UAV Multispectral Imaging Potential to Monitor and Predict Agronomic Characteristics of Different Forage Associations

The capability of UAVs imagery to monitor and predict the evolution of several forage associations was assessed during the whole growing cycle of 2019–20. For this purpose, eight different forage associations grown in triplicate were used: vetch-barley-triticale (VBT), vetch-triticale (VT), vetch-rye (VR), vetch-oats (VO), pea-barley-triticale (PBT), pea-triticale (PT), pea-rye (PR) and pea-oats (PO). Six biophysical parameters were monitored through six vegetation indices on seven measurements dates distributed along the growing cycle. The experiments were carried out on the organic farm “Gallegos de Crespes” located in the municipality of Larrodrigo (Salamanca, Spain). The results obtained in the exploratory and the correlation analysis suggested that a predictive model (PLS regression) could be performed. Overall, vetch-based associations showed slightly higher values for both the field parameters and the vegetation indices than pea-based ones. Correlations were very strong and significant for each association throughout their growing cycle, suggesting that the evolution of the associations would be monitored from the spectral indices. Integrating these multispectral observations in the PLS model, the agronomic parameters of forage associations were predicted with a reliability of more than 50%. A single combination of VNIR (or even only visible) bands was able to feed the regression model, leading to a successful prediction of the agronomic parameters.

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Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance

Sensors ◽

10.3390/s19194059 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4059 ◽

Cited By ~ 2

Author(s):

Lu ◽

Bu ◽

Keyword(s):

Regression Model ◽

Chlorophyll Content ◽

Pls Regression ◽

Edge Region ◽

Spectral Indices ◽

Romaine Lettuce ◽

Leaf Chlorophyll ◽

Chinese White ◽

White Cabbage ◽

Leaf Surfaces

As a primary pigment of leafy green vegetables, chlorophyll plays a major role in indicating vegetable growth status. The application of hyperspectral remote sensing reflectance offers a quick and nondestructive method to estimate the chlorophyll content of vegetables. Reflectance of adaxial and abaxial leaf surfaces from three common leafy green vegetables: Pakchoi var. Shanghai Qing (Brassica chinensis L. var. Shanghai Qing), Chinese white cabbage (Brassica campestris L. ssp. Chinensis Makino var. communis Tsen et Lee), and Romaine lettuce (Lactuca sativa var longifoliaf. Lam) were measured to estimate the leaf chlorophyll content. Modeling based on spectral indices and the partial least squares regression (PLS) was tested using the reflectance data from the two surfaces (adaxial and abaxial) of leaves in the datasets of each individual vegetable and the three vegetables combined. The PLS regression model showed the highest accuracy in estimating leaf chlorophyll content of pakchoi var. Shanghai Qing (R2 = 0.809, RMSE = 62.44 mg m−2), Chinese white cabbage (R2 = 0.891, RMSE = 45.18 mg m−2) and Romaine lettuce (R2 = 0.834, RMSE = 38.58 mg m−2) individually as well as of the three vegetables combined (R2 = 0.811, RMSE = 55.59 mg m−2). The good predictability of the PLS regression model is considered to be due to the contribution of more spectral bands applied in it than that in the spectral indices. In addition, both the uninformative variable elimination PLS (UVE-PLS) technique and the best performed spectral index: MDATT, showed that the red-edge region (680–750 nm) was effective in estimating the chlorophyll content of vegetables with reflectance from two leaf surfaces. The combination of the PLS regression model and the red-edge region are insensitive to the difference between the adaxial and abaxial leaf structure and can be used for estimating the chlorophyll content of leafy green vegetables accurately.

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Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

Remote Sensing ◽

10.3390/rs13142755 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2755

Author(s):

Peng Fang ◽

Nana Yan ◽

Panpan Wei ◽

Yifan Zhao ◽

Xiwang Zhang

Keyword(s):

Aboveground Biomass ◽

Production Management ◽

Vegetation Index ◽

Net Primary Productivity ◽

Vegetation Indices ◽

Biophysical Parameters ◽

Edge Information ◽

Casa Model ◽

Red Edge ◽

Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

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Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging

Plant Methods ◽

10.1186/s13007-021-00711-y ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Shuai Che ◽

Guoying Du ◽

Ning Wang ◽

Kun He ◽

Zhaolan Mo ◽

...

