Precision Viticulture

The emergence of precision agriculture technologies and an increasing demand for higher quality grape products has led to a growing interest in the practice of precision viticulture; monitoring and managing spatial variations in productivity-related variables within single vineyards. Potentially, one of the most powerful tools in precision viticulture is the use of remote sensing through its ability to rapidly provide a synoptic view of grapevine shape, size and vigour over entire vineyards. Its potential for improving viticultural practice is evident by the relationships that are known to exist between these canopy descriptors and grape quality and yield. This paper introduces the reader to remote sensing and reviews its recent, and potential, applications in viticulture. Abbreviations EM electromagnetic; GPS global positioning system; GIS geographical information system; NDVI normalised difference vegetation index

Two different spray application methods were compared in three vine varieties at different crop stages. A conventional spray application with a constant volume rate per unit ground area (l•ha-1) was compared with a variable rate application method designed to compensate electronically for measured variations in canopy dimensions. An air-blast sprayer with individual multi-nozzle spouts was fitted with three ultrasonic sensors and three electro-valves on one side, in order to modify the emitted flow rate of the nozzles according to the variability of canopy dimensions in real time. The purpose of this prototype was to precisely apply the required amount of spray liquid and avoid over dosing. On average, a 58% saving in application volume was achieved with the variable rate method, obtaining similar or even better leaf deposits.

This paper describes a Viticulture Service-Oriented Framework (VSOF) which turns around context elements or tags that are placed in the field and which can be decoded by mobile devices such as mobile phones or PDAs. The tags are used to automatically associate a field location to the relevant database tables or records and also to access contextual information or services. By pointing a mobile device to a tag, the viticulturalist may download data such as climatic data or upload information such as disease and pest incidence in a simple way, without having to provide coordinates or any other references, and without having to return to a central office. This work is part of an effort to implement a large-scale distributed cooperative network in the Douro Demarcated Region in Northeast Portugal, a region in which the effort makes particular sense due to the extremely variable topography and mesoclimates. The possibility of exchanging contextualized information and accessing contextualized services in the field, using well-known devices such as cell phones, may contribute to increase the rate of adoption of information technology in viticulture, and contribute to more efficient and closer-to-the-crops practices.

Recent studies have reported the potential of near infrared (NIR) spectral analyzers for monitoring the ripeness of grape berries alternatively to wet chemistry methods. This study covers various aspects regarding the calibration and implementation of predictive models of total soluble solids (TSS) in grape berries using laboratory and in-field collected NIR spectra.

Flavonoids are a class of bioactive compounds largely represented in grapevine and wine.
They also affect the sensory quality of fruits and vegetables, and derived products. Methods available
for flavonoid measurement are time-consuming, thus a rapid and cost-effective determination of these
compounds is an important research objective. This work tests if applying machine learning
techniques to texture analysis data allows to reach good performances for flavonoid estimation in
grape berries.
Whole berry and skin texture analysis was applied to berries from 22 red wine grape cultivars and
linked to the total flavonoid content. Three machine-learning techniques (regression tree, random
forest and gradient boosting machine) were then applied. Models reached a high accuracy both in the
external and internal validation (R² ranging from 0.75 to 0.85 for the external validation and from 0.65
to 0.75 for the internal validation, while RMSE went from 0.95 mg g-1 to 0.7 mg g-1 in the external
validation and from 1.3 mg g-1 to 1.1 mg g-1 in the internal validation).

Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine
vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB).
Normalized dierence vegetation index (NDVI) has been successfully used to assess the spatial
variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity
at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also aects
incident light and reflection, and consequently spectral response. This study analyses the eect of
protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares
several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the
indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be
the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance
between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best
estimator of LAI with linear models. Logarithmic models provided higher determination coecients,
but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship
holds for protected and unprotected canopies in initial vegetation stages, but dierent functions are
preferable once the canopy is fully developed, in particular, if tipping is performed.

