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Unmanned aerial vehicles (UAV) are a novel and flexible tool in precision viticulture, as they can be used to acquire useful information to evaluate the spatial variation of vegetative growth, yield components and grape quality. In this work, the capability of multispectral imagery acquired by a UAV and the derived spectral information to assess th...
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... factors were then applied to each band of the image mosaic in order to obtain calibrated reflectance for each image pixel. From the corrected image, a total of 11 spectral índices (Table 2), selected from literature as being the most used to characterize vegetation status, were computed. ...
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... These are able to provide vigor, yield and grape quality maps at exceptionally high ground resolution, raising the prospect that the so much feared "withinvineyard variability" might turn itself into an unexpected ally to achieve higher vineyard efficiency. Starting late in the 1990s, a multitude of studies have investigated intra-vineyard spatial variability using vegetation indices and a fairly common trait of several studies has been that, regardless of vineyard size and image ground resolution, significant spatial variability in the vegetation indices has been linked to vine performance Gatti et al., 2017;Rey et al., 2013). It has been repeatedly reported that different vigor zones within the same vineyard can achieve largely different crop levels and, in turn, varying grape composition at harvest. ...
Three vigor zones, identified in a Barbera vineyard by remote sensing at full canopy, were carefully ground-truthed to determine, over 2 years, the relative weight of soil factors in affecting within-field variability, and to investigate vigor zone influence on dry matter (DM) and nutrient partitioning into different vine organs. Regardless of season, high vigor (HV) achieved stronger vine capacity as total vegetative growth and yield while resulting in markedly less ripened fruits than low vigor (LV) vines. PCA analysis carried out on ten different soil and vine variables clearly separated the three vigor levels and the correlation matrix highlighted that the factors mostly contributing to HV were soil depth, soil K and P concentration, total available water, clay fraction and N leaf concentration. Conversely, sand fraction was the main marker for LV. When annual DM retrieved in clusters, canes, leaves, and shoot clippings was calculated for each vigor level and expressed as content (i.e. kg/ha) there was a general decreasing trend moving from HV to LV. However, when DM partitioned to each organ was given on a relative basis (i.e. percentage over total) results were similar across vigor levels. Similarly, when nutrients were given as content (e.g. kg or g/ha) out of 120 within-vigor combinations (12 nutrients, 2 seasons, 5 organs), 65 showed a significant difference between HV and LV. Conversely, with data expressed on a concentration basis (i.e. % DM) the number of significant differences between the vigor level means fell to 15. The study strengthens the causal link between soil properties and intra-vineyard spatial variability and clarifies that patterns of dry matter and nutrient partitioning to different vine organs are mildly affected by vine vigor when referred on a relative basis.
... Currently, Unmanned Aerial Vehicles (UAVs) stand out among the other remote sensing platforms because they can fly at low altitudes, capture images with ultra-high spatial resolution (millimetric accuracy) [3,10,11], and, on demand in critical moments, which are not feasible with airborne or satellite platforms. Therefore, the use of UAVs has been proven to be a crucial remote sensing tool to address PV objectives [12][13][14]. ...
The perennial and stoloniferous weed, Cynodon dactylon (L.) Pers. (bermudagrass), is a serious problem in vineyards. The spectral similarity between bermudagrass and grapevines makes discrimination of the two species, based solely on spectral information from multi-band imaging sensor, unfeasible. However, that challenge can be overcome by use of object-based image analysis (OBIA) and ultra-high spatial resolution Unmanned Aerial Vehicle (UAV) images. This research aimed to automatically, accurately, and rapidly map bermudagrass and design maps for its management. Aerial images of two vineyards were captured using two multispectral cameras (RGB and RGNIR) attached to a UAV. First, spectral analysis was performed to select the optimum vegetation index (VI) for bermudagrass discrimination from bare soil. Then, the VI-based OBIA algorithm developed for each camera automatically mapped the grapevines, bermudagrass, and bare soil (accuracies greater than 97.7%). Finally, site-specific management maps were generated. Combining UAV imagery and a robust OBIA algorithm allowed the automatic mapping of bermudagrass. Analysis of the classified area made it possible to quantify grapevine growth and revealed expansion of bermudagrass infested areas. The generated bermudagrass maps could help farmers improve weed control through a well-programmed strategy. Therefore, the developed OBIA algorithm offers valuable geo-spatial information for designing site-specific bermudagrass management strategies leading farmers to potentially reduce herbicide use as well as optimize fuel, field operating time, and costs.
... As a first approach, the choice was considered relevant because the interpretation and understanding of visible images was straightforward for experts who were used to represent this. In the future, this however does not prevent using a similar approach with more complex high resolution images, e.g., multispectral (Rey et al. 2013), hyperspectral (Diago et al. 2014) or thermal (Santesteban et al. 2017). It was considered that the use of multispectral or hyperspectral images with bands outside the visible range (i.e., near infrared) required the introduction of data processing and specific explanations to make the information intelligible to experts. ...