Keyword(s):

High Throughput ◽

Vegetation Index ◽

Multispectral Imaging ◽

Vegetation Indices ◽

Spectral Imaging ◽

Shielding Effect ◽

Quadratic Model ◽

Biomass Estimation ◽

Marine Aquaculture ◽

Significant Difference

Abstract Background Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported. Results In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%. Conclusions This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

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Mapping Wheat Dry Matter and Nitrogen Content Dynamics and Estimation of Wheat Yield Using UAV Multispectral Imagery Machine Learning and a Variety-Based Approach: Case Study of Morocco

AgriEngineering ◽

10.3390/agriengineering3010003 ◽

2021 ◽

Vol 3 (1) ◽

pp. 29-49

Author(s):

Ghizlane Astaoui ◽

Jamal Eddine Dadaiss ◽

Imane Sebari ◽

Samir Benmansour ◽

Ettarid Mohamed

Keyword(s):

Random Forest ◽

Nitrogen Uptake ◽

Dry Matter ◽

Vegetation Indices ◽

Wheat Yield ◽

Wheat Crop ◽

Multispectral Imagery ◽

Biophysical Parameters ◽

Phenological Stages ◽

Wheat Varieties

Our work aims to monitor wheat crop using a variety-based approach by taking into consideration four different phenological stages of wheat crop development. In addition to highlighting the contribution of Red-Edge vegetation indices in mapping wheat dry matter and nitrogen content dynamics, as well as using Random Forest regressor in the estimation of wheat yield, dry matter and nitrogen uptake relying on UAV (Unmanned Aerial Vehicle) multispectral imagery. The study was conducted on an experimental platform with 12 wheat varieties located in Sidi Slimane (Morocco). Several flight missions were conducted using eBee UAV with MultiSpec4C camera according to phenological growth stages of wheat. The proposed methodology is subdivided into two approaches, the first aims to find the most suitable vegetation index for wheat’s biophysical parameters estimation and the second to establish a global model regardless of the varieties to estimate the biophysical parameters of wheat: Dry matter and nitrogen uptake. The two approaches were conducted according to six main steps: (1) UAV flight missions and in-situ data acquisition during four phenological stages of wheat development, (2) Processing of UAV multispectral images which enabled us to elaborate the vegetation indices maps (RTVI, MTVI2, NDVI, NDRE, GNDVI, GNDRE, SR-RE et SR-NIR), (3) Automatic extraction of plots by Object-based image analysis approach and creating a spatial database combining the spectral information and wheat’s biophysical parameters, (4) Monitoring wheat growth by generating dry biomass and wheat’s nitrogen uptake model using exponential, polynomial and linear regression for each variety this step resumes the varietal approach, (5) Engendering a global model employing both linear regression and Random Forest technique, (6) Wheat yield estimation. The proposed method has allowed to predict from 1 up to 21% difference between actual and estimated yield when using both RTVI index and Random Forest technique as well as mapping wheat’s dry biomass and nitrogen uptake along with the nitrogen nutrition index (NNI) and therefore facilitate a careful monitoring of the health and the growth of wheat crop. Nevertheless, some wheat varieties have shown a significant difference in yield between 2.6 and 3.3 t/ha.

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Leukemia Early Screening by Using NIR Spectroscopy and LAR-PLS Regression Model

Studies in Computational Intelligence - Artificial Intelligence and Robotics ◽

10.1007/978-3-319-69877-9_21 ◽

2017 ◽

pp. 193-201

Author(s):

Ying Qi ◽

Zhenbing Liu ◽

Xipeng Pan ◽

Weidong Zhang ◽

Shengke Yan ◽

...

Keyword(s):

Regression Model ◽

Nir Spectroscopy ◽

Pls Regression ◽

Early Screening

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MONITORAMENTO DA COBERTURA VEGETAL POR SENSORIAMENTO REMOTO NO SEMIÁRIDO BRASILEIRO ATRAVÉS DE ÍNDICES DE VEGETAÇÃO

Nativa ◽

10.31413/nativa.v7i6.7646 ◽

2019 ◽

Vol 7 (6) ◽

pp. 708

Author(s):

Caio Victor Santos Silva ◽

Jhon Lennon Bezerra da Silva ◽

Geber Barbosa De Albuquerque Moura ◽

Pabrício Marcos Oliveira Lopes ◽

Cristina Rodrigues Nascimento ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Cover ◽

