University of Illinois. Urbana IL. K. A. Sudduth. Cropping Systems and Water Quality Research Unit. USDA-Agricultural ..... Moline, IL. Nave, W. R., J. W. Hummel, ...
SENSING CORN POPULATION -ANOTHER VARIABLE IN THE YIELD EQUATION J. W. Hummel Cropping Systems and Water Quality Research Unit USDA-Agricultural Research Service Columbia MO B. M. Lobdell Agricultural Engineering Department University of Illinois Urbana IL K. A. Sudduth Cropping Systems and Water Quality Research Unit USDA-Agricultural Research Service Columbia MO S. J. Birrell Agricultural and Biosystems Engineering Department Iowa State University Ames, IA ABSTRACT Crop yield maps are required to evaluate the economic efficiency of spatial production systems and are an important part of the site-specific decision-making process. However, plant population has a significant effect on yield potential and with the exception of climatic conditions, plant population can be the predominant factor limiting crop yields. Therefore, sensor-derived maps of plant population can be useful for interpreting the effect of other limiting factors on yield, and can provide important information for developing site-specific management plans. We designed and fabricated a mechanical sensor that counted corn plants as they entered the gathering chains of a combine header. We evaluated the performance of this corn population sensor over multiple years and locations. When compared to hand counts obtained at harvest, the sensors tended to slightly underestimate actual population. Errors were minimized when the combine header was operated close to the ground surface and at speeds no greater than 4.5 mph. Sensor evaluation in corn seeded at various rates revealed an increasing underestimation error with increasing population. This error was a linear function of the corn stalk feed rate past the sensor. After compensation was applied, sensed population was an excellent estimator of actual, hand-counted population (r2 = 0.93, zero mean error). Standard errors of population estimates were 802 plants/ac at feed rates below 9 plants/s and 1700 plants/ac at feed rates above 9 plants/s.
A photoelectric sensor, capable of estimating plant diameter and plant spacing as well as population, was developed and field tested on a 4-row combine corn head. An emitter and receiver pair produced the signal used to measure the in-row distance between plants to provide information on plant spacing, skips, and doubles. An air-jet system was fitted onto the header to move corn leaves and other debris away from the sensor area. Data were collected at harvest with the sensor and compared with manually collected plant distance and diameter data. Plant spacing and stalk diameter were used in software filtering to remove erroneous plant counts due to weeds and plant leaves. Data were post-processed to compare with manually collected plant diameters and spacings, and filtering techniques were developed to improve diameter, spacing, and population estimation. The higher air speed levels decreased false optical counts caused by leaves and weeds and produced more accurate estimates of plant population. INTRODUCTION Plant population is known to have a considerable effect on corn yield (Nielsen 1995). Accurate spatial plant population data could be an important factor in site-specific crop management. The data could help quantify the plant population-yield relationship, improve the interpretation of yield maps, be useful in identifying problem areas in a field, and aid in planter performance analysis. A realtime corn population sensor could provide this data including information on average plant spacing, variation of plant spacing, number of doubles, and number of skips. Without the sensor, getting a true measure of plant population and spacing uniformity manually is tedious, time consuming, and expensive (Fee 1994). Several attempts to develop a corn population counter have been reported in the literature. Easton (1996) developed a hand-held corn population analyzer. The device included a measuring wheel which rolled along the corn row and sensed plants with a spring-loaded wire arm, providing population data on up to 99 plants. Deere and Company (Gore 1995) engineers built a stalk counter and were able to estimate corn populations with up to 95% accuracy. A major source of error was multiple counts in areas of heavy weed infestation. An inherent problem with the mechanical-arm design population sensor was the inability to detect two stalks closer than 1.5 in., in addition to its sensitivity to weeds. MECHANICAL POPULATION SENSOR The initial design and testing of a combine-mounted mechanical corn population sensor were described by Birrell and Sudduth (1995). The sensor (Figure 1) consisted of a spring-loaded rod attached to a rotary potentiometer, mounted in front of the gathering chains on the row dividers of the combine head. During harvesting, the corn stalks caused the rod to rotate backward, increasing the voltage potential across the potentiometer. When the stalk released the rod, a sharp decrease in voltage occurred. The potentiometer output was fed through a low-pass filter into an analog derivative circuit and digital filter circuit to convert the sharp drop in potential into a pulse recorded by a digital counter.
