Vigour Maps Research Articles

Monitoring Onion Crop “Cipolla Rossa di Tropea Calabria IGP” Growth and Yield Response to Varying Nitrogen Fertilizer Application Rates Using UAV Imagery

Remote sensing (RS) platforms such as unmanned aerial vehicles (UAVs) represent an essential source of information in precision agriculture (PA) as they are able to provide images on a daily basis and at a very high resolution. In this framework, this study aims to identify the optimal level of nitrogen (N)-based nutrients for improved productivity in an onion field of “Cipolla Rossa di Tropea” (Tropea red onion). Following an experiment that involved the arrangement of nine plots in the onion field in a randomized complete block design (RCBD), with three replications, three different levels of N fertilization were compared: N150 (150 kg N ha−1), N180 (180 kg N ha−1), and e N210 (210 kg N ha−1). The crop cycle was monitored using multispectral (MS) UAV imagery, producing vigor maps and taking into account the yield of data. The soil-adjusted vegetation index (SAVI) was used to monitor the vigor of the crop. In addition, the coverage’s class onion was spatially identified using geographical object-based image classification (GEOBIA), observing differences in SAVI values obtained in plots subjected to differentiated N fertilizer treatment. The information retrieved from the analysis of soil properties (electrical conductivity, ammonium and nitrate nitrogen), yield performance and mean SAVI index data from each field plot showed significant relationships between the different indicators investigated. A higher onion yield was evident in plot N180, in which SAVI values were higher based on the production data.

QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps

In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour.

Assessment of Vineyard Canopy Characteristics from Vigour Maps Obtained Using UAV and Satellite Imagery.

Canopy characterisation is a key factor for the success and efficiency of the pesticide application process in vineyards. Canopy measurements to determine the optimal volume rate are currently conducted manually, which is time-consuming and limits the adoption of precise methods for volume rate selection. Therefore, automated methods for canopy characterisation must be established using a rapid and reliable technology capable of providing precise information about crop structure. This research providedregression models for obtaining canopy characteristics of vineyards from unmanned aerial vehicle (UAV) and satellite images collected in three significant growth stages. Between 2018 and 2019, a total of 1400 vines were characterised manually and remotely using a UAV and a satellite-based technology. The information collected from the sampled vines was analysed by two different procedures. First, a linear relationship between the manual and remote sensing data was investigated considering every single vine as a data point. Second, the vines were clustered based on three vigour levels in the parcel, and regression models were fitted to the average values of the ground-based and remote sensing-estimated canopy parameters. Remote sensing could detect the changes in canopy characteristics associated with vegetation growth. The combination of normalised differential vegetation index (NDVI) and projected area extracted from the UAV images is correlated with the tree row volume (TRV) when raw point data were used. This relationship was improved and extended to canopy height, width, leaf wall area, and TRV when the data were clustered. Similarly, satellite-based NDVI yielded moderate coefficients of determination for canopy width with raw point data, and for canopy width, height, and TRV when the vines were clustered according to the vigour. The proposed approach should facilitate the estimation of canopy characteristics in each area of a field using a cost-effective, simple, and reliable technology, allowing variable rate application in vineyards.

Is Precision Viticulture Beneficial for the High-Yielding Lambrusco (Vitis Vinifera L.) Grapevine District?

