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

Duarte, Lia; Teodoro, Ana Cláudia; Sousa, Joaquim J.; Pádua, Luís

doi:10.3390/agronomy11050952

Cited by 15 publications

(9 citation statements)

References 35 publications

(54 reference statements)

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“…Such interest has been sparked by the shorter time needed to plan flights and replace sensors, along with the lower cost and high geometric resolution and precision timing afforded by such platforms [15][16][17][18]. Thanks to UAV imagery biomass can be estimated, missing plants identified [19][20][21], vigour maps drawn [13,22], and quality variables predicted [23,24] from vegetation indices and digital image processing.…”

Section: Introductionmentioning

confidence: 99%

Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry

et al. 2021

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In viticulture, information about vine vigour is a key input for decision-making in connection with production targets. Pruning weight (PW), a quantitative variable used as indicator of vegetative vigour, is associated with the quantity and quality of the grapes. Interest has been growing in recent years around the use of unmanned aerial vehicles (UAVs) or drones fitted with remote sensing facilities for more efficient crop management and the production of higher quality wine. Current research has shown that grape production, leaf area index, biomass, and other viticulture variables can be estimated by UAV imagery analysis. Although SfM lowers costs, saves time, and reduces the amount and type of resources needed, a review of the literature revealed no studies on its use to determine vineyard pruning weight. The main objective of this study was to predict PW in vineyards from a 3D point cloud generated with RGB images captured by a standard drone and processed by SfM. In this work, vertical and oblique aerial images were taken in two vineyards of Godello and Mencía varieties during the 2019 and 2020 seasons using a conventional Phantom 4 Pro drone. Pruning weight was measured on sampling grids comprising 28 calibration cells for Godello and 59 total cells for Mencía (39 calibration cells and 20 independent validation). The volume of vegetation (V) was estimated from the generated 3D point cloud and PW was estimated by linear regression analysis taking V as predictor variable. When the results were leave-one-out cross-validated (LOOCV), the R2 was found to be 0.71 and the RMSE 224.5 (g) for the PW estimate in Mencía 2020, calculated for the 39 calibration cells on the grounds of oblique images. The regression analysis results for the 20 validation samples taken independently of the rest (R2 = 0.62; RMSE = 249.3 g) confirmed the viability of using the SfM as a fast, non-destructive, low-cost procedure for estimating pruning weight.

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Section: Introductionmentioning

confidence: 99%

Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry

et al. 2021

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“…In fact, the value of a pixel in row i and column j of a given image (a), is evaluated if this value is greater than or equal to the threshold value T. Thus, if the pixel value is greater than or equal to the threshold T, this takes on a value of 1, whereas if the DN of the pixels is less than the threshold T, this is considered Null (Figure 8). The estimation of the threshold value [T] for discriminating bare soil and spontaneous weeds from the vineyard canopy cannot be assumed randomly, for this reason a valid alternative has been developed, the Otsu method [162]. It assumes that well-separated classes can be distinguished about the intensity values of their pixels, the method by calculating a certain threshold value succeeds in obtaining the best separation between the classes (bare soil and vegetation) allowing the binarization of the images (Figure 9) [160].…”

Section: Computer Vision Techniquesmentioning

confidence: 99%

“…This algorithm can be applied for RGB or multispectral image segmentation [165], or to binarize the greyscale point cloud for 3D point classification [166]. The estimation of the threshold value [T] for discriminating bare soil and spontaneous weeds from the vineyard canopy cannot be assumed randomly, for this reason a valid alternative has been developed, the Otsu method [162]. It assumes that well-separated classes can be distinguished about the intensity values of their pixels, the method by calculating a certain threshold value succeeds in obtaining the best separation between the classes (bare soil and vegetation) allowing the binarization of the images (Figure 9) [160].…”

Section: Computer Vision Techniquesmentioning

confidence: 99%

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

Ferro

Catania

2023

Horticulturae

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The potential of precision viticulture has been highlighted since the first studies performed in the context of viticulture, but especially in the last decade there have been excellent results have been achieved in terms of innovation and simple application. The deployment of new sensors for vineyard monitoring is set to increase in the coming years, enabling large amounts of information to be obtained. However, the large number of sensors developed and the great amount of data that can be collected are not always easy to manage, as it requires cross-sectoral expertise. The preliminary section of the review presents the scenario of precision viticulture, highlighting its potential and possible applications. This review illustrates the types of sensors and their operating principles. Remote platforms such as satellites, unmanned aerial vehicles (UAV) and proximal platforms are also presented. Some supervised and unsupervised algorithms used for object-based image segmentation and classification (OBIA) are then discussed, as well as a description of some vegetation indices (VI) used in viticulture. Photogrammetric algorithms for 3D canopy modelling using dense point clouds are illustrated. Finally, some machine learning and deep learning algorithms are illustrated for processing and interpreting big data to understand the vineyard agronomic and physiological status. This review shows that to perform accurate vineyard surveys and evaluations, it is important to select the appropriate sensor or platform, so the algorithms used in post-processing depend on the type of data collected. Several aspects discussed are fundamental to the understanding and implementation of vineyard variability monitoring techniques. However, it is evident that in the future, artificial intelligence and new equipment will become increasingly relevant for the detection and management of spatial variability through an autonomous approach.

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“…Users do not have to worry about the management and maintenance of the database, so that GIS is truly integrated into life, making it omnipotent and omnipresent [ 15 ]. In the computer and communication environment, Open GIS [ 16 ] is a GIS system established according to industry standards and interfaces, which is characterized by allowing data not only to be limited within the system but also to be transmitted between systems. Its interoperability and portability are unmatched by traditional GIS.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of GIS Technology-Supported Cross Media Fusion Method Based on Deep Learning in Landscape Performance Evaluation

Liu

Wang

Xiao

et al. 2022

Computational Intelligence and Neuroscience

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GIS technology can provide reasonable and sustainable data support for landscape planning and ecological development and make wetland landscape planning consider the spatial layout of landscape and the optimal allocation of resources more. The key technologies of cross media intelligence mainly focus on intelligent information retrieval, analysis and reasoning, knowledge map construction, and intelligent storage. Convolutional neural network (CNN), as one of the representative algorithms of deep learning, plays an important role in retrieving landscape data and extracting image and text features across media. Further retrieval of media data, in-depth text processing, and image feature data extraction are realized by using deep learning technology, and comprehensive in-depth analysis is carried out by combining landscape plane images, three-dimensional images, and vector information in GIS technology. Provide quantitative information for the evaluation system of human landscape, economy, history, and region, so as to formulate a scientific and reasonable performance evaluation system.

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

Cited by 15 publications

References 35 publications

Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry

Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

Application of GIS Technology-Supported Cross Media Fusion Method Based on Deep Learning in Landscape Performance Evaluation

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