Unmanned aerial vehicles (UAV) offer interesting alternatives to satellites or airplanes regarding flight agility and image resolution. These sensor platforms may well be used to monitor vines field all throughout the vine’s growing season at a very high spatial resolution. They could provide useful information, different to that normally considered in the literature. To identify the possible uses of UAV images in viticulture, a specific exploratory survey was put into place. This study aimed at identifying (i) relevant information that growers and advisers (G&A) can extract from UAV images and (ii) the added value that this information can have for both G&A’s vineyard management decisions. This approach was conducted on an 11.3 ha commercial vineyard with soil, climate and a training system representative of vineyards in the south of France. UAV-based visible images (25 mm resolution) were acquired every two weeks from budburst to harvest by several UAV companies. Images were shown to a panel of G&As over six sessions during the growing season. Each of these sessions consisted of (i) an individual period during which images were first presented one at a time to each expert and then all together in chronological order from budburst to harvest, and (ii) a collective period during which G&As were asked to share and discuss their point of view. In this exploratory survey, the application of the proposed methodology demonstrated that most of the information on vine status, soil and vineyard environment could be extracted from UAV-based visible images by the experts, thus showing high interest in developing specific image processing techniques to extract this information from images. Results showed that this information was of great interest throughout the growing cycle of the vine, particularly for advisers, as a support to drive management strategies. The outputs of this exploratory study should be confirmed in other contexts than the Languedoc, France region to extrapolate the observed conclusions.
... More complex analysis has been done using MS cameras with more spectral bands, allowing advanced vegetation studies. They were applied to water stress detection [26], disease detection [27] and vigor monitoring [28,29]. The UAS images in the aforementioned studies have been used to estimate the agronomic parameters LAI [30][31][32] and biomass [24,33]. ...
Unmanned aerial system (UAS)-based remote sensing is one promising technique for precision crop management, but few studies have reported the applications of such systems on nitrogen (N) estimation with multiple sensors in rice (Oryza sativa L.). This study aims to evaluate three sensors (RGB, color-infrared (CIR) and multispectral (MS) cameras) onboard UAS for the estimation of N status at individual stages and their combination with the field data collected from a two-year rice experiment. The experiments were conducted in 2015 and 2016, involving different N rates, planting densities and rice cultivars, with three replicates. An Oktokopter UAS was used to acquire aerial photography at early growth stages (from tillering to booting) and field samplings were taken at a near date. Two color indices (normalized excess green index (NExG), and normalized green red difference index (NGRDI)), two near infrared vegetation indices (green normalized difference vegetation index (GNDVI), and enhanced NDVI (ENDVI)) and two red edge vegetation indices (red edge chlorophyll index (CIred edge), and DATT) were used to evaluate the capability of these three sensors in estimating leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA) in rice. The results demonstrated that the red edge vegetation indices derived from MS images produced the highest estimation accuracy for LNA (R2: 0.79–0.81, root mean squared error (RMSE): 1.43–1.45 g m−2) and PNA (R2: 0.81–0.84, RMSE: 2.27–2.38 g m−2). The GNDVI from CIR images yielded a moderate estimation accuracy with an all-stage model. Color indices from RGB images exhibited satisfactory performance for the pooled dataset of the tillering and jointing stages. Compared with the counterpart indices from the RGB and CIR images, the indices from the MS images performed better in most cases. These results may set strong foundations for the development of UAS-based rice growth monitoring systems, providing useful information for the real-time decision making on crop N management.
... Remotely sensed data from Unmanned Aerial Vehicle (UAV) and piloted airplane are, at the moment, the most used ones in precision farming. UAV technology is greatly appreciated since it allows to collect images with very high spatial resolution (Rey et al. 2013). This peculiarity minimize commission between vegetated and not-vegetated pixels, potentially permitting an efficient separation that certainly can improve interpretation. ...
In this work we tested consistency and reliability of satellite-derived Prescription Maps (PMs) respect to those that can be obtained by aerial imagery. Test design considered a vineyard of Moscato Reale sited in Apulia (South-Eastern Italy) and two growing seasons (2013 and 2014). Comparisons concerned Landsat 8 OLI images and aerial datasets from airborne RedLake MS4100 multispectral camera. We firstly investigated the role of spatial resolution in radiometric features of data and, in particular, of NDVI maps and consequently of vigour maps. We first measured the maximum expected correlation between satellite- and aerial-derived maps. We found that, without any pixel selection and spatial interpolation, correlation ranges between 0.35 and 0.60 depending on the degree of heterogeneity of the vineyard. We also found that this result can be improved by operating a selection of those pixels representing vines canopy in aerial imagery and spatially interpolating them. In this way correlation coefficient can be improved up to 0.85 (minimum 0.60) suggesting an excellent capability of satellite data to approximate aerial ones at vineyard level. Prescription maps derived from vigour one demonstrated to be spatially consistent; but we also found that the quantitative interpretation of mapped vigour was changing in strength according to datasets and time of acquisition. Therefore, in spite of a satisfying consistency of spatial distribution, results showed that vigour strength at vineyard level from aerial and satellite datasets is generally not consistent, partially for the presence of a bias (that we modelled).
