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electrical capacitance in the spout of a forage harvester and Schmittmann et al. (2001) measured crop layer thickness. Some of these methods (e.g. distance between feeder rolls, reaction plate, and crop layer thickness) are very interesting and showed a good coefficient of determination (R2 = 0.94–0.98). Some methods (e.g. distance between feeder rolls, electrical capacitance in the spout of a forage harvester) have to be supplied with several calibration parameters. Site-specific measurement of biomass in growing cereal crops has been proposed by Ehlert et al. (2002) using a pivoted cylindrical body moving horizontally through a plant population (moving pendulum). The angle of deviation of this pendulum varies with the plant properties. 1.2. Mowing machines The feed rate measurement technique for mowing machines is not so well developed. Demmel et al. (2002) used a principle based on belt weighing technology in the windrowing device of a mower. Recently, Ruhland et al. (2004) determined yield by means of determining the torque requirements in the windrowing device of a mower. Both methods are suitable only for mowing machines equipped with a windrowing device. A pulse radar system for grass yield measurements is still under development (Wild et al., 2003). The results obtained from the measurement showed that the sensors need further improvement. Shinners et al. (2000) developed and evaluated systems to measure material feed rate on a self-propelled forage windrower. They tested conditioning roll force, conditioning roll rise and swath shield impact force as the predictors of material feed rate. The only system to show promise of adequately predicting material feed rate through the machine was impact force on the swath shield. Recently, Shinners et al. (2003) equipped a windrower to measure material feed rate by the following sensors: pressure sensor to measure the load at the platform drive motor; speed pick-up to measure conditioning roll speed, load cell to measure crop impact on the swath forming shield and rotary potentiometers to measure crop volumetric flow past swath forming shield. The crop volumetric flow was well correlated with material feed rate when the sensor output was combined with platform inclination and roll speed. The results from these measurements were meaningful (R2 = 0.94). It can be seen from the presented literature review that we have not found material feed rate sensors suitable for three-point linkage or pulled rotary mowing machines equipped with a conditioner but without a windrowing device. The research and development of yield mapping capabilities for above-mentioned machines was the main objective of the work presented. Another reason was that on the basis of current techniques farmers can obtain yield maps from grain fields only. For responsible application of precision farming system on the field it is sometimes necessary to know the yield of forage crops as well (for example when the perennial forage is one of harvested plants in plant rotation system). 2. Materials and experimental methods A three-point linkage mowing machine ZTR 216 H (Agrostroj Pelhrimov Company, Czech Republic) was used for the measurements. This machine consisted of two working drums and a finger conditioner. Working width of the machine was 2.15 m. The mowing machine was equipped with an electronic measuring unit developed in our laboratory. The mowing machine’s conditioner shaft was supplied with a VUZT model torque sensor based on resistant strain gauges and with an optical sensor measuring conditioner shaft speed. Besides the torque sensor, the mowing machine was equipped with a curved impact plate mounted at the exit of the machine. The material ejected from the mowing machine conditioner struck the impact plate. The force created from the change in direction was measured by four elastic members with strain gauge bridges for force measurement (Fig. 1). Voltage signals from the strain gauges on the torque sensor and the impact-plate were amplified and converted into frequency by an electronic measuring apparatus developed in our laboratory. The output frequency was proportional to the measured force. For infield measurements the mowing machine was also equipped with DGPS signal antenna placed in the centre of the mowing machine and with a DGPS signal receiver placed in the cab of the tractor. A front wheel rotary speed counter was used for measuring the working speed of the tractor. The block diagram of this electronic apparatus arrangement is shown in Fig. 2. The torque sensor was calibrated in the range from 0 to 800 N m. The output frequency was correlated with measured torque at an accuracy better than 1%.
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Fig. 1. Equipment for material feed rate measurement.
The calibration of the impact plate was divided into two steps. In the first step, the strain gauge bridges were adjusted so that the relation of the output voltage with measured loading force was the same for all bridges. The aim of the second step of the calibration was to avoid the dependence of the impact plate arrangement output signal on the position where the force acts on the impact plate. In order to achieve this goal, the level of voltages from each strain gauge bridge was properly adjusted by means of resistor dividers. After this second step of calibration procedure, the final output signal does not depend on the position where the force acts on the impact plate. Laboratory measurements were performed to obtain information about the relationship of conditioner power input and signals from the impact plate to the material feed rate. It was necessary to determine the amount of conditioner’s material feed rate. The laboratory set-up was composed of a conveyer belt carrying a measured quantity of material, a tractor and a rotary drum-mowing machine equipped with the electronic measurement apparatus (Fig. 3). The same arrangement of measurement device was used to determine the effects of material changes and mowing machine parameters on the feed rate measurement accuracy. The experimental field was chosen very responsibly with the aim to assure uniform type of harvested crop (alfalfa) and uniform crop maturity (81% moisture content on a wet basis). The field was even and the harvest together with hand measurement was carried out during 3 h (from 8 to 11 a.m.). It was cloudy weather. This was advantageous because material after harvest was not drying so fast and did not affect the accuracy of the measurement.
Fig. 2. Block diagram of electronic apparatus arrangement for material feed rate measurement.
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Fig. 3. Arrangement of measurement device for laboratory tests. Conditioner was equipped with impact plate, torque sensor and electronic measurement apparatus.