The best Lambrusco wines are often obtained by blending a representative of the Lambrusco family (i.e. Lambrusco Salamino) with a smaller fraction of Ancellotta, a teinturier variety possessing an extraordinary quality of accumulating color. Due to the economic importance of the Lambrusco business and the rising interest in precision viticulture, a two-year trial was carried out in seven vineyard plots growing both the named varieties. A RapidEye satellite image taken on 9 August 2018, led to vigor maps based on unfiltered normalized difference vegetation index (NDVI). In both the years, ground truthing was performed on the test vines chosen within each vigor area for soil features, vegetative growth, yield, grape, and final wine composition. For data pooled over sites and years, Ancellotta showed a very clear response to NDVA-based vigor mapping as low vigor areas always achieved improved ripening in terms of higher total soluble solids (+1.24 Brix), color and phenols (+0.36 mg/kg and + 0.44mg/kg, respectively), and lower malate (-1.79 g/L) vs high vigor. Such a behavior was shown even in those cases where NDVI of different vigor levels and pruning weight were not closely correlated and, most notably, low vigor matched with a slightly higher yield as compared to high vigor plots. Overall, the high yielding Lambrusco Salamino was less responsive in terms of vine performance and grape composition versus intra-vineyard variability. This study highlights that, in Ancellotta, adjusting the vine balance toward ostensible lower vigor (i.e pruning weight ≤ 1 kg/m) would result in a superior choice in terms of improved ripening and wine profiles would not be detrimentally impacted by the yield level which, in fact, increased in some cases.

Monitoring the vegetation vigor in heterogeneous citrus and olive orchards. A multiscale object-based approach to extract trees’ crowns from UAV multispectral imagery

Precision agriculture (PA) constitutes one of the most critical sectors of remote sensing applications that allow obtaining spatial segmentation and within-field variability information from field crops. In the last decade, an increasing source of information is provided by unmanned aerial vehicle (UAVs) platforms, mainly equipped with optical multispectral cameras, to map, monitor, and analyze, temporal and spatial variations of vegetation using ad hoc spectral vegetation indices (VIs). Considering the centimeter or sub-centimeter spatial resolution of UAV imagery, the geographic object-based image analysis (GEOBIA) approach, is becoming prevalent in UAV remote sensing applications. In the present paper, we propose a quick and reliable semi-automatic workflow implemented to process multispectral UAV imagery and aimed at the detection and extraction of olive and citrus trees’ crowns to obtain vigor maps in the framework of PA. We focused our attention on the choice of GEOBIA data input and parameters, taking into consideration its replicability and reliability in the case of heterogeneous tree orchards. The heterogeneity concerns the different tree plantation distances and composition, different crop management (irrigation, pruning, weeding), and different tree age, height, and crown diameters. The proposed GEOBIA workflow was implemented in the eCognition Developer 9.5, coupling the use of multispectral and topographic information surveyed using the Tetracam µ-MCA06 snap multispectral camera at 4 cm of ground sample distance (GSD). Three different study sites in heterogeneous citrus (Bergamot and Clementine) and olive orchards located in the Calabria region (Italy) were provided. Multiresolution segmentation was implemented using spectral and topographic band layers and optimized by applying a trial-and-error approach. The classification step was implemented as process-tree and based on a rule set algorithm, therefore easily adaptable and replicable to other datasets. Decision variables for image classification were spectral vegetation indices (NDVI, SAVI, CVI) and topographic layers (DSM and CHM). Vigor maps were based on NDVI and NDRE and allowed to highlight those areas with low vegetative vigor. The accuracy assessment was based on a per-pixel approach and computed through the F-score (F). The obtained results are promising, considering that the resulting accuracy was high, with F-score ranging from 0.85 to 0.91 for olive and bergamot, respectively. Our proposed workflow, which has proved effective in datasets of different complexity, finds its strong point is the speed of execution and on its repeatability to other different crops with few adjustments. It appears worth of interest to highlights that it requests a working day of two good skilled operators in geomatics and computer image processing, from the on-field data collection to the obtaining of vigor maps.

UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture.

Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite’s output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d’Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers.