Figure 1. Redesigned mechanical population sensor, showing operating location of sensing rod on combine header (right). Several modifications were made to the initial design prior to a second study (Sudduth et al. 2000); however, the general operation of the sensor remained the same as described by Birrell and Sudduth (1995). To provide better flow of plants past the sensor, the mechanism was redesigned to fit within the row divider, with only the sensor rod protruding above (Figure 1). A mechanical shock absorber was added to cushion the return of the sensor rod to the at-rest position. Values of some electronic components were adjusted slightly to optimize operation of the system. Four modified population sensors as described above were installed in 1997 on a Gleaner R421 combine with a 4-row corn head. This system was used for the majority of this evaluation study. Output from the electronic circuit was captured by a digital counter and recorded on a laptop computer along with grain yield and GPS position. Sensor data from each row were recorded separately. In 1999, a Gleaner R62 combine with a 6-row corn head was also fitted with population sensors. One sequence of tests in this study utilized data from this second combine. The effect of varying combine operating conditions on sensor accuracy was evaluated. Data were collected at a range of speeds spanning normal harvesting conditions. Data were also collected at two header heights – a “low” position with the floating divider tips approximately 4 - 6 in. above the ground surface and a “high” position with the divider tips approximately 18 - 22 in. above the ground. Combine operating speed did have a significant effect on population estimation errors. Operation at 5.6 mph yielded mean underestimation errors of 1700 plants/ac, while operation at 4.5 mph or slower yielded mean underestimation errors of only 802 plants/ac. Speed effects were not significant below 4.5 mph. Increased underestimation of population at higher speeds was likely due to the response time of the counting mechanism.
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Combine header height also had a significant effect on plant population estimation for some field conditions. Populations were underestimated by 7.5% using the high header position in fields having small or weak corn plants. Underestimation was only 0.5% in the same fields using the low header position. It appeared that the additional stalk flexibility in combination with
the high header position allowed more stalks to lean to one side and not actuate the counter as they entered the gathering chains. Under good sensing conditions — harvesting at speeds below 4.5 mph, operation at a low header position, and in areas with strong stalks — the average underestimation of single rows of handcounted plants was only 0.08%. Using data from all 4 rows, the average underestimation of handcounted population was 2.2%. Using a calibration based on seeding rate was useful in correcting corn
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population estimation (Figure 2), but an upper feed rate limit of 9 plant/s — obtained by reducing harvest speed when harvesting higher population areas — was necessary to minimize errors.
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A single-row Figure 2. Relationship of calibrated, sensor-estimated population to early-season, hand-counted population for data photoelectric corn population from variable-rate seeding study. sensor was developed to address some of the problems incurred by previous corn population sensor systems (Plattner and Hummel, 1996). A Banner Model SM31L emitter and receiver pair (Banner Engineering Corp., 1994) was used as the sensor in this research. The emitter, fitted with a vertical slit aperture, projected a light beam across the row to the receiver. The sensor system was designed so that as the sensor moved down the row, the corn stalks interrupted the light beam and were counted. This system, with the through-beam sensor's ability to measure light and dark periods, had the capability to measure plant diameter and plant spacing in addition to population data. A distance input was needed for the system so that the times of dark and light events could be converted to stalk diameter and plant spacing. A radar distance input was used, and the dark and light time periods were multiplied by the velocity to obtain plant diameter and distance, respectively. A signal processing circuit was developed to convert the signals from the photoelectric sensor and the distance sensor into a form that was usable for the data acquisition board. A C-language program was written to read and reset the data acquisition board counters. Also, a real-time filtering method was written into the software to discard any plants with a diameter less than a minimum allowable value. Plant leaves, passing between the emitter-receiver pair, produced false counts and reduced the accuracy of the plant diameter and spacing estimation. Additional testing (Chaney 1997), using flexible fingers protruding through slots in the row dividers, produced slight reductions in false counts but the improvement was not significant when software filtering was used.