Zonal management in vineyards requires the prior delineation of stable yield zones within the parcel. Among the different methodologies used for zone delineation, cluster analysis of yield data from several years is one of the possibilities cited in scientific literature. However, there exist reasonable doubts concerning the cluster algorithm to be used and the number of zones that have to be delineated within a field. In this paper two different cluster algorithms have been compared (k-means and fuzzy c-means) using the grape yield data corresponding to three successive years (2002, 2003 and 2004), for a 'Pinot Noir' vineyard parcel. Final choice of the most recommendable algorithm has been linked to obtaining a stable pattern of spatial yield distribution and to allowing for the delineation of compact and average sized areas. The general recommendation is to use reclassified maps of two clusters or yield classes (low yield zone and high yield zone) and, consequently, the site-specific vineyard management should be based on the prior delineation of just two different zones or sub-parcels. The two tested algorithms are good options for this purpose. However, the fuzzy c-means algorithm allows for a better zoning of the parcel, forming more compact areas and with more equilibrated zonal differences over time.

Aim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and variability assessment, compared to the same vegetation index derived from an unmanned aerial vehicle (UAV). Normalised difference vegetation index (NDVI) was used as reference vegetation index. Methods and Results: UAV and Sentinel-2 vegetation indices were acquired for 30 vineyard blocks located in the south of France without inter-row grass. From the UAV imagery, the vegetation index was calculated using both a mixed pixels approach (both vine and inter-row) and from pure vine-only pixels. In addition, the vine projected area data were extracted using a support vector machine algorithm for vineyard segmentation. The vegetation index was obtained from Sentinel-2 imagery obtained at approximately the same time as the UAV imagery. The Sentinel-2 images used a mixed pixel approach as pixel size is greater than the row width. The correlation between these three layers and the Sentinel-2 derived vegetation indices were calculated, considering spatial autocorrelation correction for the significance test. The Gini coefficient was used to estimate variability detected by each sensor at the within-field scale. The effects of block border and dimension on correlations were estimated. Conclusions: The comparison between Sentinel-2 and UAV vegetation index showed an increase in correlation when border pixels were removed. Block dimensions did not affect the significance of correlation unless blocks were < 0.5 ha. Below this threshold, the correlation was non-significant in most cases. Sentinel-2 acquired data were strongly correlated with UAV-acquired data at both the field (R 2 = 0.87) and sub-field scale (R 2 = 0.84). In terms of variability detected, Sentinel-2 proved to be able to detect the same amount of variability as the UAV mixed pixel vegetation index. Significance and impact of the study: This study showed at which field conditions the Sentinel-2 vegetation index can be used instead of UAV-acquired images when high spatial resolution (vine-specific) management is not needed and the vineyard is characterised by no inter-row grass. This type of information may help growers to choose the most appropriate information sources to detect variability according to their vineyard characteristics. k e y w o r d s precision viticulture, remote sensing, spatial correlation, vineyard segmentation

This paper presents a study of precision agriculture in the wine industry. While precision viticulture mostly aims to maximise yields by delivering the right inputs to appropriate places on a farm in the correct doses and at the right time, the objective of this study was rather to assess vine biomass differences. The solution proposed in this paper uses aerial imagery as the primary source of data for vine analysis. The first objective to be achieved by the solution is to automatically identify vineyards blocks, vine rows, and individual vines within rows. This is made possible through a series of enhancements and hierarchical segmentations of the aerial images. The second objective is to determine the correlation of image data with the biophysical data (yield and pruning mass) of each vine. A multispectral aerial image is used to compute vegetation indices, which serve as indicators of biophysical measures. The results of the automatic detection are compared against a test field, ...