Material moisture content was determined from 10 samples from different parts of the field by a standardized method after harvest. Three samples were taken before harvest and seven during harvest at different places and times throughout the whole harvest. Moisture content of samples varied from 80 to 82% (on a wet basis). It was considered that the material moisture content was not changing during the measurement on the basis of those results. For infield measurements the signals from the DGPS receiver and signals from the torque sensor, rotary optical sensor of conditioner shaft, curved impact plate and front wheel speed counter were recorded every 5 s. A field of about 0.54 ha was harvested and measurements were made. There were 267 measurement points and so geostatistical data analysis could be carried out. At every DGPS sample, the harvested row was marked by a plastic card. The length of each measured row was approximately 150 m. Each section of harvested row between the cards was collected and weighed by hands after each pass of the machine. About 15 cells were collected from one harvested row and the distance between the plastic cards was 10 m. The weight of one cell varied from 4 to 16 kg according to instantaneous yield. It means that the whole row was finally weighed step by step. Further, next row was harvested by the mower and weighed in the same way. Altogether 19 rows were weighed up to the end of the harvested of experimental field. The losses of small particles on the surface were not monitored during the measurement. It was considered that those were almost similar across whole field. It was thus assured that the data obtained from the electronic measuring unit corresponded to the data obtained from manual weighing. It was possible both to evaluate the dependence of the conditioner power input and the signals from the impact plate on material yield later. Statistical processing of the data was performed using MS Excel, Statgraphics for Windows, ArcGIS 8, Geostatistical Analyst and GS+, version 5.1.1. 3. Results and discussion A mixture of grass and alfalfa was used for the purpose to determine the relationship between conditioner’s power input, impact plate force and material feed rate through the mower. Material was transported into the mowing machine for approximately 5 s for each measurement using the conveyer belt. The signals from the torque, speed and impact plate sensors were measured every half second, so 10 values were obtained from one single test run. Measurements started with material amount equalled to 1 kg WM s−1 feed rate. The test run with the same defined amount of material was then repeated at minimum three times. For next measurements the transported amount of material was then increased by increment approximately half a kilogram per second, up to 8 kg WM s−1 feed rate. The ten values from each test run were averaged to obtain the final result for a particular feed rate value. Calculated values were used for charting (Fig. 4). It was evident that power input of the conditioner linearly depended on material feed rate as well as the output frequency of the impact plate measuring apparatus (Fig. 4). The coefficients of determination were calculated to the value of 0.95 for the impact plate and 0.97 for the torque sensor in that case. Similar coefficients of determination were calculated for other measurements carried out. It was possible to distinguish between the material feed rate differences with a value of 0.5 kg WM s−1 . This accuracy should be sufficient in practical utilisation, i.e. creating yields maps, etc. Detailed description of that measurement was just published previously (Kumh´ala and Proˇsek, 2003). The objective of the next research was to test these two yield mapping capabilities at different conditions. The influence of changing crop variety, crop maturity and moisture and intensity of conditioning were measured. The impact of changing parameters in both measurement methods (torque sensor and impact plate) was studied.
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Fig. 4. Typical obtained chart for dependence of the conditioner power input and output frequency of the apparatus measuring impact force by means of impact plate arrangement on material feed rate.
Two types of material were used for this measurement: alfalfa with grass mixture and grass from a meadow. These crops were harvested on the same landholding but at different crop maturity and moisture content. Moisture content varied from 82 to 72% (wet basis) in alfalfa with grass mixture and from 77 to 74% (wet basis) in grass. To study the influence of conditioning intensity two levels of this intensity (low and high) were set up on mowing machine’s conditioner for each material measured. In the mowing machine ZTR 216 H, four levels of conditioning intensity was possible to set up by changing of a hood position. For the purpose of our measurement low conditioning intensity was defined when the hood set up lever was at the second position and high conditioning intensity when the hood set up lever was at the fourth position. Conditioner impeller rotational speed was not changed. To compare the influence of crop maturity (and moisture), the measurement with the material from the same landholding were repeated after 2 weeks. 3.1. Torque sensor Eight files of torque sensor measurement were obtained. The analysis of variance was used for the data evaluation in the first step. It was necessary to test the null hypothesis that the standard deviations of achieved data were the same. The P-values of Cochran’s and Bartlett’s test was calculated. Since both of these calculated P-values were less than 0.05 there was a statistically significant difference amongst the standard deviations at the 95% confidence level. This unfortunately violated one of the important assumptions underlying the analysis of variance. It was not possible to use the analysis of variance for the data evaluation in this case. From that reason it was decided to use two sample comparisons. Each file of measured data was compared with each other. In first statistically different comparison it was tested the function of measured device with the same grass from natural meadow with 77% material moisture (wet basis). The intensity of conditioning was the only factor changed. Set up intensity of conditioning was found to be an important factor affecting the torque sensor measurement. Alfalfa and grass mixture and grass alone was used next with the high level of conditioning. It was found that type of crop influenced the power input measured by torque sensor. It was also determined that crop maturity affected torque input (Fig. 5). 3.2. Impact plate Eight files of impact plate measurement were obtained as well. Analysis of variance was used to determine the impact plate effect. It was necessary to test the null hypothesis that the standard deviations of achieved data were the same. The P-values of Cochran’s test and Bartlett’s test was calculated. Since the smaller of the P-values was higher than 0.05, there was not a statistically significant difference amongst the standard deviations at the 95% confidence level and analysis of variance could be used. The calculated F-ratio equalled to 0.61 which was higher than 0.05 so there was not a statistically significant difference between the means of tested files at 95% confidence level. It follows from that analysis that the influence of
