Geospatial Modeling of Wine Grape Quality (Anthocyanin) for Optimum Sampling Strategy in Mechanized On-The-Go Differential Harvesting Programs

Geospatial Modeling of Wine Grape Quality

(Anthocyanin) for Optimum Sampling

Strategy in Mechanized On-The-Go

Differential Harvesting Programs

Balaji Sethuramasamyraja

Department of Industrial Technology, California State University, Fresno 2255 E Barstow Ave, M/S IT 09, Fresno, CA 93740-8002, USA

balajis@csufresno.edu Harvinder Singh

Department of Industrial Technology, California State University, Fresno 2255 E Barstow Ave, M/S IT 09, Fresno, CA 93740-8002, USA

George Mathew Mazhuvancheriparambath

Department of Industrial Technology, California State University, Fresno 2255 E Barstow Ave, M/S IT 09, Fresno, CA 93740-8002, USA

Abstract:

Site-specific harvest of wine grapes based on quality and segregation before delivery to winery is a profitable cultural practice in vineyard management. Wine grape segregation based on quality parameters like anthocyanin to delineate quality zones in vineyards aids differential harvest. However, capturing vineyard variability with optimal sampling strategies is essential for economical feasibility of differential harvest. In this study, anthocyanin (mg/g fruit) data was collected on two production vineyards for geo-statistical analysis of spatial variability and determination of optimum samples/acre (SPA) for differential harvesting programs. Geo-referenced field samples of wine grapes were measured for anthocyanin using near-infrared sensors (NIR) in two vineyards of San Joaquin Valley California (Twin Creeks & Merjan). Two strategies of sampling, strategy I & II were utilized for 3, 5, 7 and 10 (reference) SPA. While strategy I selected 3, 5 or 7 random SPA in whole vineyard, strategy II did the same from every 1 acre block of the vineyard. Geo-spatial interpolations using ordinary kriging prediction of anthocyanin evaluated through cross validation parameters resulted in determining applicability of strategies in capturing vineyard field variability for differential harvesting. Strategy II outperformed strategy I in predictions with 5 and 7 SPA predicting vineyard spatial variability.

Keywords: precision agriculture, precision viticulture, sampling strategy, anthocyanin, wine grapes, geo-statistics, interpolation.

Bramley and Hamilton (2004), Bramley (2005a) and Bramley et al. (2005b) [1, 3] reported spatial variability in both wine grape yield and quality over three years. The variability in yield showed spatial consistency within a vineyard, but the range in yield was significantly different temporally that could be associated with high temperatures during the bloom period (Bramley and Hamilton, 2004). The tendency towards spatial consistency is encouraging with respect to understanding the source of this variability and hence to develop more precise management practices to improve vineyard uniformity.

The spatial variability in quality (Bramley, 2005a) [1] was not as consistent as yield suggesting factors controlling quality are complex. Vineyard management practices like variable fertilizer applications, foliar nutrient programs and drip irrigation could help to minimize variability in vine growth as well as fruit quality. However, there are other factors such as slope, aspect, pests and disease that contribute to complexity of quality. To help overcome this problem, it has always been desirable to practice differential harvests (temporal and spatial) which is cost prohibitive in production settings. Bramley (2005a) [1] suggested the need for development of on-the-go wine grape quality sensing technology that could enable quality zone delineation for effective harvest and management of vineyards. In Australia, utilization of precision viticulture management strategy and accompanied geo-spatial tools (Bramley and Proffitt, 1999; Bramley and Lamb, 2003) [4, 5] has benefited the producers.

Sethuramasamyraja et al. (2007) [10] developed a differential harvesting system onboard a commercial wine grape harvester that works on the basis of a pre-generated quality map from optical near infra red sensors delineating two quality zones, demonstrating differential harvest of wine grapes from the same vineyard block on-the-go in two seasons (2006-2007). However, the two zone quality maps created were based on high density spectral dataset created by extensive field sampling. Such sampling density may not be economically feasible in production settings, thereby, demanding the need for predicting the spatial variability of wine grapes with the least sampling density. The objective of this research is to develop an optimum sampling protocol for determination of the number of sampling sites per acre (SPA) required for the wine grape quality parameter, anthocyanin content, in order to facilitate feasible data collection while quantifying spatial variability for potential differential harvesting programs.

