Water Stress Detection in Field-Grown Maize by Using Spectral Vegetation Index

2003 ◽  
Vol 34 (1-2) ◽  
pp. 65-79 ◽  
Author(s):  
Andi Bahrun ◽  
Vagn O. Mogensen ◽  
Christian R. Jensen
Keyword(s):  
Water Stress ◽  
Vegetation Index ◽  
Stress Detection

Low-cost Smart Camera System for Water Stress Detection in Crops

2020 ◽  
Author(s):  
Paula Ramos-Giraldo ◽  
S. Chris Reberg-Horton ◽  
Steven Mirsky ◽  
Edgar Lobaton ◽  
Anna M. Locke ◽  
...  
Keyword(s):  
Water Stress ◽  
Low Cost ◽  
Smart Camera ◽  
Stress Detection ◽  
Camera System

Water stress detection based on optical multisensor fusion with a least squares support vector machine classifier

2014 ◽  
Vol 117 ◽  
pp. 15-22 ◽  
Author(s):  
Dimitrios Moshou ◽  
Xanthoula-Eirini Pantazi ◽  
Dimitrios Kateris ◽  
Ioannis Gravalos
Keyword(s):  
Support Vector Machine ◽  
Water Stress ◽  
Least Squares ◽  
Support Vector Machine Classifier ◽  
Support Vector ◽  
Multisensor Fusion ◽  
Stress Detection

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 Detecting Leaf Pulvinar Movements on NDVI Time Series of Desert Trees: A New Approach for Water Stress Detection

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e106613 ◽  
Author(s):  
Roberto O. Chávez ◽  
Jan G. P. W. Clevers ◽  
Jan Verbesselt ◽  
Paulette I. Naulin ◽  
Martin Herold
Keyword(s):  
Time Series ◽  
Water Stress ◽  
Stress Detection ◽  
New Approach ◽  
Ndvi Time Series

Optical sensing estimation of leaf nitrogen concentration in maize across a range of water-stress levels

2011 ◽  
Vol 62 (6) ◽  
pp. 474 ◽  
Author(s):  
Tong-Chao Wang ◽  
B. L. Ma ◽  
You-Cai Xiong ◽  
M. Farrukh Saleem ◽  
Feng-Min Li
Keyword(s):  
Water Stress ◽  
Vegetation Index ◽  
Optical Sensing ◽  
Leaf Nitrogen ◽  
Growth Stages ◽  
N Concentration ◽  
Leaf N Concentration ◽  
Chlorophyll Index ◽  
Plant Moisture ◽  
Leaf N

Optical sensing techniques offer an instant estimation of leaf nitrogen (N) concentration during the crop growing season. Differences in plant-moisture status, however, can obscure the detection of differences in N levels. This study presents a vegetation index that robustly measures differences in foliar N levels across a range of plant moisture levels. A controlled glasshouse study with maize (Zea mays L.) subjected to both water and N regimes was conducted in Ottawa, Canada. The purpose of the study was to identify spectral waveband(s), or indices derived from different wavebands, such as the normalised difference vegetation index (NDVI), that are capable of detecting variations in leaf N concentration in response to different water and N stresses. The experimental design includes three N rates and three water regimes in a factorial arrangement. Leaf chlorophyll content and spectral reflectance (400–1075 nm) were measured on the uppermost fully expanded leaves at the V6, V9 and V12 growth stages (6th, 9th and 12th leaves fully expanded). N concentrations of the same leaves were determined using destructive sampling. A quantitative relationship between leaf N concentration and the normalised chlorophyll index (normalised to well fertilised and well irrigated plants) was established. Leaf N concentration was also a linear function (R2 = 0.9, P < 0.01) of reflectance index (NDVI550, 760) at the V9 and V12 growth stages. Chlorophyll index increased with N nutrition, but decreased with water stress. Leaf reflectance at wavebands of 550 ± 5 nm and 760 ± 5 nm were able to separate water- and N-stressed plants from normal growing plants with sufficient water and N supply. Our results suggest that NDVI550, 760 and normalised chlorophyll index hold promise for the assessment of leaf N concentration at the leaf level of both normal and water-stressed maize plants.

scholarly journals Drought Stress Detection in Juvenile Oilseed Rape Using Hyperspectral Imaging with a Focus on Spectra Variability

2020 ◽  
Vol 12 (20) ◽  
pp. 3462
Author(s):  
Wiktor R. Żelazny ◽  
Jan Lukáš
Keyword(s):  
Drought Stress ◽  
Hyperspectral Imaging ◽  
Oilseed Rape ◽  
Treatment Effects ◽  
Vegetation Index ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Stress Detection ◽  
Mean Values ◽  
Standard Deviations