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Fig. 5. Graphic comparison of tested files from torque-meter measurement. Legend—Gra 1 con 2: grass in optimal harvest maturity (77% of moisture content), low intensity of conditioning; Alf 1 con 2: alfalfa in optimal harvest maturity (82% of moisture content), low intensity of conditioning; Alf 2 con 2: alfalfa after 2 weeks (72% of moisture content), low intensity of conditioning; Alf 2 con 4: alfalfa (72% of moisture content), high intensity of conditioning; Alf 1 con 4: alfalfa (82% of moisture content), high intensity of conditioning; Gra 1 con 4: grass (77% of moisture content), high intensity of conditioning. Table 1 Summary statistics of the variable Variable/parameter
Conditioner power input (kW)
Impact plate sensor reading (Hz)
Material feed rate (kg WM s−1 )
Average Standard deviation Coefficient of variation (%)
2.50 1.84 73.7
140.55 111.62 79.4
1.94 0.65 33.4
tested factors on impact plate measurement was not possible to statistically determine from our tests. Linear correlation could be applied to all data for that reason. The calculated R2 -value was 0.94 which was in concordance with the previous measurements. More detailed description of the effects of material changes and mowing machine parameters on the feed rate measurement accuracy was just published previously (Kumh´ala et al., 2003). 3.3. Infield measurement statistical analysis Summary statistics of power input, output frequency sensor reading of the impact plate and feed rate values calculated from hand weighing are given in Table 1. It can be seen from maximum and minimum values as well as from the variation coefficient that the variability of data was relatively high in all cases, especially for data coming from the electronic measurement device. The first step in data evaluation was the calculation of a correlation matrix (Table 2) which showed Pearson product moment correlation between each pair of variables. The results from Table 2 indicated a statistically significant correlation for all tested pairs of variables at a 99% confidence level. An important correlation from the useful outcomes Table 2 The correlation matrix of tested variables Variable
Conditioner power input (kW)
Conditioner power input (kW) Impact plate sensor reading (Hz) Material feed rate (kg s−1 )
0.84 0.73
Impact plate sensor reading (Hz)
Material feed rate (kg WM s−1 )
0.84
0.73 0.63
0.63
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Fig. 6. Variograms of conditioner power input (top), impact plate sensor reading (centre) and material feed rate (bottom).
point of view was between the data from the conditioner power input and material feed rate and the data from the impact plate sensor reading and material feed rate. It was possible to derive from Table 2 that the correlation was better for conditioner power input (0.73) than for impact plate sensor reading (0.63). This was possibly due to some problems with zero point setting on the impact plate measuring device. Another important aspect was the moderately strong relationship between the variables from conditioner power input measurement and impact plate sensor reading (0.84). It indicated that both measurement devices recorded very similar data—logged comparable variables during the measurement. 3.4. Infield measurement geostatistical analysis Spatial relationships between the measured files were studied by means of variogram construction and evaluation of variogram parameters. All data files obtained were standardized to zero mean and unit variance. Experimental variograms were built for all values measured. The experimental variogram calculated was substituted by a model variogram in the next step. The creation of the model variogram was done on the basis of R2 -values. This coefficient evaluates the accuracy of the fitted model by means of the sum of residual squares RSS (residual sums of squares). The variograms for all three measured files are shown in Fig. 6. It is clear from Fig. 6 that it was possible to model the variograms for all three measurements and to calculate the basic parameters of variograms. Beside variograms for all data, the cross-variograms for data combinations were created as well. The parameters of calculated variograms and cross-variograms are shown in Table 3.
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Table 3 Parameters of the variogram models Variable/parameter
Conditioner power input (kW2 )
Impact plate sensor (Hz2 )
Material feed rate (kg2 s−2 )
Material feed rate vs. conditioner (kW kg s−1 )
Material feed rate vs. impact plate sensor (Hz kg s−1 )
Impact plate sensor vs. conditioner (kW Hz)
Nugget C0 Sill C0 + C Range A0 (m) C0 /(C0 + C) (%) R2 RSS Model
0.30 1.74 54.2 17 0.88 0.42 Gaussian
0.34 1.98 65.4 17 0.97 0.1 Gaussian
0.34 2.1 70.3 16 0.97 0.12 Gaussian
0.06 1.63 61.8 4 0.92 0.31 Gaussian
0.001 1.73 71 1 0.96 0.17 Gaussian
0.15 1.74 60.8 9 0.94 0.22 Gaussian
The Gaussian variogram model was used for all evaluation of data. It is possible to derive from the value of R2 and RSS that the model variograms were well chosen in all cases. A strong spatial dependence was observed for all data (Table 3). The spatial relationship between the tested data was evaluated by cross-variograms. The values from the material feed rate measurement were used as a basic variable and the values of conditioner power input and impact plate sensor reading as an additional variable. In the case of conditioner power input and impact plate sensor reading files evaluation the impact plate sensor reading was used as the basic variable and conditioner power input as an additional variable. Experimental cross-variograms were fitted following the Gaussian model in all three cases and the parameters of crossvariograms were calculated (see Table 3). It is clear from C0 /(C0 + C) value that the spatial relationship of observed data was improved. A visual display of the data distribution is shown by the maps. These maps were plotted using the Kriging method. Plotted maps are shown in Fig. 7. Lighter parts of the maps show places in the field with lower measured values of observed variables and darker parts show the places with higher measured values. It can be seen that lower values were indicated in the south-western part of the field in all cases and contrastingly higher values were found in the north-eastern part of the field. If the color distribution in the maps is evaluated, it is clear that there are many similarities between the map of conditioner power input and material feed rate and between the map of impact plate sensor reading and material feed
Fig. 7. Maps of kriged estimates of conditioner power input (kW), impact plate sensor reading (Hz) and material feed rate (kg s−1 ).