... This growth is due to the embedded systems miniaturization and process capabilities. In this way, it is possible to use UAVs in several situations, such as public safety [1], management of natural hazards [2], environmental management [3], intervention in hostile environments [4], maintenance of infrastructures [5], precision agriculture [6] and cargo transportation [7], for instance. ...
... All these characteristics allow us to obtain high resolution information in an automatic and accurate manner. By using these vehicles it is possible to transport sensors, which provide multispectral information that can be integrated into spectral indices to predict several physiological variables [28,[33][34][35], specifically for the evaluation of intra-vineyard spatial variability [35][36][37][38]. Some researchers have suggested that several indices using information between 500 and 800 nm can estimate vine water status indirectly, with coefficients of determination (R 2 ) ranging between 0.01 and 0.68 (Table 1). ...
Water stress which affects yield and wine quality is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on an unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500-800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56-0.87 with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model was able to estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of -9.1%. For the validation of CMI, MAE, RMSE and RE values were between 0.26-0.27 MPa, 0.32-0.34 MPa and -24.2-25.6%, respectively
... All these characteristics allow us to obtain high resolution information in an automatic and accurate manner. By using these vehicles it is possible to transport sensors, which provide multispectral information that can be integrated into spectral indices to predict several physiological variables [28,[33][34][35], specifically for the evaluation of intra-vineyard spatial variability [35][36][37][38]. Some researchers have suggested that several indices using information between 500 and 800 nm can estimate vine water status indirectly, with coefficients of determination (R 2 ) ranging between 0.01 and 0.68 (Table 1). ...
Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively.
... Some of them are used to estimate plant vegetative vigour, that is said to be correlated with biophysical parameters (Haboudane, Miller, Pattey, Zarco-Tejada, & Strachan, 2004;Hall, Lamb, Holzapfel, & Louis, 2002;Johnson, Bosch, Williams, & Lobitz, 2001;Montero et al., 1999;Pinter et al., 2003). In particular, in the viticulture research field many experiences, aimed at understanding the role of remote sensing in ordinary agronomic practices, can be found (Bramley, 2001;Bramley, Pearse, & Chamberlain, 2003;Borgogno-Mondino et al., 2017;Rey et al., 2013). Some works also try to define and map uncertainty of spectral indices to separate significant differences in crops from not-significant ones and better calibrate relationship with ground parameters (Borgogno-Mondino, Lessio, & Gomarasca, 2016). ...
UAVs have already demonstrated to be effective in many fields. Nevertheless, at the moment, it is not still clear the type and the value of benefits they can provide for remote sensing purposes in agriculture. In particular, in the Italian context, this technique has still to demonstrate that derivable information can improve ordinary crop management. Furthermore, it is not still clear if costs are consistent with the ones of the agricultural sector and if any actual benefit can be really obtained. Some basic questions have to be answered: (a) are costs consistent with sector incomes? and (b) which is the related economic/environmental value? In this work reference values for UAV costs and productivity are proposed. A cost simulating model, based on both technical and economic considerations, and parameterized in respect of the size of the imaged area is proposed. Different UAV company paradigms are considered demonstrating that sustainable costs can be obtained only by making remote sensing skills internal to company. A brief discussion is also given, concerning (a) UAV potential market in the Italian viticulture context and (b) expected minimal composition that a company, basing its business on this type of service, should have.
... However, a recent review of UAV acquisitions applied to vegetated areas [18] indicates that the objective of most of the published studies focus on UAV vegetation characteristics mapping, while only a few actually present quantitative validation results where UAV estimates are compared to ground measurements. Many of these studies are based on the use of multi or hyperspectral data to compute several vegetation indices in relation to the vegetation vigor [16,[19][20][21][22] or grapevine species [23,24]. More quantitative studies are related to water and nitrogen stress [21,25]. ...
In the context of precision viticulture, remote sensing in the optical domain offers a potential way to map crop structure characteristics, such as vegetation cover fraction, row orientation or leaf area index, that are later used in decision support tools. A method based on the RGB color model imagery acquired with an unmanned aerial vehicle (UAV) is proposed to describe the vineyard 3D macro-structure. The dense point cloud is first extracted from the overlapping RGB images acquired over the vineyard using the Structure from Motion algorithm implemented in the Agisoft PhotoScan software. Then, the terrain altitude extracted from the dense point cloud is used to get the 2D distribution of height of the vineyard. By applying a threshold on the height, the rows are separated from the row spacing. Row height, width and spacing are then estimated as well as the vineyard cover fraction and the percentage of missing segments along the rows. Results are compared with ground measurements with root mean square error (RMSE) = 9.8 cm for row height, RMSE = 8.7 cm for row width and RMSE = 7 cm for row spacing. The row width, cover fraction, as well as the percentage of missing row segments, appear to be sensitive to the quality of the dense point cloud. Optimal flight configuration and camera setting are therefore mandatory to access these characteristics with a good accuracy.
