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

High spatial ground resolution and highly flexible and timely control due to reduced planning time are the strengths of unmanned aerial vehicle (UAV) platforms for remote sensing applications. These characteristics make them ideal especially in the medium–small agricultural systems typical of many Italian viticulture areas of excellence. UAV can be equipped with a wide range of sensors useful for several applications. Numerous assessments have been made using several imaging sensors with different flight times. This paper describes the implementation of a multisensor UAV system capable of flying with three sensors simultaneously to perform different monitoring options. The intra-vineyard variability was assessed in terms of characterization of the state of vines vigor using a multispectral camera, leaf temperature with a thermal camera and an innovative approach of missing plants analysis with a high spatial resolution RGB camera. The normalized difference vegetation index (NDVI) values detected in different vigor blocks were compared with shoot weights, obtaining a good regression (R2 = 0.69). The crop water stress index (CWSI) map, produced after canopy pure pixel filtering, highlighted the homogeneous water stress areas. The performance index developed from RGB images shows that the method identified 80% of total missing plants. The applicability of a UAV platform to use RGB, multispectral and thermal sensors was tested for specific purposes in precision viticulture and was demonstrated to be a valuable tool for fast multipurpose monitoring in a vineyard.

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Upscaling Evapotranspiration with Parsimonious Models in a North Carolina Vineyard

Agronomy ◽

10.3390/agronomy9030152 ◽

2019 ◽

Vol 9 (3) ◽

pp. 152 ◽

Cited By ~ 2

Author(s):

Christian Dold ◽

Joshua Heitman ◽

Gill Giese ◽

Adam Howard ◽

John Havlin ◽

...

Keyword(s):

North Carolina ◽

Water Stress ◽

Water Use ◽

Land Surface ◽

Vegetation Index ◽

Multiple Linear Regression Model ◽

Water Dynamics ◽

Stress Index ◽

Crop Water Stress Index ◽

Water Stress Index

Water stress can positively or negatively impact grape yield and yield quality, and there is a need for wine growers to accurately regulate water use. In a four-year study (2010–2013), energy balance fluxes were measured with an eddy-covariance (EC) system in a North Carolina vineyard (Vitis vinifera cv. Chardonnay), and evapotranspiration (ET) and the Crop Water Stress Index (CWSI) calculated. A multiple linear regression model was developed to upscale ET using air temperature (Ta), vapor pressure deficit (VPD), and Landsat-derived Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI). Daily ET reached values of up to 7.7 mm day−1, and the annual ET was 752 ± 59 mm, as measured with the EC system. The grapevine CWSI was between 0.53–0.85, which indicated moderate water stress levels. Median vineyard EVI was between 0.22 and 0.72, and the EVI range (max–min) within the vineyard was 0.18. The empirical models explained 75%–84% of the variation in ET, and all parameters had a positive linear relationship to ET. The Root Mean Square Error (RMSE) was 0.52–0.62 mm. This study presents easily applicable approaches to analyzing water dynamics and ET. This may help wine growers to cost-effectively quantify water use in vineyards.

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

Agronomy ◽

10.3390/agronomy9100581 ◽

2019 ◽

Vol 9 (10) ◽

pp. 581 ◽

Cited By ~ 9

Author(s):

Luís Pádua ◽

Pedro Marques ◽

Telmo Adão ◽

Nathalie Guimarães ◽

António Sousa ◽

...

Keyword(s):

Climate Change ◽

Water Stress ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Crop Yields ◽

Moisture Stress ◽

Temporal Analysis ◽

Stress Index ◽

Crop Water Stress Index ◽

Multi Temporal

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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The Application of a Self-Organizing Model for the Estimation of Crop Water Stress Index (CWSI) in Soybean with Different Watering Levels

Water ◽

10.3390/w13223306 ◽

2021 ◽

Vol 13 (22) ◽

pp. 3306

Author(s):

Angela Anda ◽

Brigitta Simon-Gáspár ◽

Gábor Soós

Keyword(s):