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Table 4 Structural correlation coefficients of the conditioner power input, impact plate sensor reading and material feed rate Variable
Conditioner power input (kW)
Conditioner power input (kW) Impact plate sensor reading (Hz) Material feed rate (kg s−1 )
0.94 0.85
Impact plate sensor reading (Hz)
Material feed rate (kg WM s−1 )
0.94
0.85 0.85
0.85
rate. Nevertheless, the most significant similarity is visible between the map of conditioner power input and the map of impact plate sensor reading. The calculated structural correlation coefficient values (Table 4) were higher than the values of the correlation coefficient (see Table 2). It is possible to derive from these results that there is a relatively high and comparable spatial relationship (0.85) between the tested files of conditioner power input and material feed rate and impact plate sensor reading and material feed rate. It is possible to conclude that, from the geostatistical evaluation point of view there is not any important difference between the measurements based on conditioner power input and the measurements based on impact plate sensor reading, in relation to material feed rate measurements. The highest value of structural correlation coefficient was calculated for the conditioner power input and impact plate sensor reading (0.94). These results are in accordance with the results from the visual evaluation of maps created. Therefore, another important finding is that both measurement devices recorded very similar process of logged variables during the test and the function of both tested devices was similar. This was in accordance with the results from Pearson’s correlation analysis in the statistical analysis part of this paper as well. 4. Conclusion As the results show, the determination of yield by means of conditioner power input measurement and by means of material impact force measurement are potential approaches to yield map creation from a grass harvest by rotary mowing-conditioning machine. The laboratory measurements carried out proved that a very good linear relationship existed between the conditioner’s power input, output frequency of the apparatus measuring impact force by means of the impact plate, and material feed rate through the mowing machine. The calculated coefficients of determination (R2 ) were about 0.95. It was possible to distinguish between material feed rate differences 0.5 kg WM s−1 using both methods. Another important finding from the laboratory measurements was that the results from the torque sensor measurement are influenced by crop variety and maturity and intensity of conditioning. The same influence was not observed for impact plate. The infield measurement proved for both the methods (torque sensor, impact plate) in our configuration the linear dependence on material feed rate at a 99% confidence level. A structural correlation coefficient with a value of 0.85 was calculated for both data combinations. It can be concluded from the results of the measurements presented that both principles of material feed rate measurement can be used for grass yield map creation under real field conditions. Acknowledgement This project was funded by Ministry of Education, Youth and Sports of the Czech Republic, Research project no. MSM 6046070905. References Auernhammer, H., Demmel, M., Pirro, P., 1996. Lokale Ertragsermittlung mit dem Feldh¨ackslern (local yield monitoring in a forage harvester). Landtechnik 3, 152–153. Barnett, N.G., Shinners, K.J., 1998. Analysis of systems to measure mass-flow-rate and moisture on a forage harvester. ASAE Paper No. 981118. ASAE, St. Joseph, MI, USA. Demmel, M., Schwenke, T., Heuwinkel, H., Locher, F., Rottmeier, J., 2002. Lokale Ertragsermittlung in einem Scheibenm¨ahwerk mit Aufbereiten (local yield monitoring in a mower conditioner with windrowing device). In: Proceedings of Conference Agricultural Engineering 2002, Halle. VDI Verlag GmbH, Germany, pp. 139–143.
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Diekhans, N., 2002. Ein praxisnahes Verfahren f¨ur eine Ertragsmessung an Feldh¨ackslern (a practical solution for yield measurement on a forage harvester). In: Proceedings of Conference Agricultural Engineering 2002, Halle. VDI Verlag GmbH, Germany, pp. 133–137. Ehlert, D., Schmidt, H., 1995. Ertragskartierung mit Feldh¨ackslern (yield mapping in forage harvesters). Landtechnik 4, 204–205. Ehlert, D., Volker, U., Kalk, W.-D., 2002. Sensorgest¨utzte Stickstoffd¨ungung in Winterweizen (sensor based nitrogen fertilization in winter wheat). In: Proceedings of Conference Agricultural Engineering 2002, Halle. VDI Verlag GmbH, Germany, pp. 127–132. Kumh´ala, F., Proˇsek, V., 2003. Laboratory measurement of moving machine material feed rate. Precis. Agric. 4, 413–419. Kumh´ala, F., Kroul´ık, M., Maˇsek, J., Proˇsek, V., 2003. Development and testing of two methods for the measurement of the mowing machine feed rate. Plant Soil Environ. 49, 519–524. Martel, H., Savoie, P., 1999. Sensors to measure forage mass flow and moisture continuously. ASAE Paper No. 991050. ASAE, St. Joseph, MI, USA. Missotten, B., Broos, B., Strubbe, G., De Baerdemaeker, J., 1997. A yield sensor for forage harvesters, precision agriculture 1997. In: Stafford, J.V. (Ed.), Proceedings of the First European Conference on Precision Agriculture. BIOS Scientific Publishers Ltd., Oxford, UK, pp. 529–536. Ruhland, S., Haedicke, S., Wild, K., 2004. A measurement technique for determination of grass. In: Proceedings of Conference Agricultural Engineering 2004, Dresden. VDI Verlag GmbH, Germany, pp. 317–324. Shinners, K.J., Barnett, N.G., Schlesser, W.M., 2000. Measuring mass-flow-rate on forage cutting equipment. ASAE Paper No. 001036. ASAE, St. Joseph, MI, USA. Shinners, K.J., Huenink, B.M., Behringer, C.B., 2003. Precision agriculture as applied to North American hay and forage production. In: Qick, G. (Ed.), Proceedings of the International Conference on Crop Harvesting and Processing, ASAE Publication No. 701P1103e, Louisville, KY, USA. Schmittmann, O., Kromer, K.-H., Weltzien, C., 2001. Yield Monitoring on Forage Harvester. In: Blahovec, J., Libra, M. (Eds.), Proceedings of PMA 2001. CUA Prague, Czech Republic, pp. 286–291. Vansichen, R., De Baerdemaeker, J., 1993. A measurement technique for yield mapping of corn silage. J. Agric. Eng. Res. 55, 1–10. Wild, K., Ruhland, S., Haedicke, S., 2003. Pulse radar systems for yield measurements in forage harvesters. In: Stafford, J., Werner, A. (Eds.), Precision Agriculture, Proceedings of the Fourth European Conference on Precision Agriculture. Wageningen Academic Publishers, The Netherlands, pp. 739–744.