We recently completed additional research, in which we mounted photoelectric sensors on a 4row corn head on a John Deere 4420 combine. Previous research (Nave et al. 1977) had shown that air jets could be successfully employed to produce an air stream to move crop material on a combine. The use of air to move plant material other than stalks to decrease false counts and provide accurate plant diameter and spacing information appeared promising. An air-jet system was designed to move material other than corn stalks from the path of the photoelectric sensor beam. A pressure blower was mechanically driven by the combine’s header drive shaft. A PVC pipe manifold and duct system carried the high velocity air to the air jets, which were slightly forward of the optical sensor location in each of the row dividers. The air system outlets (Figure 3) consisted of 5.1-cm PVC pipe extending through holes cut in the row dividers. The outlets were in front of the optical sensors by an average distance of 2.1 in. from the center of the optical sensors to the center of the air jet outlet. Dampers installed in the manifold allowed tests at several different air velocity settings. Field tests were carried out in corn planted in 30-in. rows at a population of 33,000 seeds/ac. Fifteen treatments (combinations of three implement travel speeds and five air treatments) were included in the tests. The three travel speeds were 2.5, 3.1 and 3.7 mph. Travel speed was limited by the maximum gathering chain speed of the combine. The air treatments were: 1) corn leaves stripped from stalks and zero air velocity, 2) zero air velocity, 3) low air velocity (~92 ft/s), 4) intermediate air velocity (~177 ft/s), and 5) high air velocity (~259 ft/s). A Windows®-based post-processing program developed to compare the sensor data with manually measured plant spacings and stalk diameters was used to analyze the data (Drummond, 2001). The program included filtering methods to eliminate false counts from the data set. A minimum diameter filter was set at 0.47 in. This filter eliminated any dark period, presumably a weed or other plant material, that was under 0.47 in. in diameter. A maximum diameter filter was set at 2.0 in. to eliminate any dark period, presumably a leaf, over 2.0 in. in length. A minimum distance filter set at 2.36 in. removed the second of any two dark periods that were spaced less than 2.36 in. from each other, to eliminate doubles from the plant count.
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Figure 3. Figure The effect of travel speed on normalized 4. Photoelectric emitter and air-jetcounts. outlet locations in corn head divider point. Plant Population Estimation Plant population estimation, i.e., plants/unit area, was the first variable of interest. A variable, normalized count, was formed by dividing the sensor counts by the manual plant counts for each row of each plot. The average sensor normalized counts for all rows and treatments were 0.942 for the optical sensor. Optimum sensor performance would correspond to a normalized count value of one. Performance of the optical sensor was significantly poorer (" = 0.05) at the highest travel speed (Figure 4). The low header gathering chain speed used during Replication 1 (rearward velocity of 1.75 mph) caused bunching of the corn stalks in the area of the sensor, particularly at the high travel speed, and was a major contributor to the low counts. For Replications 2 and 3, the gathering chain speed was set at its highest speed and plant movement into the snapping rolls was improved, even though the rearward velocity was still only 3.0 mph. Normalized optical counts were higher for the zero air setting (Figure 5), and the counts trended lower as increased air velocity moved more plant leaves away from the sensor. However, there was no difference in normalized optical counts between the intermediate and high air settings. This result indicates that air velocities higher than the intermediate setting level would not improve the estimation of plant population. Even though increased air velocity reduced the incidences when leaves contributed to normalized optical count, the effect of air setting was not significant. The normalized counts trended lower with increased air velocity, away from the ideal value of one, because false counts were being eliminated from the total optical count.