Environmental Changes ◽

Spectral Response ◽

Vegetation Indices ◽

Digital Processing ◽

Semiarid Region ◽

Dry Period ◽

Biophysical Parameters ◽

Brazilian Semiarid

São necessárias medidas que visem à proteção e conservação dos recursos hídricos e naturais de forma rápida e eficiente. As técnicas de sensoriamento remoto são essenciais para o monitoramento ambiental dos recursos no semiárido no espaço e no tempo. Objetivou-se monitorar e analisar à dinâmica da cobertura vegetal através da variabilidade espaço-temporal do albedo da superfície e índices de vegetação em região de Caatinga do semiárido brasileiro por sensoriamento remoto. A área de estudo é o município de Arcoverde, localizado no semiárido de Pernambuco. O estudo foi desenvolvido através de seis imagens orbitais do Landsat-5 do sensor TM. O processamento digital dos parâmetros biofísicos foi realizado pelo algoritmo SEBAL. Os resultados foram analisados através da estatística descritiva e quanto a sua variabilidade. Áreas possivelmente degradadas foram identificadas pelos altos valores de albedo e índices de vegetação significativamente menores, localizadas à sudoeste e noroeste da região. Os índices apresentaram comportamento similares, principalmente no período seco, com baixos valores sendo próximos de zero, áreas afetadas pelo período de seca no semiárido. O SAVI apresentou maior precisão, destacando melhor resposta espectral da vegetação. O sensoriamento remoto promoveu monitoramento espaço-temporal adequado, destacando principalmente o período classificado como climaticamente seco através do albedo e índices de vegetação.Palavras-chave: Caatinga; NDVI; SAVI; mudanças ambientais; SEBAL. MONITORING OF VEGETATION COVER BY REMOTE SENSING IN BRAZILIAN SEMIARID THROUGH VEGETATION INDICES ABSTRACT: Measures are needed aimed at the protection and conservation of water and natural resources quickly and efficiently. Remote sensing techniques are essential for the environmental monitoring of resources in the semiarid region in space and time. Aimed to monitor and analyze the dynamics of vegetation cover through the spatial-temporal variability of the surface albedo and indices of vegetation in the Caatinga region of the Brazilian semiarid by remote sensing. The study area is the municipality of Arcoverde, located in the semiarid of Pernambuco. The study was developed through six orbital images of Landsat-5 of the TM sensor. The digital processing of the biophysical parameters was performed by the SEBAL algorithm. The results were analyzed through descriptive statistics and their variability. Possibly degraded areas were identified by high albedo values and significantly lower vegetation indices, located in the southwest and northwest of the region. The indexes showed similar behavior, mainly in the dry period, with low values being close to zero, areas affected by the dry period in the semiarid. The SAVI presented higher accuracy, highlighting better spectral response of the vegetation. Remote sensing promoted adequate space-time monitoring, highlighting mainly the period classified as climatically dry through the albedo and vegetation indexes.Keywords: Caatinga; NDVI; SAVI; environmental changes; SEBAL.

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Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure

Clinical Therapeutics ◽

10.1016/j.clinthera.2018.01.016 ◽

2018 ◽

Vol 40 (3) ◽

pp. 456-468.e1 ◽

Cited By ~ 2

Author(s):

Shuchi N. Joshi ◽

Nuggehally R. Srinivas ◽

Deven V. Parmar

Keyword(s):

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Human Exposure ◽

Model Development ◽

Sampling Strategy ◽

Accurate Prediction ◽

Limited Sampling Strategy ◽

Limited Sampling ◽

Successful Prediction

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Correlation analysis framework for localization-based superresolution microscopy

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1711314115 ◽

2018 ◽

Vol 115 (13) ◽

pp. 3219-3224 ◽

Cited By ~ 21

Author(s):

Joerg Schnitzbauer ◽

Yina Wang ◽

Shijie Zhao ◽

Matthew Bakalar ◽

Tulip Nuwal ◽

...

Keyword(s):

Correlation Analysis ◽

Single Molecule ◽

Quantitative Description ◽

Superior Performance ◽

Cellular Structures ◽

Analysis Framework ◽

Biophysical Parameters ◽

Superresolution Microscopy ◽

Analysis Platform ◽

Multiple Scenarios

Superresolution images reconstructed from single-molecule localizations can reveal cellular structures close to the macromolecular scale and are now being used routinely in many biomedical research applications. However, because of their coordinate-based representation, a widely applicable and unified analysis platform that can extract a quantitative description and biophysical parameters from these images is yet to be established. Here, we propose a conceptual framework for correlation analysis of coordinate-based superresolution images using distance histograms. We demonstrate the application of this concept in multiple scenarios, including image alignment, tracking of diffusing molecules, as well as for quantification of colocalization, showing its superior performance over existing approaches.

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