Development and evaluation of forage yield measure sensors in a mowing-conditioning machine Frantiˇsek Kumh´ala ∗ , Milan Kroul´ık, Vaclav Proˇsek Department of Agricultural Machines, Technical Faculty, Czech University of Agriculture in Prague, Kamycka 129, 165 21 Prague, 6 Suchdol, Czech Republic Received 13 January 2006; received in revised form 8 March 2007; accepted 28 March 2007
Abstract The main aim of this research was to develop and evaluate sensor system to create forage yield maps on a three-point linkage type of rotary mower-conditioner. The method was based on the mowing machine conditioner’s power requirement measured by a torque sensor or on material change in momentum measured by a curved impact plate. Laboratory measurements were taken to determine the dependence of conditioner power input and signals from the impact plate on material mass flow. A mixture of grass and alfalfa was used. There was a very good linear relationship between the conditioner’s power, impact force from an impact plate, and material feed rate through the mower. The calculated coefficients of determination (R2 ) were about 0.95. It was possible to differentiate a material feed rate difference 0.5 kg s−1 using either method. The effects of material changes and mower parameters on the accuracy of feed rate measurement were then measured. It was observed that the results form torque sensor was influenced by crop variety, maturity and intensity of conditioning. The same influence was not observed for impact plate. A field of 0.54 ha was then harvested. A comparison of data from the torque sensor and impact plate with data from hand measurement were made by means of statistical and geostatistical analysis. Conditioner power input measurement and crop impact force were used to create grass yield maps. © 2007 Elsevier B.V. All rights reserved. Keywords: Yield maps; Grass; Alfalfa; Mowing machine; Conditioner; Impact plate; Torque measurement
1. Introduction 1.1. Forage harvesters Vansichen and De Baerdemaeker (1993) calculated yield from the torque of a forage harvester’s blower. Another possibility is to measure the distance between feeder rolls of the harvester (Ehlert and Schmidt, 1995; Auernhammer et al., 1996; Barnett and Shinners, 1998; Martel and Savoie, 1999; Schmittmann et al., 2001; Diekhans, 2002). A mass flow sensor for a pull type (trailed) forage harvester based on a reaction plate in the spout was constructed and tested by Missotten et al. (1997). Similar sensors were tested by other authors (Barnett and Shinners, 1998; Martel and Savoie, 1999; Schmittmann et al., 2001) for self-propelled forage harvesters. Martel and Savoie (1999) measured
∗
Corresponding author. Tel.: +420 224 383 135; fax: +420 22 383 122. E-mail address: [email protected] (F. Kumh´ala).
0168-1699/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.compag.2007.03.013
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electrical capacitance in the spout of a forage harvester and Schmittmann et al. (2001) measured crop layer thickness. Some of these methods (e.g. distance between feeder rolls, reaction plate, and crop layer thickness) are very interesting and showed a good coefficient of determination (R2 = 0.94–0.98). Some methods (e.g. distance between feeder rolls, electrical capacitance in the spout of a forage harvester) have to be supplied with several calibration parameters. Site-specific measurement of biomass in growing cereal crops has been proposed by Ehlert et al. (2002) using a pivoted cylindrical body moving horizontally through a plant population (moving pendulum). The angle of deviation of this pendulum varies with the plant properties. 1.2. Mowing machines The feed rate measurement technique for mowing machines is not so well developed. Demmel et al. (2002) used a principle based on belt weighing technology in the windrowing device of a mower. Recently, Ruhland et al. (2004) determined yield by means of determining the torque requirements in the windrowing device of a mower. Both methods are suitable only for mowing machines equipped with a windrowing device. A pulse radar system for grass yield measurements is still under development (Wild et al., 2003). The results obtained from the measurement showed that the sensors need further improvement. Shinners et al. (2000) developed and evaluated systems to measure material feed rate on a self-propelled forage windrower. They tested conditioning roll force, conditioning roll rise and swath shield impact force as the predictors of material feed rate. The only system to show promise of adequately predicting material feed rate through the machine was impact force on the swath shield. Recently, Shinners et al. (2003) equipped a windrower to measure material feed rate by the following sensors: pressure sensor to measure the load at the platform drive motor; speed pick-up to measure conditioning roll speed, load cell to measure crop impact on the swath forming shield and rotary potentiometers to measure crop volumetric flow past swath forming shield. The crop volumetric flow was well correlated with material feed rate when the sensor output was combined with platform inclination and roll speed. The results from these measurements were meaningful (R2 = 0.94). It can be seen from the presented literature review that we have not found material feed rate sensors suitable for three-point linkage or pulled rotary mowing machines equipped with a conditioner but without a windrowing device. The research and development of yield mapping capabilities for above-mentioned machines was the main objective of the work presented. Another reason was that on the basis of current techniques farmers can obtain yield maps from grain fields only. For responsible application of precision farming system on the field it is sometimes necessary to know the yield of forage crops as well (for example when the perennial forage is one of harvested plants in plant rotation system). 2. Materials and experimental methods A three-point linkage mowing machine ZTR 216 H (Agrostroj Pelhrimov Company, Czech Republic) was used for the measurements. This machine consisted of two working drums and a finger conditioner. Working width of the machine was 2.15 m. The mowing machine was equipped with an electronic measuring unit developed in our laboratory. The mowing machine’s conditioner shaft was supplied with a VUZT model torque sensor based on resistant strain gauges and with an optical sensor measuring conditioner shaft speed. Besides the torque sensor, the mowing machine was equipped with a curved impact plate mounted at the exit of the machine. The material ejected from the mowing machine conditioner struck the impact plate. The force created from the change in direction was measured by four elastic members with strain gauge bridges for force measurement (Fig. 1). Voltage signals from the strain gauges on the torque sensor and the impact-plate were amplified and converted into frequency by an electronic measuring apparatus developed in our laboratory. The output frequency was proportional to the measured force. For infield measurements the mowing machine was also equipped with DGPS signal antenna placed in the centre of the mowing machine and with a DGPS signal receiver placed in the cab of the tractor. A front wheel rotary speed counter was used for measuring the working speed of the tractor. The block diagram of this electronic apparatus arrangement is shown in Fig. 2. The torque sensor was calibrated in the range from 0 to 800 N m. The output frequency was correlated with measured torque at an accuracy better than 1%.