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Figure 5. The effect of air setting on normalized counts. Air settings: 1) no leaves and zero air, 2) zero air, 3) low air velocity, 4) intermediate air velocity, and 5) high air velocity.
Plant Diameter Estimation Plant diameter is a very important factor in corn stalk integrity as well as productivity, especially in silage hybrids. The optical sensors were used to estimate plant diameter. Plant diameters after the filters were applied were used in the statistical analysis. Plant diameters were measured by the optical sensors for all four rows of every plot, but plant diameter was measured manually only in row one. A variable, normalized plant diameter, was formed to make a comparison between sensor data and actual field measurements of plant diameter. Normalized plant diameters were the optical sensor diameter estimations divided by the manual plant diameter measurements. The average normalized optical sensor diameter for all rows and treatments was 1.095. The optimum sensor performance would correspond to a normalized diameter value of one. The optical sensor overestimated the manual diameter measurements for all three experimental travel speeds (Figure 6). Optical sensor diameter estimation was significantly lower (" = 0.05) at the lowest travel speed, but there were no significant differences in normalized optical sensor diameter. Normalized diameter was the least accurate at the high travel speed. This decrease in sensor accuracy at the highest travel speed is attributed to characteristics of the photoelectric sensors. The emitter modulated the light beam at a set frequency, while the receiver demodulated the signal. During the dark-to-light transition (trailing edge of a stalk), the demodulation scheme calls for the receiver to wait until four pulses at the set frequency have occurred before the output is changed. This short delay is present so that only the sensor beam is used as the signal from the emitter rather than ambient light or some other light signal (Banner Engineering Corp., 1994). The short delay in recognizing the light beam after a dark event makes the dark-to-light transition less accurate than the light-to-dark transition. As travel speed increased, the distance traveled during the delay also increased, causing an increase in the plant diameter estimate. Normalized optical diameters were nearly equal at air settings two and three (zero and low air) (Figure 7), and approached the level achieved at air setting one as air velocity was increased. Air setting five decreased normalized diameter to 1.060, although no significant differences occurred at the " = 0.05 level. The trend toward a more accurate plant diameter estimation at air setting five may have indicated that the high air velocity was moving leaves that were hanging beside corn stalks away from the sensor beam area. This process may have decreased the sensor diameter to the actual stalk diameter instead of sensing the stalk and adjacent leaves, estimating a false high diameter. Air setting five appeared to improve diameter measurement accuracy more than air settings three and four. Normalized diameter at air setting one (no air; leaves stripped from plants) was 1.009, indicating that the optical sensor is able to accurately sense stalk diameters in a controlled environment.
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Figure 6. The effect of travel speed on normalized diameter (optical/manual ).
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Figure 7. The effect of air setting on normalized counts. Air settings: 1) no leaves and zero air, 2) zero air, 3) low air velocity, 4) intermediate air velocity, and 5) high air velocity.
Plant Spacing Estimation A variable, normalized plant spacing, was calculated to compare sensor performance to actual field measurements of plant spacings. Normalized spacings were the optical sensor spacings divided by the manual plant spacings for each row of each plot. The average normalized spacings for all rows and treatments were 1.084 for the optical sensors (data not presented). The optimum sensor performance would correspond to a normalized spacing value of one. This variable is discussed in detail in Lobdell (2001). CORN POPULATION MAPPING The mechanical population sensor and a yield monitor were used to simultaneously map corn population and yield during harvest in Missouri (Figure 8). In addition, mechanical population sensors have been placed on research and cooperator’s combines at other locations to map corn populations during harvest. In preliminary plot tests, when population data was included in the limiting factor analysis (stepwise regression), it accounted for more of the yield variance than all soil nutrient factors combined (Figure 9). The sensor population information could also be used to accurately predict yield monitor readings based on the assumption that the total grain entering the combine was number of plants harvested during each second interval multiplied by the mean yield per plant (Figure 10). These results emphasize the significance of population on the yield potential and final crop yield.