Vineyard Variability Analysis through UAV-Based Vigour Maps to Assess Climate Change Impacts

Climate change is projected to be a key influence on crop yields across the globe. Regarding viticulture, primary climate vectors with a significant impact include temperature, moisture stress, and radiation. Within this context, it is of foremost importance to monitor soils’ moisture levels, as well as to detect pests, diseases, and possible problems with irrigation equipment. Regular monitoring activities will enable timely measures that may trigger field interventions that are used to preserve grapevines’ phytosanitary state, saving both time and money, while assuring a more sustainable activity. This study employs unmanned aerial vehicles (UAVs) to acquire aerial imagery, using RGB, multispectral and thermal infrared sensors in a vineyard located in the Portuguese Douro wine region. Data acquired enabled the multi-temporal characterization of the vineyard development throughout a season through the computation of the normalized difference vegetation index, crop surface models, and the crop water stress index. Moreover, vigour maps were computed in three classes (high, medium, and low) with different approaches: (1) considering the whole vineyard, including inter-row vegetation and bare soil; (2) considering only automatically detected grapevine vegetation; and (3) also considering grapevine vegetation by only applying a normalization process before creating the vigour maps. Results showed that vigour maps considering only grapevine vegetation provided an accurate representation of the vineyard variability. Furthermore, significant spatial associations can be gathered through (i) a multi-temporal analysis of vigour maps, and (ii) by comparing vigour maps with both height and water stress estimation. This type of analysis can assist, in a significant way, the decision-making processes in viticulture.

Use of multispectral and thermal imagery in precision viticulture

The increasing demand for higher quality and yield of wine production has led to a growing interest in precision viticulture, i.e., practices of monitoring and managing spatial variations in variables related to productivity within a vineyard. This paper presents a few applications of optical measurements, in combination with monitoring systems making use of geolocation and remote/proximal sensing, to calculate vegetation indices related to plant vigour and water stress in vineyards. Measurements were performed on vineyards in Burgenland, Austria, by both aerial and proximal (terrestrial) sensing techniques. A remote-sensing, four-band multispectral sensor, placed on an Unmanned Aerial Vehicle (UAV), has been used to detect the spectral signature of the vineyard and to calculate the NDVI index, useful to selectively address the harvest on the basis of quality and quantity of grapes. Proximal, thermal infrared imaging complemented the investigation providing information about the water status of the vegetation through the CWSI index. Examples of vigour maps are provided, showing, inside a given parcel, the presence of canopies at different level of vegetation characteristics. Results provide a range of information useful to make the optimal choice in management strategies of vineyards.

Development of canopy vigour maps using UAV for site-specific management during vineyard spraying process

Site-specific management of crops represents an important improvement in terms of efficiency and efficacy of the different labours, and its implementation has experienced a large development in the last decades, especially for field crops. The particular case of the spray application process for what are called “specialty crops” (vineyard, orchard fruits, citrus, olive trees, etc.) represents one of the most controversial and influential actions directly related with economical, technical, and environmental aspects. This study was conducted with the main objective to find possible correlations between data obtained from remote sensing technology and the actual canopy characteristics. The potential correlation will be the starting point to develop a variable rate application technology based on prescription maps previously developed. An unmanned aerial vehicle (UAV) equipped with a multispectral camera was used to obtain data to build a canopy vigour map of an entire parcel. By applying the specific software DOSAVINA®, the canopy map was then transformed into a practical prescription map, which was uploaded into the dedicated software embedded in the sprayer. Adding to this information precise georeferenced placement of the sprayer, the system was able to modify the working parameters (pressure) in real time in order to follow the prescription map. The results indicate that site-specific management for spray application in vineyards result in a 45% reduction of application rate when compared with conventional spray application. This fact leads to a equivalent reduction of the amount of pesticide when concentration is maintained constant, showing once more that new technologies can help to achieve the goal of the European legislative network of safe use of pesticides.

Sentinel-2 Data Analysis and Comparison with UAV Multispectral Images for Precision Viticulture

Precision viticulture (PV) requires the use of technologies that can detect the spatial and temporal variability of vineyards and, at the same time, allow useful information to be obtained at sustainable costs. In order to develop a cheap and easy-to-handle operational monitoring scheme for PV, the aim of this work was to evaluate the possibility of using Sentinel-2 multispectral images for long-term vineyard monitoring through the Normalized Difference Vegetation Index (NDVI). Vigour maps of two vineyards located in northeastern Italy were computed from satellite imagery and compared with those derived from UAV multispectral images; their correspondence was evaluated from qualitative and statistical points of view. To achieve this, the UAV images were roughly resampled to 10 m pixel size in order to match the spatial resolution of the satellite imagery. Preliminary results show the potential use of open source Sentinel-2 platforms for monitoring vineyards, highlighting links with the information given in the agronomic bulletins and identifying critical areas for crop production. Despite the large differences in spatial resolution, the results of the comparison between the UAV and Sentinel-2 data were promising. However, for long-term vineyard monitoring at territory scale, further studies using multispectral sensor calibration and groundtruth data are required.