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Fig. 1. Equipment for material feed rate measurement.
The calibration of the impact plate was divided into two steps. In the first step, the strain gauge bridges were adjusted so that the relation of the output voltage with measured loading force was the same for all bridges. The aim of the second step of the calibration was to avoid the dependence of the impact plate arrangement output signal on the position where the force acts on the impact plate. In order to achieve this goal, the level of voltages from each strain gauge bridge was properly adjusted by means of resistor dividers. After this second step of calibration procedure, the final output signal does not depend on the position where the force acts on the impact plate. Laboratory measurements were performed to obtain information about the relationship of conditioner power input and signals from the impact plate to the material feed rate. It was necessary to determine the amount of conditioner’s material feed rate. The laboratory set-up was composed of a conveyer belt carrying a measured quantity of material, a tractor and a rotary drum-mowing machine equipped with the electronic measurement apparatus (Fig. 3). The same arrangement of measurement device was used to determine the effects of material changes and mowing machine parameters on the feed rate measurement accuracy. The experimental field was chosen very responsibly with the aim to assure uniform type of harvested crop (alfalfa) and uniform crop maturity (81% moisture content on a wet basis). The field was even and the harvest together with hand measurement was carried out during 3 h (from 8 to 11 a.m.). It was cloudy weather. This was advantageous because material after harvest was not drying so fast and did not affect the accuracy of the measurement.
Fig. 2. Block diagram of electronic apparatus arrangement for material feed rate measurement.
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Fig. 3. Arrangement of measurement device for laboratory tests. Conditioner was equipped with impact plate, torque sensor and electronic measurement apparatus.
Material moisture content was determined from 10 samples from different parts of the field by a standardized method after harvest. Three samples were taken before harvest and seven during harvest at different places and times throughout the whole harvest. Moisture content of samples varied from 80 to 82% (on a wet basis). It was considered that the material moisture content was not changing during the measurement on the basis of those results. For infield measurements the signals from the DGPS receiver and signals from the torque sensor, rotary optical sensor of conditioner shaft, curved impact plate and front wheel speed counter were recorded every 5 s. A field of about 0.54 ha was harvested and measurements were made. There were 267 measurement points and so geostatistical data analysis could be carried out. At every DGPS sample, the harvested row was marked by a plastic card. The length of each measured row was approximately 150 m. Each section of harvested row between the cards was collected and weighed by hands after each pass of the machine. About 15 cells were collected from one harvested row and the distance between the plastic cards was 10 m. The weight of one cell varied from 4 to 16 kg according to instantaneous yield. It means that the whole row was finally weighed step by step. Further, next row was harvested by the mower and weighed in the same way. Altogether 19 rows were weighed up to the end of the harvested of experimental field. The losses of small particles on the surface were not monitored during the measurement. It was considered that those were almost similar across whole field. It was thus assured that the data obtained from the electronic measuring unit corresponded to the data obtained from manual weighing. It was possible both to evaluate the dependence of the conditioner power input and the signals from the impact plate on material yield later. Statistical processing of the data was performed using MS Excel, Statgraphics for Windows, ArcGIS 8, Geostatistical Analyst and GS+, version 5.1.1. 3. Results and discussion A mixture of grass and alfalfa was used for the purpose to determine the relationship between conditioner’s power input, impact plate force and material feed rate through the mower. Material was transported into the mowing machine for approximately 5 s for each measurement using the conveyer belt. The signals from the torque, speed and impact plate sensors were measured every half second, so 10 values were obtained from one single test run. Measurements started with material amount equalled to 1 kg WM s−1 feed rate. The test run with the same defined amount of material was then repeated at minimum three times. For next measurements the transported amount of material was then increased by increment approximately half a kilogram per second, up to 8 kg WM s−1 feed rate. The ten values from each test run were averaged to obtain the final result for a particular feed rate value. Calculated values were used for charting (Fig. 4). It was evident that power input of the conditioner linearly depended on material feed rate as well as the output frequency of the impact plate measuring apparatus (Fig. 4). The coefficients of determination were calculated to the value of 0.95 for the impact plate and 0.97 for the torque sensor in that case. Similar coefficients of determination were calculated for other measurements carried out. It was possible to distinguish between the material feed rate differences with a value of 0.5 kg WM s−1 . This accuracy should be sufficient in practical utilisation, i.e. creating yields maps, etc. Detailed description of that measurement was just published previously (Kumh´ala and Proˇsek, 2003). The objective of the next research was to test these two yield mapping capabilities at different conditions. The influence of changing crop variety, crop maturity and moisture and intensity of conditioning were measured. The impact of changing parameters in both measurement methods (torque sensor and impact plate) was studied.
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Fig. 4. Typical obtained chart for dependence of the conditioner power input and output frequency of the apparatus measuring impact force by means of impact plate arrangement on material feed rate.
