Individual plant definition and missing plant characterization in vineyards from high-resolution UAV imagery

Primicerio, Jacopo; Caruso, Giovanni; Comba, Lorenzo; Crisci, Alfonso; Gay, Paolo; Guidoni, Silvia; Genesio, L.; Aimonino, Davide Ricauda; Vaccari, Francesco Primo

doi:10.1080/22797254.2017.1308234

Cited by 38 publications

(25 citation statements)

References 30 publications

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“…In what concerns UAV-based approaches for individual identification of plants, the published studies mostly focus on tree detection within both forest and agriculture contexts [50][51][52]. The outcomes resulting from photogrammetric processing can be used to estimate individual geometrical and biophysical grapevine parameters, providing a plant-specific application for PV [53]. In this scope, De Castro et al [38] proposed an object-based image analysis (OBIA) method using very high-resolution vineyard DSMs (1 cm ground sample distance-GSD) to estimate grapevine vegetation within vineyard plots.…”

Section: Introductionmentioning

confidence: 99%

Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery

et al. 2020

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The use of unmanned aerial vehicles (UAVs) for remote sensing applications in precision viticulture significantly increased in the last years. UAVs’ capability to acquire high spatiotemporal resolution and georeferenced imagery from different sensors make them a powerful tool for a better understanding of vineyard spatial and multitemporal heterogeneity, allowing the estimation of parameters directly impacting plants’ health status. In this way, the decision support process in precision viticulture can be greatly improved. However, despite the proliferation of these innovative technologies in viticulture, most of the published studies rely only on data from a single sensor in order to achieve a specific goal and/or in a single/small period of the vineyard development. In order to address these limitations and fully exploit the advantages offered by the use of UAVs, this study explores the multi-temporal analysis of vineyard plots at a grapevine scale using different imagery sensors. Individual grapevine detection enables the estimation of biophysical and geometrical parameters, as well as missing grapevine plants. A validation procedure was carried out in six vineyard plots focusing on the detected number of grapevines and missing grapevines. A high overall agreement was obtained concerning the number of grapevines present in each row (99.8%), as well as in the individual grapevine identification (mean overall accuracy of 97.5%). Aerial surveys were conducted in two vineyard plots at different growth stages, being acquired for RGB, multispectral and thermal imagery. Moreover, the extracted individual grapevine parameters enabled us to assess the vineyard variability in a given epoch and to monitor its multi-temporal evolution. This type of analysis is critical for precision viticulture, constituting as a tool to significantly support the decision-making process.

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Section: Introductionmentioning

confidence: 99%

Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery

et al. 2020

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Several remote sensing technologies have been tested in precision viticulture to characterize vineyard spatial variability, from traditional aircraft and satellite platforms to recent unmanned aerial vehicles (UAVs). Imagery processing is still a challenge due to the traditional row-based architecture, where the inter-row soil provides a high to full presence of mixed pixels. In this case, UAV images combined with filtering techniques represent the solution to analyze pure canopy pixels and were used to benchmark the effectiveness of Sentinel-2 (S2) performance in overhead training systems. At harvest time, UAV filtered and unfiltered images and ground sampling data were used to validate the correlation between the S2 normalized difference vegetation indices (NDVIs) with vegetative and productive parameters in two vineyards (V1 and V2). Regarding the UAV vs. S2 NDVI comparison, in both vineyards, satellite data showed a high correlation both with UAV unfiltered and filtered images (V1 R2 = 0.80 and V2 R2 = 0.60 mean values). Ground data and remote sensing platform NDVIs correlation were strong for yield and biomass in both vineyards (R2 from 0.60 to 0.95). These results demonstrate the effectiveness of spatial resolution provided by S2 on overhead trellis system viticulture, promoting precision viticulture also within areas that are currently managed without the support of innovative technologies.

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“…sUAS agricultural applications were presented in four of the eleven manuscripts in this special issue. Primicerio et al (2017) presented a new methodology for identifying missing plants in a vineyard using RGB images. Marino and Alvino (2018) used cluster analysis of multi-temporal images to identify homogeneous wheat areas.…”

Section: Small Unmanned Aerial System Development and Applications Inmentioning

confidence: 99%

Small unmanned aerial system development and applications in precision agriculture and natural resource management

Abd-Elrahman

Quirk

Corbera

et al. 2019

European Journal of Remote Sensing

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Individual plant definition and missing plant characterization in vineyards from high-resolution UAV imagery

Cited by 38 publications

References 30 publications

Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery

Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery

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

Small unmanned aerial system development and applications in precision agriculture and natural resource management

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