PRECISION AGRICULTURE APPLICATIONS OF AN ON-THE-GO SOIL REFLECTANCE SENSOR ABSTRACT

Transcription

1 PRECISION AGRICULTURE APPLICATIONS OF AN ON-THE-GO SOIL REFLECTANCE SENSOR C.D. Christy, P. Drummond, E. Lund Veris Technologies Salina, Kansas ABSTRACT This work demonstrates the utility of an on-the-go optical reflectance sensor in mapping soil attributes in central Kansas (USA). The sensor measures the reflectance of the soil at wavelengths ranging from 950nm to 1650nm and at a depth of approximately 70 mm below the soil surface. The sensor was used to map eight fields on approximately 20 meter transects. Once each field was mapped, the reflectance data was compressed using principal components analysis (PCA) and then clustered using a fuzzy c-means algorithm. A fuzzy logic algorithm was used to determine representative sample locations within each cluster and samples were acquired for laboratory analysis. Once this process was completed for all eight fields, calibrations for various soil attributes were created using partial least squares regression (PLS). Validation techniques indicated that the calibrations provided reliable prediction of organic matter, ph buffering capacity, and Mehlich 1 phosphorus. Less accurate, but potentially usable calibrations were obtained for ph and Mechlich 3 phosphorus. These calibrations were then applied to the complete set of field spectra in order to create soil attribute maps. In turn, these data layers were used to create maps to control the rate of applied lime and fertilizer. Keywords: near infrared (NIR), organic matter, buffer capacity, phosphorus INTRODUCTION Research in precision agriculture has shown the high degree of spatial variability and the need for on-the-go soil sensors to quantify the variability in a cost effective manner (Adamchuk, 2004). The spectrophotometer presented here is one such sensor and may have utility in precision agriculture. The system measures diffuse reflectance in the near infrared spectral region. A subset of the spectra are matched with the results of laboratory analyses and used to create a calibration using multivariate statistical techniques. This paper presents some results of its usage on eight fields in Central Kansas. Usage of the system and creation of calibrations are briefly discussed, but the emphasis is placed on showing some results in the form of soil attribute maps and proposing potential applications of the information in a precision agriculture context. In this particular example from Central Kansas, the maps may be useful for adjusting nitrogen application based on organic matter; accounting for buffer capacity in

2 lime applications; and adjusting phosphorus application based on existing reserves. Several researchers have presented spectrophotometers for on-the-go application (Shibusawa, 2001; Shonk, 1991; Sudduth, 1993). The system presented herein is unique in that it makes reflectance measurements through a sapphire window mounted on the bottom of a shank. The window is pressed directly against the soil so that an air gap is avoided. Consequently, the measurements are not adversely affected by airborne dust. Furthermore, movement of the soil against the window produces a self-cleaning effect so that splatter from water or mud is inherently removed. MATERIALS AND METHODS The spectrophotometer used in this work was built into a shank (Figure 1), mounted on a toolbar, and pulled behind a tractor. The device makes measurements through a sapphire window mounted on the bottom of the shank. The device uses a tungsten halogen bulb to illuminate the soil and an optic to direct reflected light into a fiber optic for transmission to the spectrometer. Shutters in the shank (not shown) are manipulated to acquire the dark and reference spectra approximately every 3 to 5 minutes. The shank is lowered into the ground to approximately 7 cm and pulled through the soil at approximately 6 km/hr. Approximately 20 spectra per second are acquired from the spectrometer and immediately transferred through a universal serial bus (USB) connection to a personal computer for storage. An acquisition program and all subsequent data processing programs were written in G using Labview (National Instruments, Austin, Texas, USA). An InGaAs photodiode-array spectrometer (Model NIR-128L-1.7-USB, Control Development, Inc., South Bend, Indiana, USA) was used at the receiving end of the fiber optic. The spectrometer has a specified range of 920 to 1718 nm and a resolution of 6.35 nm. The device uses a grating to separate light according to wavelength and project the light onto a 128 element standard InGaAs detector. The integration time was seconds. Upon completion of mapping a field, the spectra were converted to absorbance (Naes, 2001) and compressed using principal components analysis (PCA) and outlying spectra were removed. Subsequently, the spectra were divided into 15 clusters based upon the first 3 principal components (PC's) using a fuzzy c-means algorithm. Within each cluster, a location with minimal spatial variability was chosen as a soil sampling location. This procedure produced a set of locations within the field that was representative of the overall spectral variation (Naes, 1987). The spectrum from each sample location was matched with laboratory analysis to create a database for creating calibrations (N=119). Partial least squares regression (PLS) was used in conjunction with two different cross validation techniques; leave-one-out and leave-1/8-out. In leave-one-out cross validation, each sample is omitted and predicted using a calibration made from the remaining samples. Leave-1/8-out is identical except that 1/8 of the data set was omitted at a time to create a more stringent test. Calibrations were created using 6 different

