Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture

Matese, Alessandro; Gennaro, Salvatore Filippo Di

doi:10.3390/agriculture8070116

Cited by 107 publications

(83 citation statements)

References 31 publications

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“…Some application of sensor-based technologies to detect abiotic stress has been investigated, i.e., for detection of drought stress in barley using hyperspectral images [39] and in maize utilising spectral indices and IR-temperature [40]. Matese and Di Gennaro [41] tested the applicability of RGB, thermal and multispectral sensors to detect water stress index in vineyards. Despite examples mentioned of application of sensor-based technologies for early abiotic stress detection, the current review highlights the lack of exhaustive research in this field.…”

Section: Cluster Analysis: Trend and Gaps In The Researchmentioning

confidence: 99%

Extreme Weather Events in Agriculture: A Systematic Review

et al. 2019

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Despite the increase of publications focusing on the consequences of extreme weather events (EWE) for the agricultural sector, a specific review of EWE related to agriculture is missing. This work aimed at assessing the interrelation between EWE and agriculture through a systematic quantitative review of current scientific literature. The review analysed 19 major cropping systems (cereals, legumes, viticulture, horticulture and pastures) across five continents. Documents were extracted from the Scopus database and examined with a text mining tool to appraise the trend of publications across the years, the specific EWE-related issues examined and the research gaps addressed. The results highlighted that food security and economic losses due to the EWE represent a major interest of the scientific community. Implementation of remote sensing and imagery techniques for monitoring and detecting the effects of EWE is still underdeveloped. Large research gaps still lie in the areas concerning the effects of EWE on major cash crops (grapevine and tomato) and the agronomic dynamics of EWE in developing countries. Current knowledge on the physiological dynamics regulating the responses of main crops to EWE appears to be well established, while more research is urgently needed in the fields of mitigation measures and governance systems.

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Section: Cluster Analysis: Trend and Gaps In The Researchmentioning

confidence: 99%

Extreme Weather Events in Agriculture: A Systematic Review

et al. 2019

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“…Photogrammetric processing of imagery, as point cloud(s), is used to compute orthomosaics, Digital Elevation Models (DEM), and 3D models of the monitored area [9].UAV can be equipped with a wide range of imaging sensors useful to reliably describe the spatial variability in vineyards. RGB and multispectral cameras are the most frequently used sensors in precision viticulture applications [10]. RGB acquires spectral bands not only in the visible (Red-Green-Blue (RGB)) and multispectral but also in the near infrared spectrum (Near infrared-Red-Green (NRG)).…”

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confidence: 99%

Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images

et al. 2019

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Technical resources are currently supporting and enhancing the ability of precision agriculture techniques in crop management. The accuracy of prescription maps is a key aspect to ensure a fast and targeted intervention. In this context, remote sensing acquisition by unmanned aerial vehicles (UAV) is one of the most advanced platforms to collect imagery of the field. Besides the imagery acquisition, canopy segmentation among soil, plants and shadows is another practical and technical aspect that must be fast and precise to ensure a targeted intervention. In this paper, algorithms to be applied to UAV imagery are proposed according to the sensor used that could either be visible spectral or multispectral. These algorithms, called HSV-based (Hue, Saturation, Value), DEM (Digital Elevation Model) and K-means, are unsupervised, i.e., they perform canopy segmentation without human support. They were tested and compared in three different scenarios obtained from two vineyards over two years, 2017 and 2018 for RGB (Red-Green-Blue) and NRG (Near Infrared-Red-Green) imagery. Particular attention is given to the unsupervised ability of these algorithms to identify vines in these different acquisition conditions. This ability is quantified by the introduction of over- and under- estimation indexes, which are the algorithm’s ability to over-estimate or under-estimate vine canopies. For RGB imagery, the HSV-based algorithms consistently over-estimate vines, and never under-estimate them. The k-means and DEM method have a similar trend of under-estimation. While for NRG imagery, the HSV is the more stable algorithm and the DEM model slightly over-estimates the vines. HSV-based algorithms and the DEM algorithm have comparable computation time. The k-means algorithm increases computational demand as the quality of the DEM decreases. The algorithms developed can isolate canopy vegetation data, which is useful information about the current vineyard state, and can be used as a tool to be efficiently applied in the crop management procedure within precision viticulture applications.

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“…CWSI values can vary between 0 (no stress signs) and 1 (high levels of stress). In this study, T wet and T dry values were obtained as described in the work of Matese and Di Gennaro [23]: T wet was obtained by wetting some leaves and immediately measuring their temperature, while T dry values were obtained by applying petroleum jelly in the leaves and registering their temperatures after some minutes had gone by. Temperature values were measured using a handheld infrared thermometer (Shenzhen Jumaoyuan Science and Technology Co., Ltd., Shenzhen, China), with a ±1.5 • C precision and operating between 8000 nm to 14,000 nm.…”

Section: Data Processing and Parameters Extractionmentioning

confidence: 99%

“…Among the different VIs, which can be considered as a set of arithmetic operations applied in different bands used to extract different vegetation characteristics [18], the normalized difference vegetation index (NDVI) [19] must be highlighted as it is frequently used in agricultural applications to estimate different crop-related parameters: biomass [20]; canopy structure, leaf area index (LAI), crop management [21]; and mapping vigour zones [22]. Moreover, it was found to correlate well with grape quality properties [23]. As for temperature-based indices, they constitute a quick and practical way to estimate crop water status, therefore indicating the plants' water content.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture

Cited by 107 publications

References 31 publications

Extreme Weather Events in Agriculture: A Systematic Review

Extreme Weather Events in Agriculture: A Systematic Review

Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images

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

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