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

Abstract

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 database 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 & B. Tisseyre recommendation is to consider a two-step sampling method where BuN is randomly sampled and BW is sampled according to the NDVI values.

Comparing efficiency of different sampling schemes to estimate yield and quality parameters in fruit orchards

“…Compared to the other sampling schemes, SRS (scheme A) obtained the greatest inaccuracy in estimating the yield (almost 10%). However, this value could be considered as acceptable given the criterion adopted by other researchers (Carrillo et al, 2016). When stratifying the samples using the NDVI or the ECa, the sample means worked even better achieving very good accuracy values below 2% (Table 1).…”

“…As NDVI is related to vine vigour, the method is a way of distributing sampling points by covering the areas of different vigour to capture vineyard canopy variability within the plot. This same idea is behind the method proposed by Carrillo et al (2016) to improve yield estimates, also in viticulture. The authors concluded with the need to consider a two-step sampling method combining NDVI-based sampling with random vine sampling to apply each strategy to predict a specific component of the productive potential of the vineyard.…”

Potential of Sentinel-2 satellite images to monitor vine fields grown at a territorial scale

“…It must be clear that the objective of this technical note is not to provide an explanation for the observed NDVI spatial variability. Much work has already been dedicated to this issue in the literature (Acevedo-Opazo et al, 2008;Carrillo et al, 2016;Hall et al, 2002;Johnson et al, 2001). The objective is rather to discuss what seems to be observable and provide areas for further investigation.…”

“…Multiple studies have shown the interest of remote sensing images for field or within-field management purposes in viticulture such as irrigation or fertilization monitoring (Acevedo-Opazo et al, 2008;Carrillo et al, 2016;Hall et al, 2002;Johnson et al, 2001). However, current limitations of most remote sensing acquisition platforms are their low temporal resolution (16 days for the LandSat8 platform) and their cost ($1.65/km² for Spot 6-7 new image acquisition with a minimum order of 500km² or $1.28/km² for RapidEye new image acquisition with a minimum order of 3500km²).…”

Sentinel-2 Validation for Spatial Variability Assessment in Overhead Trellis System Viticulture Versus UAV and Agronomic Data

“…It is undeniable that the factor that has exponentially encouraged the spread of UAV application in agriculture is the continuous advance in sensor technologies, providing higher resolution, lower weight and dimensions, and cost reduction [23,[25][26][27][28]. Several authors describe a wide range of UAV applications for PV purposes: vigor and biomass [29][30][31][32][33][34], yield and quality monitoring [35,36], water stress [37][38][39][40][41], canopy management [42], diseases [43][44][45][46], weeds [47][48][49], and missing plants [50][51][52][53].…”