Accurate quantification of the spatial variation of canopy size is crucial for vineyard management
in the context of Precision Viticulture. Biophysical parameters associated with canopy size,
such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized
Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite,
or aerial imagery with moderate-resolution capture information at nadir of pixels whose values
are a mix of canopy, sunlit soil, and shaded soil fractions and their respective spectral signatures. VI
values for each fraction are considerably different. On a VSP vineyard, the illumination direction
for each specific row orientation depends on the relative position of sun and earth. Respective
proportions of shaded and sunlit soil fractions change as a function of solar elevation and azimuth,
but canopy fraction is independent of these variations. The focus of this study is the interaction of
illumination direction with canopy orientation, and the corresponding effect on integrated NDVI.
The results confirm that factors that intervene in determining the direction of illumination on a
VSP will alter the integrated NDVI value. Shading induced considerable changes in the NDVI
proportions affecting the final integrated NDVI value. However, the effect of shading decreases as
the row orientation approaches the solar path. Therefore, models of biophysical parameters using
moderate-resolution imagery should consider corrections for variations caused by factors affecting
the angle of illumination to provide more general solutions that may enable canopy data to be
obtained from mixed, integrated vine NDVI.

A b s t r a c t. The aim of this work is to present a fast and cheap method for high-resolution mapping of calcic horizons in vineyards based on geoelectrical proximal sensing. The study area, 45 ha located in southern Sicily (Italy), was characterized by an old, partially dismantled marine terrace and soils with a calcic horizon at different depths. The geoelectrical investigation consisted of a survey of the soil electrical resistivity recorded with the Automatic Resistivity Profiling-03 sensor. The electrical resistivity values at three pseudo-depths, 0-50, 0-100 and 0-170 cm, were spatialized by means of ordinary kriging. A principal component analysis of the three electrical resistivity maps was carried out. During the survey, 18 boreholes, located at different electrical resistivity values, were made for soil description and sampling. The depth to the calcic horizon showed a strong correlation with electrical resistivity. The regression model between calcic horizon and the principal component analysis factors with the highest correlation coefficients was selected to spatialise the calcic horizon values. An Normalized Difference Vegetation Index map was used to validate the calcic horizon map in terms of crop response to different soil rooting depths. The strengths of this method are the quick, non-invasive kind of survey, the relevance for vine vigour, and the high spatial resolution of the final map.

Grapes are the most widely grown commercial fruit crop in the world, and also one of the most popular fruit crops for horticultural production. Grape growers constantly search the ways in order to maximize their profits all over the world. It becomes to be important to use new information technologies to increase to overall returns. Precision Viticulture (PV) refers to the application of new and emerging information technologies to the production of grapes to improve the efficacy of production, maximize the quality of production, minimize the environmental footprint of production and minimize the risk associated with production for the grower and processer. Precision viticulture depends on new and emerging technologies such as global positioning systems (GPS), meteorological and other environmental sensors, satellite and airborne remote sensing, and geographic information systems (GIS) to assess and respond to variability. It can be possible that take under control such as soil fertility, fertilizer application norm, disease, water, weed, harvesting, and environmental management by precision viticulture systems in vineyard. So, to reduce inputs such as fertilizer, water, pesticides and to increase yield and quality of grape berries, we must to increase precision technologies in our vineyards. In this review, Precision Viticulture tools will be demonstrated to producing of high quality grapes. Finally, this study will also help grape growers and government agencies that provide new information and technologies such as Remote Sensing to growers in order to detect some factors affecting to maximize grape production.

Keskin M, YE Sekerli, SM Say, M Topcueri. 2018. Farmers’ Experiences with GNSS-Based Tractor Auto Guidance in Adana Province of Turkey. Journal of Agricultural Faculty of Gaziosmanpasa University. 35(2):172-181.

Abstract: Auto Guidance (AG) systems offer many advantages for farmers including more accuracy, more efficiency and agricultural input savings. The adoption of AG systems is increasing in many countries. Also, their adoption rate is rising in Adana province and in other regions of Turkey. The aim of this study was to assess the experiences and satisfactions of 55 out of about 110 farmers who used AG systems in Adana using face-to-face interviews. A large portion of the farmers (34.5%) had a land area of 200-300 ha followed by 50-100 ha (23.6%). The most common (49.1%) GNSS signal correction method was RTK + CORS + GSM which requires annual subscription fee. Participant farmers used the AG system mostly in tillage (98.2%) followed by planting (47.3%) and fertilization (29.1%). Most of the problems (83.3%) they faced were related to hardware. The biggest benefits provided by the system were creating straight soil ridges (98.2%), flexible working hours (92.7%), time-savings (80.0%), fuel savings (80.0%) and labor savings (50.9%). The majority of the users were "Very satisfied" (81.8%) and "Satisfied" (16.4%) with the system. However, 96.4% of them did not use other Precision Agriculture (PA) technologies and the greatest reason (54.5%) was that they did not have any knowledge about them; thus, farmers need training on other PA technologies.