2. Materials and Methods

Experimental Materials

Site Location: Vineyard

Two different vineyards were utilized, in two different seasons, for the expreriments. The vineyards were Twin Creeks Vineyard (2006) located in Lodi, California and Merjan Vineyard (2007) located in Madera, California (Figure 1). Cabernet sauvignon was the wine grape varietal under study in both vineyards. Table 1 lists site specification of vineyards blocks.

Vineyards Acreage, Vines/ac.

Rows, ~Vines/row

Row Heading, Row & Vine

spacing Sampled Vines Vines in Block Samples/ acre (SPA) Twin Creeks (Lodi, CA)

45, 681 157, 196 E-W,

8 ft. & 8 ft.

437 30,645 9.7 Merjan

(Madera, CA)

160, 581 135, 335 N-S

10 ft. & 7.5 ft.

1330 92,960 8.3 Merjan South

(Madera, CA)

78, 581 135, 335 N-S

10 ft.& 7.5 ft.

698 45,318 8.9

system to achieve 8 inch (20 cm) accuracy for precision mapping and geographic information system (GIS) data collection. The Trimble TerraSync™ (Trimble Navigation Limited, Sunnyvale, CA) is mobile computing software for spatial data collection and data maintenance. Trimble GPS Pathfinder Office Software Ver. 4.0 (Trimble Navigation Limited, Sunnyvale, CA) was utilized for post processing including data validation and conversion to Environmental Systems Research Institute, ESRI ArcGIS native shape file format. ArcGIS 9 desktop software and geo-statistical extension (ESRI, Redlands, CA) was used for geo-statistical analysis.

Vineyard Sensors GPS Software

Twin Creeks Luminar 5030 Miniature “Hand-held” NIR Analyzer1

Trimble GeoXTTM handheldGPS Receiver4

ESRI ArcGIS 9.1, 9.2 Extension: GeoSpatial Analyst3 Trimble GPS Pathfinder Office

Ver. 4.04 Merjan Zeiss CORONA VIS/NIR 1.7

Spectrometer2

Trimble GeoXHTM hand held GPS Receiver4 1

Brimrose Corporation, Baltimore, MD 2

Zeiss Microimaging, Thronwood, NY 3

Environmental Systems Research Institute, ESRI, Redlands, CA 4

Trimble Navigation Limited, Sunnyvale, CA

Table 2 - Sensors, Instrumentation and Software utilized in experiments

Luminar 5030 Miniature "Hand held" Near Infrared (NIR) Analyzer (Brimrose Corporation of America, Baltimore, Maryland) was utilized for the quantification of wine grape brix and anthocyanin (mg/g of fruit) in Twin Creeks vineyard (2006). The spectral range used for the data collection was 1100 to 2300 nm in transmission mode, with a wavelength increment of 2 nm. Calibration for the NIR spectrometer was based on reference wet chemistry analysis using standard laboratory techniques. Zeiss Corona 45 VISNIR 1.7 spectrophotometer (Carl Zeiss Inc., Peabody, Massachuseets) was utilized to acquire anthocyanin (mg/g of fruit) values in the VIS/NIR range (400 nm to 1680 nm) at Merjan in 2007. Winegrape fruit samples were homogenized 45 s prior to spectral analysis resulting in 167 scans averaged as anthocyanin indicator per sample. The system was calibrated using reference method for grape color using Iland method (Iland, 1993) and twenty percent of the samples were correlated with a standard reference for winegrape color.