Unmanned aircraft system advances health mapping of fragile polar vegetation

Abstract Plants like mosses can be sensitive stress markers of subtle shifts in Arctic and Antarctic environmental conditions, including climate change. Traditional ground‐based monitoring of fragile polar vegetation is, however, invasive, labour intensive and physically demanding. High‐resolution multispectral satellite observations are an alternative, but even their recent highest achievable spatial resolution is still inadequate, resulting in a significant underestimation of plant health due to spectral mixing and associated reflectance impurities. To resolve these obstacles, we have developed a new method that uses low‐altitude unmanned aircraft system (UAS) hyperspectral images of sub‐decimeter spatial resolution. Machine‐learning support vector regressions (SVR) were employed to infer Antarctic moss vigour from quantitative remote sensing maps of plant canopy chlorophyll content and leaf density. The same maps were derived for comparison purposes from the WorldView‐2 high spatial resolution (2.2 m) multispectral satellite data. We found SVR algorithms to be highly efficient in estimating plant health indicators with acceptable root mean square errors (RMSE). The systematic RMSEs for chlorophyll content and leaf density were 3.5–6.0 and 1.3–2.0 times smaller, respectively, than the unsystematic errors. However, application of correctly trained SVR machines on space‐borne multispectral images considerably underestimated moss chlorophyll content, while stress indicators retrieved from UAS data were found to be comparable with independent field measurements, providing statistically significant regression coefficients of determination (median r2 = .50, pt test = .0072). This study demonstrates the superior performance of a cost‐efficient UAS mapping platform, which can be deployed even under the continuous cloud cover that often obscures optical high‐altitude airborne and satellite observations. Antarctic moss vigour maps of appropriate resolution could provide timely and spatially explicit warnings of environmental stress events, including those triggered by climate change. Since our polar vegetation health assessment method is based on physical principles of quantitative spectroscopy, it could be adapted to other short‐stature and fragmented plant communities (e.g. tundra grasslands), including alpine and desert regions. It therefore shows potential to become an operational component of any ecological monitoring sensor network.

A comparison between multispectral aerial and satellite imagery in precision viticulture

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).

Vineyard detection from unmanned aerial systems images

In viticulture, the adoption of precision agriculture techniques is nowadays increasingly essential to reach required high product quality standards. New reliable tools for mapping crop variability indexes in a vineyard or a single parcel are necessary to deploy site-specific management practices. In this paper, a new method to automatically detect vine rows in gray-scale aerial images is presented. The developed image processing algorithm is constituted by three main steps based on dynamic segmentation, Hough Space Clustering and Total Least Squares techniques. The procedure’s reliability has also been proven in the presence of disturbance elements, like dense inter-row grassing, bushes and trees shadows, by properly detecting vine rows in the vineyard images. Moreover, its adaptive features allow it to obtain optimal results in the presence of uneven image illumination due, for example, to the presence of clouds or steep terrain slopes. The extracted row and inter-row information, besides being the basis for vineyard characterization maps computation, like vine plants vigor maps, could also be used as a reference for other precision viticulture tasks such as, for example, path planning of unmanned ground vehicles.