Keskin M, YE Sekerli, SM Say, M Topcueri. 2018. Adana İlinde Çiftçilerin Uydu-Esaslı Traktör Otomatik Dümenleme Sistemi Deneyimleri. Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi. 35(2):172-181.

Özet: Otomatik Dümenleme (OD) sistemleri tarımsal işlemlerde çiftçilere yüksek hassassiyet, yüksek verim ve daha az tarımsal girdi kullanımı gibi birçok yarar sağlamaktadır. OD sistemlerinin kullanımı birçok ülkede artmaktadır. Türkiye'de Adana ilinde ve diğer bölgelerde kullanımı da artmaktadır. Bu çalışmanın amacı, Adana'da OD sistemleri kullanan yaklaşık 110 çiftçiden 55'i ile yüz yüze görüşerek çiftçilerin deneyim ve memnuniyet düzeylerini değerlendirmekti. Çiftçilerin büyük bir kısmının (%34.5) 200-300 ha ve 50-100 ha (%23.6) araziye sahip olduğu tespit edilmiştir. En yaygın (%49.1) GNSS sinyal düzeltme yöntemi yıllık abonelik ücreti gerektiren RTK + CORS + GSM sistemidir. Çiftçiler, OD sistemini en fazla toprak işleme (%98.2), ekim (%47.3) ve gübreleme (%29.1) işlemlerinde kullanmaktadır. Karşılaştıkları sorunların çoğunlukla (%83.3) donanımla ilgili olduğu belirlenmiştir. Sistemin sağladığı en önemli yararların, düz toprak sırtı oluşturma (%98.2), esnek çalışma saatleri (%92.7), zamandan tasarruf (%80.0), yakıt tasarrufu (%80.0) ve işgücü tasarrufu (%50.9) olduğu tespit edilmiştir. Kullanıcıların çoğunluğu sistemden "Çok memnun" (%81.8) ve "Memnun" (%16.4) olduğunu bildirmiştir. Bununla birlikte, katılımcı çiftçilerin %96.4'ü diğer Hassas Tarım (HT) teknolojilerini kullanmamaktadır ve bunun en büyük nedeninin (% 54.5) bu sistemler hakkında bilgi sahibi olmamalarıdır. Bu sebeple, çiftçilerin diğer HT teknolojileri üzerine eğitim almaları gerekli görülmektedir.

The aim of the present work was to test and develop a combination of both geophysical and pedological survey techniques devoted to the definition of a correct plan for precision viticulture. In particular, the objective of the study was to evaluate the potentiality of a combined use of these techniques to identify areas with uniform soil properties within 4 test vineyards of the "Barone Ricasoli" farm, located in the Chianti wine district (Tuscany, central Italy) and to evaluate the relationships between soil properties and wine quality. Two different geophysical methods based on the measurement of the electrical conductivity were used: an electro-magnetic induction method and electric resistivity tomographies; these were combined with detailed pedological analyses and with the evaluation of remote sensing maps. All results were compared and discussed, and finally a cluster analysis based on the evidences of geophysical tests and on pedological data was performed. For each of the identified uniform areas, a separate winemaking was successively made, and the quality of the wines is discussed and correlated to the geophysical-pedological results. The study has shown that the approach used is suitable for mapping and understanding the anomalies in soil distribution which partially reflects in the quality and effectiveness of wine production. Moreover, it was demonstrated that the geophysical data alone are not able to provide any pedological information because, in the investigated area, electrical conductivity is affected by various soil properties in a complex manner; however, these methods are very useful to integrate and complement pedological data in the aims of precision viticulture.