Experimental Methods

Wine Grape Sampling

In 2006 season, at Twin Creeks vineyard, 437 vines were sampled out of 30,645 resulting in sampling rate of 9.7 samples per acre. For each sample vine, two representative clusters were visually selected with five berries/cluster used for anthocyanin and brix data collection using optical spectrometer for geo-statistical analysis. In 2007 season, at Merjan vineyard, 1330 vines were sampled out of 92,960 resulting in sampling rate of 8.3 samples per acre. In south block alone, 698 vines were sampled out of 45,318 resulting in sampling rate of 8.9 samples per acre. For each sample vine, ten clusters were collected with five from upper canopy and the rest from lower canopy and then five berries/cluster used for anthocyanin and brix data collection using optical spectrometer for geo-statistical analysis (Singh et al. 2008).

Geo-statistical analysis

The wine grape quality parameter spatial dependence was determined by semi-variograms calculated using (1), where the semi-variogram value of the wine grape quality, separated by effective distance (h) is



(

)

(

)



/

2

(

)

)

(

h

N

x

Z

x

Z

h









where N(h_{) = the number of paired values Z(xi) and Z(xi+h) (Isaaks & Srivastava, 1989).}

analysis. It is the best-unbiased predictor irrespective of the normality of data and best linear unbiased estimator as the mean residual error nears zero minimizes the variance of the errors (Pannatier, 1996) [8].

Error Analysis Condition

Prediction Accuracy

Mean Prediction Error ~ 0 Mean Standardized Error <<

Uncertainty of Prediction Standard Error

Average Standard Error (ASE) = Root Mean Square Prediction Error

(RMS PE)

Case I: Overestimation If ASE > RMS PE Case II: Underestimation

If ASE < RMS PE RMS Standardized Error ~ 1

Case I: Overestimation If RMS Standardized Error < 1

Case II: Underestimation If RMS Standardized Error > 1

Table 3 - Parameters to evaluate stochastic interpolations(ordinary kriging)

Strategy I and II

For strategy I, the subset feature in ESRI geo-statistical analyst extension was used to split the dataset randomly complying with the 3, 5, and 7 SPA (Table 4). In strategy II, 3, 5 or 7 samples were randomly selected from every 1 acre block (Figure 2).

Vineyard Strategy I

(SPA: 3, 5, & 7)

Strategy II Total Sample Vines (~10 SPA reference)

Twin Creeks 135, 225 & 315 3, 5, 7 437

Merjan 480, 800 & 1120 3, 5, 7 1330

Merjan South 234, 390 & 546 3, 5, 7 698

3. Results and Discussion

The anthocyanin (mg/g of fruit) dataset was subjected to exploratory statistical (histogram, normal qq plot, trend analysis, prediction error) analysis. Spatial analysis of dataset in ArcGIS geo-statistical extension for stochastic analysis requires normality condition for error analysis. Table 5 and 6 lists the statistical parameters that were used to check the normality condition.

SPA (Samples/ac)

Count Min Max Mean σ 1 2 Q1 Median Q3

Twin

Cree

ks 3 135 _{5 222} 0.64 _0.64 1.01_1.06 0.87_{0.88 0.077}0.079 -0.57_-0.49 2.59_{2.77 0.83}0.82 _0.89 0.88 0.93_0.94

7 314 0.64 1.06 0.87 0.077 -0.55 2.85 0.83 0.88 0.93

10 437 0.64 1.06 0.88 0.076 -0.50 2.78 0.83 0.88 0.93

Merj

an

3 478 0.46 1.47 0.94 0.169 0.11 2.93 0.82 0.94 1.05

5 797 0.37 1.49 0.94 0.174 0.10 3.03 0.83 0.94 1.06

7 1116 0.37 1.49 0.93 0.175 0.12 2.98 0.82 0.93 1.05

10 1329 0.37 1.49 0.93 0.176 0.11 2.98 0.81 0.93 1.05

Merj

.S.

3 230 0.41 1.44 1.05 0.161 -0.39 3.79 0.95 1.04 1.17

5 383 0.41 1.5 1.05 0.165 -0.37 3.74 0.94 1.05 1.16

7 544 0.5 1.48 1.05 0.159 -0.28 3.26 0.95 1.06 1.16

10 698 0.41 1.5 1.05 0.163 -0.30 3.31 0.94 1.06 1.16

σ – Standard Deviation, 1 – Skewness, 2 – Kurtosis, Q1 – 1st Quartile, and Q3 – 3rd Quartile Table 5 - Strategy I Histogram Parameters (Anthocyanin mg/g fruit.)

