Use of multi-spectral airborne imagery to improve yield sampling in viticulture

The wine industry needs to know the yield of each vine field precisely to optimize quality management and limit the costs of harvest operations. Yield estimation is usually based on random vine sampling. The resulting estimations are often not precise enough because of the high variability within vineyard fields. The aim of the work was to study the relevance of using NDVI-based sampling strategies to improve estimation of mean field yield. The study was conducted in nine non-irrigated vine fields located in southern France. For each field, NDVI was derived from multi-spectral airborne images. The variables which define the yield: [berry weight at harvest (BWh), bunch number per vine (BuN) and berry number per bunch (BN)] were measured on a regular grid. This data-base allowed for five different sampling schemes to be tested. These sampling methods were mainly based on a stratification of NDVI values, they differed in the way as to whether NDVI was used as ancillary information to design a sampling strategy for BuN, BN, BW or for all yield variables together. Results showed a significant linear relationship between NDVI and BW, indicating the interest of using NDVI information to optimize sampling for this parameter. However this result is mitigated by the low incidence of BW in the yield variance (4 %) within the field. Other yield components, BuN and BN explain a higher percentage of yield variance (60 and 11 % respectively) but did not show any clear relationship with NDVI. A large difference was observed between fields, which justifies testing the optimized sampling methods on all of them and for all yield variables. On average, sampling methods based on NDVI systematically improved vine field yield estimates by at least 5–7 % compared to the random method. Depending on the fields, error improvement ranged from −2 to 15 %. Based on these results, the practical recommendation is to consider a two-step sampling method where BuN is randomly sampled and BW is sampled according to the NDVI values.

[1]  José Antonio Martínez Casasnovas,et al.  Viticultura de precisión: Predicción de cosecha a partir de variables del cultivo e índices de vegetación. , 2005 .

[2]  Scott M. Lesch,et al.  Sensor-directed response surface sampling designs for characterizing spatial variation in soil properties , 2005 .

[3]  P. Comtois.  The gamma distribution as the true aerobiological probability density function (PDF) , 2000 .

[4]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[5]  Sanjiv Singh,et al.  Yield estimation in vineyards by visual grape detection , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Alfred Stein,et al.  An overview of spatial sampling procedures and experimental design of spatial studies for ecosystem comparisons , 2003 .

[7]  B. Tisseyre,et al.  The potential of high spatial resolution information to define within-vineyard zones related to vine water status , 2008, Precision Agriculture.

[8]  P. Lagacherie,et al.  Is a Systematic Two-Dimensional EMI Soil Survey Always Relevant for Vineyard Production Management? A Test on Two Pedologically Contrasting Mediterranean Vineyards , 2010 .

[9]  D. Lamb,et al.  Using remote sensing to predict grape phenolics and colour at harvest in a Cabernet Sauvignon vineyard: Timing observations against vine phenology and optimising image resolution , 2008 .

[10]  Paul E. Blom,et al.  Trellis tension monitoring improves yield estimation in vineyards. , 2009 .

[11]  David W. Lamb,et al.  Within-season temporal variation in correlations between vineyard canopy and winegrape composition and yield , 2011, Precision Agriculture.

[12]  J. V. Stafford,et al.  A comparison of the spatial variability of vineyard yield in European and Australian production systems. , 2005 .

[13]  Dvoralai Wulfsohn,et al.  Multilevel systematic sampling to estimate total fruit number for yield forecasts , 2011, Precision Agriculture.

[14]  H. Ojeda,et al.  L'imagerie aérienne : application à la caractérisation des potentiels viticoles et oenologiques , 2008 .

[15]  D. P. Shrestha,et al.  Digital terrain analysis in ILWIS : lecture note , 2003 .

[16]  P. F. Scholander,et al.  Sap Pressure in Vascular Plants , 1965, Science.

[17]  Elena Gottardini,et al.  Concentration of airborne pollen of Vitisvinifera L. and yield forecast: a case study at S.Michele all'Adige, Trento, Italy , 2000 .

[18]  Supreeth Achar,et al.  A Camera and Laser System for Automatic Vine Balance Assessment , 2011 .

[19]  M. Abdel-Hamid,et al.  SPATIAL PREDICTION OF SOIL SALINITY USING ELECTROMAGNETIC INDUCTION TECHNIQUES , 2015 .

[20]  Jean-Christophe Foltête,et al.  Localising missing plants in squared-grid patterns of discontinuous crops from remotely sensed imagery , 2005, Comput. Geosci..

[21]  J. Meyers,et al.  Improving vineyard sampling efficiency via dynamic spatially explicit optimisation , 2011 .

[22]  J. V. Stafford,et al.  Precision agriculture ’13 , 2013, Wageningen Academic Publishers.

[23]  Scott M. Lesch,et al.  Spatial Prediction of Soil Salinity Using Electromagnetic Induction Techniques: 1. Statistical Prediction Models: A Comparison of Multiple Linear Regression and Cokriging , 1995 .

[24]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[25]  B. Tisseyre,et al.  Are precision agriculture tools and methods relevant at the whole-vineyard scale? , 2013, Precision Agriculture.

[26]  Bruno Tisseyre,et al.  Temporal stability of within-field patterns of NDVI in non irrigated Mediterranean vineyards , 2011 .

[27]  Jg Baldwin,et al.  The relation between weather and fruitfulness of the sultana vine , 1964 .

[28]  Salviano F. S. P. Soares,et al.  Automatic detection of bunches of grapes in natural environment from color images , 2012, J. Appl. Log..

[29]  Budiman Minasny,et al.  Proximal Soil Sensing , 2010 .

[30]  G. Rabatel,et al.  Grape berry calibration by computer vision using elliptical model fitting , 2007 .

[31]  G. Dunn,et al.  Yield prediction from digital image analysis: A technique with potential for vineyard assessments prior to harvest , 2008 .

[32]  E. Vilas,et al.  Estimating Vineyard Yields: Introduction to a Simple, Two-Step Method , 1992, American Journal of Enology and Viticulture.

[33]  H. Ojeda,et al.  Identification and significance of sources of spatial variation in grapevine water status , 2010 .

[34]  María-Paz Diago,et al.  Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions , 2012, Sensors.