Segmentación automática de imágenes digitales para estimar el vigor en viñas

La información requerida para implementar aplicaciones de viticultura de precisión en parcelas reales ha desembocado en el uso extensivo de la teledetección y la detección aérea. Si bien estos métodos son ventajosos por cubrir vastas áreas y proveer diversa información radiométrica, suelen ser inalcanzables para el productor español medio debido a los gastos ocasionados. Esta investigación desarrolla una nueva metodología para generar mapas de vigor con referencias globales basados en imágenes digitales tomadas con una cámara montada sobre un tractor convencional y con capacidad para percibir en el espectro visible, infrarrojo cercano (NIR) y ultravioleta (UV). Para hacer el sistema más versátil se analizaron dos niveles de muestreo: intensivo (1 m2 de cobertura vegetal) y super-intensivo (0,1 m2 de cobertura). El núcleo de la metodología propuesta se basa en el algoritmo de segmentación de imágenes para cuantificar automáticamente diferencias en vigor vegetativo. El cálculo del umbral dinámico se fundamenta en los conceptos matemáticos de gradiente y curvatura. Los resultados obtenidos mostraron que es posible cuantificar diferencias en vigor vegetativo de viñas utilizando el rango espectral del NIR con un muestreo intensivo. El muestreo super-intensivo y la banda espectral UV no resultaron adecuados para esta aplicación, aunque pueden aportar información clave en otras aplicaciones agronómicas. Las pruebas de campo demostraron la viabilidad de generar mapas georreferenciados de vigor desde vehículos convencionales, y mostraron el potencial de esta técnica como instrumento predictivo para explotaciones de tamaño medio.

A flexible unmanned aerial vehicle for precision agriculture

An unmanned aerial vehicle (“VIPtero”) was assembled and tested with the aim of developing a flexible and powerful tool for site-specific vineyard management. The system comprised a six-rotor aerial platform capable of flying autonomously to a predetermined point in space, and of a pitch and roll compensated multi-spectral camera for vegetation canopy reflectance recording. Before the flight campaign, the camera accuracy was evaluated against high resolution ground-based measurements, made with a field spectrometer. Then, “VIPtero” performed the flight in an experimental vineyard in Central Italy, acquiring 63 multi-spectral images during 10 min of flight completed almost autonomously. Images were analysed and classified vigour maps were produced based on normalized difference vegetation index. The resulting vigour maps showed clearly crop heterogeneity conditions, in good agreement with ground-based observations. The system provided very promising results that encourage its development as a tool for precision agriculture application in small crops.

Whithin-field temporal stability of some parameters in Viticulture: potential toward a site specific management

<p style="text-align: justify;"><strong>Aims</strong>: The goal of this paper is to present the results of a study run over 7 consecutive years which aims at characterising the Temporal Stability of Within-Field Variability (TSWFV) for the most routinely measured vine parameters. In the context of precision viticulture TSWFV is of importance to know whether or not it is relevant to use the within-field variability of the year « n » to design a site-specific management strategy for the year « n+1 ».</p><p style="text-align: justify;"><strong>Methods and results</strong>: The experiment was based on 6 vine parameters measured at 30 sites located within a non-irrigated vineyard block. Parameters measured included indicators of a vine capacity to produce biomass (pruning weight, yield and size of the canopy) as well as indicators of harvest quality (sugar content, pH and Total Titrable Acidity). For each parameter a significant temporal variability of the field average was observed from one year to another. This temporal variability led us to define the TSWFV as the occurrence of consistently high value or low value zones within the field. The definition of high and low values is done according to the average field value of the year for each parameter.</p><p style="text-align: justify;"><strong>Conclusion</strong>: TSWFV analysis allows the parameters to be classified into two distinct types. Type 1 parameters (pruning weight, yield and canopy size) which present a significant TSWFV and type 2 parameters (sugar, Total Titrable Acidity and pH) which present no TSWFV.</p><p style="text-align: justify;"><strong>Significance and impact of study</strong>: For precision viticulture management these results are significant. They show that yield or vigour (pruning weight, size of the canopy) maps of the previous years are relevant in designing site-specific management strategies in the year « n+1 » or subsequent years. Conversely, maps of quality parameters from previous years are not useful in determining how to manage harvest quality in the year « n+1 ».</p>