Water Stress ◽

Water Use ◽

Stress Index ◽

Crop Water ◽

Crop Water Stress Index ◽

Growing Seasons ◽

Water Stress Index ◽

Wide Range ◽

Crop Water Stress ◽

Self Organizing

A field experiment was conducted with soybean to observe evapotranspiration (ET) and crop water stress index (CWSI) with three watering levels at Keszthely, Hungary, during the growing seasons 2017–2020. The three different watering levels were rainfed, unlimited, and water stress in flowering. Traditional and converted evapotranspirometers documented water stress levels in two soybean varieties (Sinara, Sigalia), with differing water demands. ET totals with no significant differences between varieties varied from 291.9 to 694.9 mm in dry, and from 205.5 to 615.6 mm in wet seasons. Theoretical CWSI, CWSIt was computed using the method of Jackson. One of the seasons, the wet 2020 had to be excluded from the CWSIt analysis because of uncertain canopy temperature, Tc data. Seasonal mean CWSIt and Tc were inversely related to water use efficiency. An unsupervised Kohonen self-organizing map (K-SOM) was developed to predict the CWSI, CWSIp based on easily accessible meteorological variables and Tc. In the prediction, the CWSIp of three watering levels and two varieties covered a wide range of index values. The results suggest that CWSIp modelling with the minimum amount of input data provided opportunity for reliable CWSIp predictions in every water treatment (R2 = 0.935–0.953; RMSE = 0.033–0.068 mm, MAE = 0.026–0.158, NSE = 0.336–0.901, SI = 0.095–0.182) that could be useful in water stress management of soybean. However, highly variable weather conditions in the mild continental climate of Hungary might limit the potential of CWSI application. The results in the study suggest that a less than 450 mm seasonal precipitation caused yield reduction. Therefore, a 100–160 mm additional water use could be recommended during the dry growing seasons of the country. The 150 year-long local meteorological data indicated that 6 growing seasons out of 10 are short of precipitation in rainfed soybean.

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Monitoring Crop Water Status and Irrigation Needs Using Multispectral Airborne Sensors

HortScience ◽

10.21273/hortsci.30.4.905d ◽

1995 ◽

Vol 30 (4) ◽

pp. 905D-905

Author(s):

Thomas R. Clarke ◽

M. Susan Moran

Keyword(s):

Water Stress ◽

Near Infrared ◽

Water Status ◽

Canopy Cover ◽

Stress Index ◽

Crop Water ◽

Vegetative Cover ◽

Crop Water Stress Index ◽

Water Stress Index ◽

Subsurface Drip

Water application efficiency can be improved by directly monitoring plant water status rather than depending on soil moisture measurements or modeled ET estimates. Plants receiving sufficient water through their roots have cooler leaves than those that are water-stressed, leading to the development of the Crop Water Stress Index based on hand-held infrared thermometry. Substantial error can occur in partial canopies, however, as exposed hot soil contributes to deceptively warm temperature readings. Mathematically comparing red and near-infrared reflectances provides a measure of vegetative cover, and this information was combined with thermal radiance to give a two-dimensional index capable of detecting water stress even with a low percentage of canopy cover. Thermal, red, and near-infrared images acquired over subsurface drip-irrigated cantaloupe fields demonstrated the method's ability to detect areas with clogged emitters, insufficient irrigation rate, and system water leaks.

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An insight to the performance of crop water stress index for olive trees

Agricultural Water Management ◽

10.1016/j.agwat.2012.12.004 ◽

2013 ◽

Vol 118 ◽

pp. 79-86 ◽

Cited By ~ 47

Author(s):

N. Agam ◽

Y. Cohen ◽

J.A.J. Berni ◽

V. Alchanatis ◽

D. Kool ◽

...

Keyword(s):

Water Stress ◽

Stress Index ◽

Crop Water ◽

Crop Water Stress Index ◽

Water Stress Index ◽

Olive Trees ◽

Crop Water Stress

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Detecting Plant Stress Using Thermal and Optical Imagery From an Unoccupied Aerial Vehicle

Frontiers in Plant Science ◽

10.3389/fpls.2021.734944 ◽

2021 ◽

Vol 12 ◽

Author(s):

Bonny Stutsel ◽

Kasper Johansen ◽

Yoann M. Malbéteau ◽

Matthew F. McCabe

Keyword(s):

Salinity Stress ◽

Vegetation Index ◽

Plant Stress ◽

Infrared Camera ◽

Water Security ◽

Optical Data ◽

Stress Index ◽

Crop Water Stress Index ◽

Water Stress Index ◽

Management Interventions

Soil and water salinization has global impact on the sustainability of agricultural production, affecting the health and condition of staple crops and reducing potential yields. Identifying or developing salt-tolerant varieties of commercial crops is a potential pathway to enhance food and water security and deliver on the global demand for an increase in food supplies. Our study focuses on a phenotyping experiment that was designed to establish the influence of salinity stress on a diversity panel of the wild tomato species, Solanum pimpinellifolium. Here, we explore how unoccupied aerial vehicles (UAVs) equipped with both an optical and thermal infrared camera can be used to map and monitor plant temperature (Tp) changes in response to applied salinity stress. An object-based image analysis approach was developed to delineate individual tomato plants, while a green–red vegetation index derived from calibrated red, green, and blue (RGB) optical data allowed the discrimination of vegetation from the soil background. Tp was retrieved simultaneously from the co-mounted thermal camera, with Tp deviation from the ambient temperature and its change across time used as a potential indication of stress. Results showed that Tp differences between salt-treated and control plants were detectable across the five separate UAV campaigns undertaken during the field experiment. Using a simple statistical approach, we show that crop water stress index values greater than 0.36 indicated conditions of plant stress. The optimum period to collect UAV-based Tp for identifying plant stress was found between fruit formation and ripening. Preliminary results also indicate that UAV-based Tp may be used to detect plant stress before it is visually apparent, although further research with more frequent image collections and field observations is required. Our findings provide a tool to accelerate field phenotyping to identify salt-resistant germplasm and may allow farmers to alleviate yield losses through early detection of plant stress via management interventions.

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