Population (plants/acre) 15000 16000 17000 18000 19000 20000 21000 22000 23000 36540
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Figure 8. A typical illustration of a corn population map obtained with the mechanical sensor, compared with the yield map (simultaneous data collection).
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CONCLUSIONS Mechanical corn population sensor performance was encouraging under most conditions, although actual population was underestimated at high populations. When compared to hand counts obtained at harvest under all operating conditions, the sensor underestimated population on average by 4.4% (r2=0.93) with a standard error of 1550 plants/ac. However, when operating in test blocks without weak plants (and/or doubles) at speeds less than 5.6 mph, the average underestimation was reduced to 0.08% (r2=0.96) with a standard error of 1100 plants/ac. This represents an error of less than two plants in a 30 ft transect. The underestimation and standard error of the predicted population was directly related to the stalk feed rate into the sensor. When the feedrate was restricted to less than 9 plants/s, the standard error was 729 plants/ac. This threshold would represent a speed of 4.5 mph at a population of 24,300 plants/ac. Mechanical sensor accuracy was very good at low plant populations where there is a significant possibility of substantial yield reductions due to population. Underestimation of high populations may not be critical provided it is possible to determine that the actual population is above an agronomic threshold. The corn population sensor was able to provide accurate data to map spatial trends in plant population and the effect of this variation on crop yields. The optical through-beam sensor system can successfully estimate corn population at harvest. The tendencies to underestimate populations, and overestimate plant diameter with increasing travel speed can be accommodated through proper calibration. Additional tests are underway to evaluate the sensor’s capabilities at other growth stages. The air-jet system significantly affected the number of plants counted by the sensor, with the highest counts recorded with the intermediate and high air settings. Further improvements in corn population estimation may be possible by optimizing the size, shape, and location of the air jets. REFERENCES Banner Engineering Corp. 1994. Handbook of Photoelectric Sensing. 2nd Ed. 9714 Tenth Ave. North, Minneapolis, MN. Birrell, S. J. and K. A. Sudduth. 1995. Corn population sensor for precision farming. ASAE Paper No. 95-1334. St. Joseph, MI: ASAE. Chaney, M. M. 1997. Leaf deflection fingers for real-time corn population sensor. Unpublished research. University of Illinois at Urbana-Champaign. Urbana, IL. Drummond, S. T. 2001. Personal communication. USDA-ARS Cropping Systems & Water Quality Research Unit. Columbia, MO. Easton, D. 1996. Corn Population and Plant Spacing Variability: The next mapping layer. p. 723727. In P.C. Robert, R. H. Rust, and W. E. Larson (ed.), Proc. 3rd Intl. Conf. on Precision Agriculture. ASA, CSSA, and SSSA, Madison, WI.
Fee, R. 1994. Precision planting time trials. Successful Farming 92(March):34-37. Gore, L. M. 1996. Report: Stalk counter for VRT study fall of 1995. Deere & Co. Moline, IL. Nave, W. R., J. W. Hummel, and R. R. Yoerger. 1977. Air-jet and row-crop headers for soybeans. Transactions of the ASAE 20(6):1037-1041, 1044. Nielsen, R. L. 1995. Planting speed effects on stand establishment and grain yield of corn. Journal of Production Agriculture 8(3):391-393. Plattner, C. E. and J. W. Hummel. 1996. Corn plant population sensor for precision agriculture. p. 785794. In P. C. Robert, R. H. Rust, and W. E. Larson (ed.), Proc. 3rd Intl. Conf. on Precision Agriculture. ASA, CSSA, and SSSA, Madison, WI. Sudduth, K. A., S. J. Birrell, and M. J. Krumpelman. 2000. Field evaluation of a corn population sensor. In P. C. Robert, R. H. Rust, and W. E. Larson (ed.), Proc. 5th Intl. Conf. on Precision Agriculture. ASA, CSSA, and SSSA, Madison, WI.