Two types of material were used for this measurement: alfalfa with grass mixture and grass from a meadow. These crops were harvested on the same landholding but at different crop maturity and moisture content. Moisture content varied from 82 to 72% (wet basis) in alfalfa with grass mixture and from 77 to 74% (wet basis) in grass. To study the influence of conditioning intensity two levels of this intensity (low and high) were set up on mowing machine’s conditioner for each material measured. In the mowing machine ZTR 216 H, four levels of conditioning intensity was possible to set up by changing of a hood position. For the purpose of our measurement low conditioning intensity was defined when the hood set up lever was at the second position and high conditioning intensity when the hood set up lever was at the fourth position. Conditioner impeller rotational speed was not changed. To compare the influence of crop maturity (and moisture), the measurement with the material from the same landholding were repeated after 2 weeks. 3.1. Torque sensor Eight files of torque sensor measurement were obtained. The analysis of variance was used for the data evaluation in the first step. It was necessary to test the null hypothesis that the standard deviations of achieved data were the same. The P-values of Cochran’s and Bartlett’s test was calculated. Since both of these calculated P-values were less than 0.05 there was a statistically significant difference amongst the standard deviations at the 95% confidence level. This unfortunately violated one of the important assumptions underlying the analysis of variance. It was not possible to use the analysis of variance for the data evaluation in this case. From that reason it was decided to use two sample comparisons. Each file of measured data was compared with each other. In first statistically different comparison it was tested the function of measured device with the same grass from natural meadow with 77% material moisture (wet basis). The intensity of conditioning was the only factor changed. Set up intensity of conditioning was found to be an important factor affecting the torque sensor measurement. Alfalfa and grass mixture and grass alone was used next with the high level of conditioning. It was found that type of crop influenced the power input measured by torque sensor. It was also determined that crop maturity affected torque input (Fig. 5). 3.2. Impact plate Eight files of impact plate measurement were obtained as well. Analysis of variance was used to determine the impact plate effect. It was necessary to test the null hypothesis that the standard deviations of achieved data were the same. The P-values of Cochran’s test and Bartlett’s test was calculated. Since the smaller of the P-values was higher than 0.05, there was not a statistically significant difference amongst the standard deviations at the 95% confidence level and analysis of variance could be used. The calculated F-ratio equalled to 0.61 which was higher than 0.05 so there was not a statistically significant difference between the means of tested files at 95% confidence level. It follows from that analysis that the influence of
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Fig. 5. Graphic comparison of tested files from torque-meter measurement. Legend—Gra 1 con 2: grass in optimal harvest maturity (77% of moisture content), low intensity of conditioning; Alf 1 con 2: alfalfa in optimal harvest maturity (82% of moisture content), low intensity of conditioning; Alf 2 con 2: alfalfa after 2 weeks (72% of moisture content), low intensity of conditioning; Alf 2 con 4: alfalfa (72% of moisture content), high intensity of conditioning; Alf 1 con 4: alfalfa (82% of moisture content), high intensity of conditioning; Gra 1 con 4: grass (77% of moisture content), high intensity of conditioning. Table 1 Summary statistics of the variable Variable/parameter
Conditioner power input (kW)
Impact plate sensor reading (Hz)
Material feed rate (kg WM s−1 )
Average Standard deviation Coefficient of variation (%)
2.50 1.84 73.7
140.55 111.62 79.4
1.94 0.65 33.4
tested factors on impact plate measurement was not possible to statistically determine from our tests. Linear correlation could be applied to all data for that reason. The calculated R2 -value was 0.94 which was in concordance with the previous measurements. More detailed description of the effects of material changes and mowing machine parameters on the feed rate measurement accuracy was just published previously (Kumh´ala et al., 2003). 3.3. Infield measurement statistical analysis Summary statistics of power input, output frequency sensor reading of the impact plate and feed rate values calculated from hand weighing are given in Table 1. It can be seen from maximum and minimum values as well as from the variation coefficient that the variability of data was relatively high in all cases, especially for data coming from the electronic measurement device. The first step in data evaluation was the calculation of a correlation matrix (Table 2) which showed Pearson product moment correlation between each pair of variables. The results from Table 2 indicated a statistically significant correlation for all tested pairs of variables at a 99% confidence level. An important correlation from the useful outcomes Table 2 The correlation matrix of tested variables Variable
Conditioner power input (kW)
Conditioner power input (kW) Impact plate sensor reading (Hz) Material feed rate (kg s−1 )
0.84 0.73
Impact plate sensor reading (Hz)
Material feed rate (kg WM s−1 )
0.84
0.73 0.63
0.63
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Fig. 6. Variograms of conditioner power input (top), impact plate sensor reading (centre) and material feed rate (bottom).
point of view was between the data from the conditioner power input and material feed rate and the data from the impact plate sensor reading and material feed rate. It was possible to derive from Table 2 that the correlation was better for conditioner power input (0.73) than for impact plate sensor reading (0.63). This was possibly due to some problems with zero point setting on the impact plate measuring device. Another important aspect was the moderately strong relationship between the variables from conditioner power input measurement and impact plate sensor reading (0.84). It indicated that both measurement devices recorded very similar data—logged comparable variables during the measurement. 3.4. Infield measurement geostatistical analysis Spatial relationships between the measured files were studied by means of variogram construction and evaluation of variogram parameters. All data files obtained were standardized to zero mean and unit variance. Experimental variograms were built for all values measured. The experimental variogram calculated was substituted by a model variogram in the next step. The creation of the model variogram was done on the basis of R2 -values. This coefficient evaluates the accuracy of the fitted model by means of the sum of residual squares RSS (residual sums of squares). The variograms for all three measured files are shown in Fig. 6. It is clear from Fig. 6 that it was possible to model the variograms for all three measurements and to calculate the basic parameters of variograms. Beside variograms for all data, the cross-variograms for data combinations were created as well. The parameters of calculated variograms and cross-variograms are shown in Table 3.