Hyperspectral imaging (HSI) has been gaining recognition as a promising proximal and remote sensing technique for crop drought stress detection. A modelling approach accounting for the treatment effects on the stress indicators’ standard deviations was applied to proximal images of oilseed rape—a crop subjected to various HSI studies, with the exception of drought. The aim of the present study was to determine the spectral responses of two cultivars, ‘Cadeli’ and ‘Viking’, representing distinctive water management strategies, to three types of watering regimes. Hyperspectral data cubes were acquired at the leaf level using a 2D frame camera. The influence of the experimental factors on the extent of leaf discolorations, vegetation index values, and principal component scores was investigated using Bayesian linear models. Clear treatment effects were obtained primarily for the vegetation indexes with respect to the watering regimes. The mean values of RGI, MTCI, RNDVI, and GI responded to the difference between the well-watered and water-deprived plants. The RGI index excelled among them in terms of effect strengths, which amounted to −0.96[−2.21,0.21] and −0.71[−1.97,0.49] units for each cultivar. A consistent increase in the multiple index standard deviations, especially RGI, PSRI, TCARI, and TCARI/OSAVI, was associated with worsening of the hydric regime. These increases were captured not only for the dry treatment but also for the plants subjected to regeneration after a drought episode, particularly by PSRI (a multiplicative effect of 0.33[0.16,0.68] for ‘Cadeli’). This result suggests a higher sensitivity of the vegetation index variability measures relative to the means in the context of the oilseed rape drought stress diagnosis and justifies the application of HSI to capture these effects. RGI is an index deserving additional scrutiny in future studies, as both its mean and standard deviation were affected by the watering regimes.

scholarly journals Using Hyperspectral and Multispectral Indices to Detect Water Stress for an Urban Turfgrass System

Agronomy ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 439 ◽  
Author(s):  
Badzmierowski ◽  
McCall ◽  
Evanylo
Keyword(s):  
Water Stress ◽  
Spectral Reflectance ◽  
Festuca Arundinacea ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Water ◽  
Management Tool ◽  
N Accumulation ◽  
Spatial Coverage ◽  
Reflectance Data

Spectral reflectance measurements collected from hyperspectral and multispectral radiometers have the potential to be a management tool for detecting water and nutrient stress in turfgrass. Hyperspectral radiometers collect hundreds of narrowband reflectance data compared to multispectral radiometers that collect three to ten broadband reflectance data for a cheaper cost. Spectral reflectance data have been used to create vegetation indices such as the normalized difference vegetation index (NDVI) and the simple ratio vegetation index (RVI) to assess crop growth, density, and fertility. Other indices such as the water band index (WBI) (narrowband index) and green-to-red ratio index (GRI) (both broadband and narrowband index) have been proposed to predict soil moisture status in turfgrass systems. The objective of this study was to compare the value of multispectral and hyperspectral radiometers to assess soil volumetric water content (VWC) and tall fescue (Festuca arundinacea Schreb.) responses. The multispectral radiometer VI had the strongest relationships to turfgrass quality, biomass, and tissue N accumulation during the trial period (April 2017–August 2018). Soil VWC had the strongest relationship to WBI (r = 0.60), followed by GRI and NDVI (both r = 0.54) for the 0% evapotranspiration (ET). Nonlinear regression showed strong relationships at high water stress periods in each year for WBI (r = 0.69–0.79), GRI (r = 0.64–0.75), and NDVI (r = 0.58–0.79). Broadband index data collected using a mobile multispectral sensor is a cheaper alternative to hyperspectral radiometry and can provide better spatial coverage.

Plant Water Stress Detection Using Leaf Temperature and Microclimatic Information

2011 ◽  
Author(s):  
Vasu Udompetaikul ◽  
Shrini K Upadhyaya ◽  
David C Slaughter ◽  
Bruce D Lampinen ◽  
Ken A Shackel
Keyword(s):  
Water Stress ◽  
Leaf Temperature ◽  
Plant Water ◽  
Stress Detection ◽  
Plant Water Stress

The satellite-based, forest-water stress detection algorithm

2012 ◽  
Author(s):  
Satoshi Tanigawa ◽  
Masao Moriyama ◽  
Yoshiaki Honda ◽  
Koji Kajiwara
Keyword(s):  
Water Stress ◽  
Detection Algorithm ◽  
Stress Detection
Sign in / Sign up

Export Citation Format

Share Document