SPA (Samples/ac)

Count Min Max Mean σ 1 2 Q1 Median Q3

Twin

Cree

ks 3 135 _{5 226} 0.64 _0.64 1.01_{1.01 0.88}0.89 0.072_0.074 -0.62_{-0.64 3.20}3.34 _{0.84 0.89} 0.84 0.89 0.94_0.94

7 315 0.64 1.01 0.88 0.074 -0.50 2.77 0.83 0.88 0.93

10 437 0.64 1.06 0.08 0.076 -0.50 2.78 0.83 0.88 0.93

Merj

an 3 456 _{5 761} 0.46 _0.43 1.49_{1.49 0.93}0.93 0.183_0.181 0.21_{0.12 2.91}2.83 _{0.81 0.93} 0.81 0.94 1.06_1.06

7 1065 0.43 1.49 0.93 0.178 0.11 2.95 0.81 0.93 1.05

10 1329 0.37 1.49 0.93 0.176 0.11 2.98 0.81 0.93 1.05

Merj

.S. 3 242 _{5 398} 0.50 _{0.50 1.42}1.42 1.05_1.05 0.159_0.158 -0.37_{-0.46 3.27}3.29 _{0.95 1.05} 0.94 1.06 1.18_1.15

7 551 0.41 1.50 1.04 0.159 -0.35 3.39 0.95 1.06 1.58

10 698 0.41 1.50 1.05 0.163 -0.30 3.31 0.94 1.06 1.16

σ – Standard Deviation, 1 – Skewness, 2– Kurtosis, Q1– 1 st

Quartile, Q3– 3 rd

Quartile

Strategy I

Table 7 lists the results of strategy I ordinary kriging cross validation parameters using 3, 5, 7, and 10 SPA. The standard for performance comparison of 3, 5 and 7 is 10 SPA.

Blocks ~SPA (Samples/ac) Mean Pred. Error RMS Pred. Error Average Standard Error Mean Standardized Error RMS Standardized Error Twin Creeks

3* 0.00250 0.07514 0.07579 0.03029 0.99150

5** 0.00133 0.07503 0.07231 0.01657 1.03700

7* 0.00199 0.07252 0.07378 0.02590 0.98290

10* 0.00170 0.06920 0.07320 0.02310 0.94460

Merjan 3* 0.00250 0.16090 0.16720 0.01461 0.96280

5* 0.00146 0.15680 0.17080 0.00821 0.91770

7* 0.00108 0.15420 0.17100 0.00611 0.90190

10* 0.00180 0.15400 0.17160 0.01000 0.89730

Merjan South

3* 0.00153 0.15190 0.15740 0.00879 0.96510

5* 0.00332 0.15600 0.15850 0.02058 0.98250

7* 0.00137 0.14490 0.14810 0.00884 0.97670

10* 0.00160 0.14810 0.15370 0.01030 0.96290

* – Overestimation, ** – Underestimation (nominal)

Table 7. Strategy I Anthocyanin (mg/g fruit) Cross Validation Parameters

Figures 3, 4 and 5 list the ordinary kriging interpolations for Twin Creeks, Merjan and Merjan South using strategy I. In Twin Creeks vineyard, 3 SPA (Figure 3 a), over estimated due to the sparse nature of the dataset resulting in localization. In 5 SPA (Figure 3 b), the mid interval 0.8 – 0.85 and the higher interval 0.85 – 0.9 are underestimating, corroborated by RMS Standardized error > 1 (1.037) and Average Standard Error < RMS Prediction Error (0.07231 < 0.07503). In 7 SPA (Figure 3 c), the lower interval 0.7 – 0.8 is moderately over estimating staying close to the reference (Figure 3 d). Except 5 SPA, rest of the SPAs underestimates anthocyanin with RMS Standardized error < 1 (0.99, 0.98, 0.94 for 3, 7 & 10 respectively) and Average Standard Error > RMS Prediction Error (Table 7).