3 Fig. 1. The shank-based spectrophotometer used to obtain NIR reflectance spectra. 1: sapphire window; 2: halogen lamp; 3: collection optic; 4: fiber optic; 5: spectrometer; 6: power supply. spectral pre-treatments, including first and second derivatives in combination with the standard normal variate (SNV). Pretreatments that performed the best in terms of cross validation error were selected for usage in creating field maps. The field maps are created by simply applying the selected calibration to the whole set of field spectra. This presentation will concentrate on calibrations for four attributes that are pertinent in precision agriculture; organic matter (OM), ph, buffer capacity, and phosphorus. Buffer capacity was quantified in terms of "delta ph", which is the difference between the water ph and the ph obtained after the addition of the SMP buffer. Soils that are highly buffered change the least when the buffer is added and therefore have a low "delta ph". In other words, "delta ph" is inversely proportional to buffer capacity. RESULTS AND DISCUSSION Figures 2 and 3 display the results of the leave one-eighth out cross validation in terms of correlation (R 2 ) and error (RPD) respectively. (RPD is simply a ratio of the standard deviation of the population to the prediction error.) As the graphs indicate, the best predictive performance was obtained for organic matter (OM), delta ph, and Mechlich 1 Phosphorus. The field application of each of these attributes in discussed in more detail below.

4 Coefficient of Determination (R 2 ) OM ph delta ph P (M3) P (M1) Soil Property Fig. 2. Correlation results of leave-one-eighth out cross validation RPD OM ph delta ph P (M3) P (M1) Soil Property Fig. 3. Prediction error of leave-one-eighth out cross validation in terms of RPD. Soil Organic Matter Adjustments to Nitrogen Applications In determining the appropriate application rates for nitrogen, adjustments are often recommended for organic matter (OM). In Kansas, the most basic recommendation is based upon yield goal and organic matter (Kansas State University, Department of Agronomy, publication MF-2586). The rate is increased as the yield goal is increased and decreased for soils with higher OM. For wheat, the credit for OM is 10 lbs per percentage point. Consequently, once OM is measured with the spectrophotometer, calculation of the nitrogen credit is straightforward. This is illustrated in Figure 4, where the OM measurement is shown in the graph on the left and the nitrogen credit is shown in the graph on the right. In the left graph, the data was not interpolated in order to show the exact transects traversed by the spectrophotometer. In the graph on the right, the data was interpolated using Kriging with a 30 meter search radius in order to create a map that could be used for nitrogen application.

5 Fig. 4. Organic matter and nitrogen adjustment maps for field 5. There are two potential ways the nitrogen adjustment could be used. On the one hand, a single yield goal could be used for the field so that the nitrogen application rate would be a fixed rate less the credit for organic matter. On the other hand, the yield goal could be determined on a site-specific basis using yield monitor data. This would seem more appropriate since there could be a general tendency for elevated organic matter to increase yield and yield goals. A second example of organic matter measurement is shown in Figure 5 where the organic matter and nitrogen adjustment maps are shown for a 40-hectare field.