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Table 3 Parameters of the variogram models Variable/parameter
Conditioner power input (kW2 )
Impact plate sensor (Hz2 )
Material feed rate (kg2 s−2 )
Material feed rate vs. conditioner (kW kg s−1 )
Material feed rate vs. impact plate sensor (Hz kg s−1 )
Impact plate sensor vs. conditioner (kW Hz)
Nugget C0 Sill C0 + C Range A0 (m) C0 /(C0 + C) (%) R2 RSS Model
0.30 1.74 54.2 17 0.88 0.42 Gaussian
0.34 1.98 65.4 17 0.97 0.1 Gaussian
0.34 2.1 70.3 16 0.97 0.12 Gaussian
0.06 1.63 61.8 4 0.92 0.31 Gaussian
0.001 1.73 71 1 0.96 0.17 Gaussian
0.15 1.74 60.8 9 0.94 0.22 Gaussian
The Gaussian variogram model was used for all evaluation of data. It is possible to derive from the value of R2 and RSS that the model variograms were well chosen in all cases. A strong spatial dependence was observed for all data (Table 3). The spatial relationship between the tested data was evaluated by cross-variograms. The values from the material feed rate measurement were used as a basic variable and the values of conditioner power input and impact plate sensor reading as an additional variable. In the case of conditioner power input and impact plate sensor reading files evaluation the impact plate sensor reading was used as the basic variable and conditioner power input as an additional variable. Experimental cross-variograms were fitted following the Gaussian model in all three cases and the parameters of crossvariograms were calculated (see Table 3). It is clear from C0 /(C0 + C) value that the spatial relationship of observed data was improved. A visual display of the data distribution is shown by the maps. These maps were plotted using the Kriging method. Plotted maps are shown in Fig. 7. Lighter parts of the maps show places in the field with lower measured values of observed variables and darker parts show the places with higher measured values. It can be seen that lower values were indicated in the south-western part of the field in all cases and contrastingly higher values were found in the north-eastern part of the field. If the color distribution in the maps is evaluated, it is clear that there are many similarities between the map of conditioner power input and material feed rate and between the map of impact plate sensor reading and material feed
Fig. 7. Maps of kriged estimates of conditioner power input (kW), impact plate sensor reading (Hz) and material feed rate (kg s−1 ).
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Table 4 Structural correlation coefficients of the conditioner power input, impact plate sensor reading and material feed rate Variable
Conditioner power input (kW)
Conditioner power input (kW) Impact plate sensor reading (Hz) Material feed rate (kg s−1 )
0.94 0.85
Impact plate sensor reading (Hz)
Material feed rate (kg WM s−1 )
0.94
0.85 0.85
0.85
rate. Nevertheless, the most significant similarity is visible between the map of conditioner power input and the map of impact plate sensor reading. The calculated structural correlation coefficient values (Table 4) were higher than the values of the correlation coefficient (see Table 2). It is possible to derive from these results that there is a relatively high and comparable spatial relationship (0.85) between the tested files of conditioner power input and material feed rate and impact plate sensor reading and material feed rate. It is possible to conclude that, from the geostatistical evaluation point of view there is not any important difference between the measurements based on conditioner power input and the measurements based on impact plate sensor reading, in relation to material feed rate measurements. The highest value of structural correlation coefficient was calculated for the conditioner power input and impact plate sensor reading (0.94). These results are in accordance with the results from the visual evaluation of maps created. Therefore, another important finding is that both measurement devices recorded very similar process of logged variables during the test and the function of both tested devices was similar. This was in accordance with the results from Pearson’s correlation analysis in the statistical analysis part of this paper as well. 4. Conclusion As the results show, the determination of yield by means of conditioner power input measurement and by means of material impact force measurement are potential approaches to yield map creation from a grass harvest by rotary mowing-conditioning machine. The laboratory measurements carried out proved that a very good linear relationship existed between the conditioner’s power input, output frequency of the apparatus measuring impact force by means of the impact plate, and material feed rate through the mowing machine. The calculated coefficients of determination (R2 ) were about 0.95. It was possible to distinguish between material feed rate differences 0.5 kg WM s−1 using both methods. Another important finding from the laboratory measurements was that the results from the torque sensor measurement are influenced by crop variety and maturity and intensity of conditioning. The same influence was not observed for impact plate. The infield measurement proved for both the methods (torque sensor, impact plate) in our configuration the linear dependence on material feed rate at a 99% confidence level. A structural correlation coefficient with a value of 0.85 was calculated for both data combinations. It can be concluded from the results of the measurements presented that both principles of material feed rate measurement can be used for grass yield map creation under real field conditions. Acknowledgement This project was funded by Ministry of Education, Youth and Sports of the Czech Republic, Research project no. MSM 6046070905. References Auernhammer, H., Demmel, M., Pirro, P., 1996. Lokale Ertragsermittlung mit dem Feldh¨ackslern (local yield monitoring in a forage harvester). Landtechnik 3, 152–153. Barnett, N.G., Shinners, K.J., 1998. Analysis of systems to measure mass-flow-rate and moisture on a forage harvester. ASAE Paper No. 981118. ASAE, St. Joseph, MI, USA. Demmel, M., Schwenke, T., Heuwinkel, H., Locher, F., Rottmeier, J., 2002. Lokale Ertragsermittlung in einem Scheibenm¨ahwerk mit Aufbereiten (local yield monitoring in a mower conditioner with windrowing device). In: Proceedings of Conference Agricultural Engineering 2002, Halle. VDI Verlag GmbH, Germany, pp. 139–143.
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