Strategy II

Table 8 lists the results of strategy II ordinary kriging cross validation parameters using sampling densities 3, 5, 7, and 10 (standard for performance comparison) SPA.

Blocks ~SPA (Samples/ac) Mean Error RMS Error Average Standard Error Mean Standardized Error RMS Standardized Error Twin Creeks

3** 0.00180 0.07290 0.06990 0.02670 1.04200

5** 0.00130 0.07400 0.07290 0.01620 1.01600

7* 0.00090 0.07150 0.07270 0.01230 0.98390

10* 0.00170 0.06920 0.07320 0.02310 0.94460

Merjan 3* 0.00250 0.16900 0.18270 0.01340 0.92510

5* 0.00220 0.15990 0.17670 0.01230 0.90520

7* 0.00170 0.15770 0.17400 0.00950 0.90660

10* 0.00180 0.15400 0.17160 0.01000 0.89730

Merjan South

3* 0.00130 0.14390 0.14410 0.00840 0.99810

5* 0.00220 0.14330 0.14480 0.01460 0.98880

7* 0.00170 0.14830 0.15130 0.01070 0.97900

10* 0.00160 0.14810 0.15370 0.01030 0.96290

* – Overestimation, ** – Underestimation (nominal)

Table 8. Strategy II Cross Validation Parameters (Anthocyanin mg/g fruit)

Figures 6, 7 and 8 list the ordinary kriging interpolations for Twin Creeks, Merjan and Merjan South using strategy II. Strategy II, predictions are significantly better than strategy I for 3, 5 and 7 SPA for all vineyards. The difference is that 3 SPA (Figure 6b) and 5 SPA (Figure 6 c) were underestimating for Twin Creeks as compared to the 7 SPA (Figure 6 d) that is over estimating. Similar to strategy I, strategy II 3, 5 and 7 SPA predictions are all over estimating consistently for Merjan and Merjan South Vineyards. 3 SPA (Figure 7 b) is again the least good predictor of variability as compared to the 5 and 7 SPA that could be attributed to micro variability of anthocyanin across the vineyard. In comparison, 5 SPA and 7 SPA equally capture the variability of the vineyard and would work satisfactorily for two quality fruit zones in differential harvesting. However, if any more than two quality zones need to be harvested, then 7 SPA is appropriate.

Figure 8 - Strategy II – Merjan Vineyard South Block - Anthocyanin interpolated maps – ordinary kriging a) 3 SPA b) 5 SPA c) 7 SPA and d) 10 SPA (reference)

4. Conclusions

Geospatial analysis of the wine grape quality parameter, anthocyanin in Twin Creeks and Merjan vineyards in San Joaquin Valley California region evaluated the potential of optimum field sampling in differential harvesting. An average of 3, 5, and 7 SPA anthocyanin predictions using geo-statistical ordinary kriging analysis were compared against reference of ~10 SPA (9.7, 8.3, and 8.9 for Twin Creeks, Merjan and Merjan South block vineyards, respectively) to capture vineyard spatial variability. Two strategies were pursued for geospatial analysis, strategy I based on random sampling of sampling sites in a vineyard block amounting to 3, 5, and 7 SPA while strategy II is based on random sampling of 3, 5 and 7 SPA from each 1 acre block segments in the vineyards.

Wines US. Jim Orvis and Oren Kaye (Constellation Wines US - Mission Bell Winery, Madera, CA), John Gonsalves (Constellation Wines US – Mission Bell Twin Creek Vineyard, Lodi, CA), Greg T. Berg (OXBO International Corporation, Kingsburg, CA), Antonio Odair Santos (IAC – Instituto Agronomico, Sao Paulo, Brazil), Robert Cochran, Robert Wample (Viticulture and Enology Research Center, California State University, Fresno, CA), and Jorge Rodriguez and Sivakumar Sachidhanantham(Department of Industrial Technology, California State University, Fresno, CA), and Jeff Bentley (AgLeader Technologies, Ames, IA) are acknowledged for their contributions. References

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