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

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.

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

Agriculture ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. 116 ◽  
Author(s):  
Alessandro Matese ◽  
Salvatore Di Gennaro
Keyword(s):  
Water Stress ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Stress Index ◽  
Crop Water Stress Index ◽  
Practical Applications ◽  
Water Stress Index ◽  
Sensing Applications ◽  
Wide Range ◽  
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.

scholarly journals Upscaling Evapotranspiration with Parsimonious Models in a North Carolina Vineyard

Agronomy ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 152 ◽  
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.

scholarly journals Analysis of relations between crop temperature indices and yield of different sunflower hybrids foliar treated by biopreparations

2016 ◽  
Vol 62 (1) ◽  
pp. 28-40 ◽  
Author(s):  
Marek Kovár ◽  
Ivan Černý ◽  
Dávid Ernst
Keyword(s):  
Amino Acids ◽  
Foliar Application ◽  
Plant Stress ◽  
Quantitative Relationship ◽  
Stress Index ◽  
Growth Stages ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Prediction And Control ◽  
And Control

Abstract The application of biological active preparations (BAPs) and remote-sensing control in the management of agronomic intervention are an important part of successful crop cultivation. The effects of foliar application of two BAPs (containing amino acids or Abiestins®) on yield and yield-forming, as well eco-physiological traits calculated from infrared thermographs data (crop water stress index, CWSI and index of stomatal conductance, Ig) of three hybrids of sunflower were studied in field poly-factorial experiments, realised during two years (2012 and 2013). The results showed that the application of selected BAPs has contributed to an increase of the sunflower yield, in particular through an increase in the weight of thousand seeds (rp = 0.761, P < 0.001). Similarly, oil content in achenes was significantly higher in treatments with BAPs, mainly with preparation containing free amino acids. The study describes the quantitative relationship between yield and quality of sunflower production (rp = −0.41, P < 0.01). Selected hybrids of sunflower in two growth stages showed the significant differences in CWSI and Ig (both at P < 0.01), respectively. An analysis of negative linear relation between the yield of achenes and CWSI (rp = −0.654, P < 0.001) confirmed that higher value of plant stress resulted in a smaller yield and vice-versa. The opposite trend was observed between yield and Ig index (rp = 0.576, P < 0.001). The data obtained from IR thermography can be used for monitoring the physiological health of sunflower plants, as well in potential prediction and control of yield.

Monitoring Crop Water Status and Irrigation Needs Using Multispectral Airborne Sensors

HortScience ◽  
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.

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

2013 ◽  
Vol 118 ◽  
pp. 79-86 ◽  
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

Evaluation of Resistances for Bermudagrass Turf Crop Water Stress Index Models

1994 ◽  
Vol 86 (3) ◽  
pp. 574-581 ◽  
Author(s):  
H. R. Jalali‐Farahani ◽  
D. C. Slack ◽  
D. M. Kopec ◽  
A. D. Matthias ◽  
P. W. Brown
Keyword(s):  
Water Stress ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop Water Stress

Quantifying Wheat Water Stress with the Crop Water Stress Index to Schedule Irrigations

1994 ◽  
Vol 86 (1) ◽  
pp. 195-199 ◽  
Author(s):  
Donald J. Garrot ◽  
Michael J. Ottman ◽  
D.D. Fangmeier ◽  
Stephen H. Husman
Keyword(s):  
Water Stress ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop Water Stress

scholarly journals Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling

2020 ◽  
Vol 12 (6) ◽  
pp. 1024 ◽  
Author(s):  
Yan Zhao ◽  
Andries B Potgieter ◽  
Miao Zhang ◽  
Bingfang Wu ◽  
Graeme L Hammer
Keyword(s):  
High Resolution ◽  
Crop Production ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Crop Model ◽  
Stress Index ◽  
Yield Prediction ◽  
Field Scale ◽  
Crop Water Stress Index ◽  
Sentinel 2

Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) satellite data with a return cycle of five days and a high resolution at 13 spectral bands allows close observation of crop phenology and crop physiological attributes at field scale during crop growth. Here, we test the potential for indices derived from S2 data to estimate dryland wheat yields at the field scale and the potential for enhanced predictability by incorporating a modelled crop water stress index (SI). Observations from 103 study fields over the 2016 and 2017 cropping seasons across Northeastern Australia were used. Vegetation indices derived from S2 showed moderately high accuracy in yield prediction and explained over 70% of the yield variability. Specifically, the red edge chlorophyll index (CI; chlorophyll) (R2 = 0.76, RMSE = 0.88 t/ha) and the optimized soil-adjusted vegetation index (OSAVI; structural) (R2 = 0.74, RMSE = 0.91 t/ha) showed the best correlation with field yields. Furthermore, combining the crop model-derived SI with both structural and chlorophyll indices significantly enhanced predictability. The best model with combined OSAVI, CI and SI generated a much higher correlation, with R2 = 0.91 and RMSE = 0.54 t/ha. When validating the models on an independent set of fields, this model also showed high correlation (R2 = 0.93, RMSE = 0.64 t/ha). This study demonstrates the potential of combining S2-derived indices and crop model-derived indices to construct an enhanced yield prediction model suitable for fields in diversified climate conditions.

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