6 Fig 5. Organic matter and nitrogen adjustment maps for Field 6. Buffer Capacity and Lime Application As shown in Figures 2 and 3, spectrophotometer predictions for buffer capacity were relatively accurate (R2=0.74) but predictions for ph were only moderately accurate (R2=0.58). The two quantities can be used together to determine buffer ph, which is the basis of the lime recommendation. The calculation is as follows: Buffer ph = ph + delta ph As seen above, the two separate quantities contribute equally to the overall error in lime recommendation. Usage of spectrophotometer measurements of ph and

7 Fig. 6. The ph measurement (a) and the buffer capacity measurement (b) are used together to calculate buffer ph and then derive a lime recommendation (c). buffer ph are shown in Figure 6. This method would only be workable if both quantities are predicted with adequate accuracy. On the other hand, if the spectrophotometer prediction of ph is not of sufficient accuracy, the ph layer would have to be obtained by other means. Adjustments to Phosphorus Application The results obtained for phosphorus prediction with the spectrophotometer are quite interesting in that the accuracy was high for the Mechlich 1 extraction but moderate for the Mechlich 3 extraction. Unfortunately, the Mechlich 3 extraction is the one typically used for fertilizer recommendations in Central Kansas. Figure 7 displays maps of both extractions for Field 2. The maps have striking similarity, but there is significant contrast between the two on the north edge of the field (top). In this example, the field has a history of manure application so both extractions show relatively high levels. (Note: the Mechlich 1 results are in lbs per acre, while the Mechlich 3 results are in ppm). Should these maps be deemed to be sufficiently accurate, they could be used to determine the appropriate levels of phosphorus application. In this case, the existing levels are high and phosphorus application could be reduced or eliminated for most of the field.

8 Fig. 7. Maps of two different phosphorus extractions. Mechlich 1 phosphorus (a) is accurately predicted but the prediction for Mechlich 3 phosphorus (b) is only moderately accurate. SUMMARY AND CONCLUSIONS The spectrophotometer presented in this paper produced quantitative measurements with high accuracy for OM, ph buffer capacity, and Mechlich 1 phosphorus. The measurements were moderately accurate for ph and Mechlich 3 phosphorus. As the examples demonstrated, these measurements could be used to apply nitrogen, lime, and phosphorus on a site-specific basis. The potential applications presented also bring up questions for future research. For example, what level of accuracy is required on ph in order to produce lime recommendations that are agronomically viable? Similarly, what level of accuracy is needed in order to use the results to adjust levels of phosphorus application? These questions were not addressed in this paper, but can be answered by a more extensive statistical analysis of the data and through subsequent experiments. Finally, experiments in other geographic areas will be needed to determine whether these results would be applicable to a specific region.

9 REFERENCES Adamchuk, V.I., Hummel, J.W., Morgan, M.T., Upadyaya, S.K., On-thego sensors for precision agriculture. Computers and Electronics in Agriculture 44, Naes, T., The design of calibration in near infrared reflectance analysis by clustering. Journal of Chemometrics, 1, Naes, T. Isaksson, T. Fearn, and T. Davies,2002. A User-Friendly Guide to Multivariate Calibration and Classification. Chichester, UK: NIR Publications. Shibusawa, S., Imade Anom, S.W., Sato, S., Sasao, A., Hirako, S., Soil mapping using the real-time soil spectrophotometer. In: Grenier, G., Blackmore, S. (Eds.), ECPA 2001, Third Europeon Conference on Precision Agriculture, vol. 1. Agro Montpellier, pp Shonk, J.L., Gaultney, L.D., Schulze, D.G., Van Scoyoc, G.E., Spectroscopic sensing of soil organic matter content. Transactions of the ASAE 34, Sudduth, K.A., Hummel, J.W., Portable, near-infrared spectrophotometer for rapid soil analysis. Transactions of the ASAE, vol. 36, pp Sudduth, K.A., Hummel, J.W., Soil organic matter, CEC, and moisture sensing with a portable NIR spectrophotometer. Transactions of the ASAE, vol 36, pp