Near infrared methodology for growth monitoring of spinach plants in the field

2021 ◽  
Author(s):  
Dolores C. Pérez-Marín ◽  
Irina Torres ◽  
Miguel Vega-Castellote ◽  
Ana Garrido-Varo ◽  
María-Teresa Sánchez
Keyword(s):  
Near Infrared ◽  
Growth Monitoring

scholarly journals Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm

2019 ◽  
Vol 11 (10) ◽  
pp. 1226 ◽  
Author(s):  
Jianqing Zhao ◽  
Xiaohu Zhang ◽  
Chenxi Gao ◽  
Xiaolei Qiu ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Near Infrared ◽  
Similarity Index ◽  
Crop Growth ◽  
Contrast Threshold ◽  
Growth Monitoring ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Aerial Vehicle ◽  
Uav Images

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

scholarly journals Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3–24 Months

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2028
Author(s):  
Alexander Miller ◽  
Jacqueline Huvanandana ◽  
Peter Jones ◽  
Heather Jeffery ◽  
Angela Carberry ◽  
...  
Keyword(s):  
Young Children ◽  
South African ◽  
Fat Mass ◽  
Near Infrared ◽  
Reference Method ◽  
Growth Monitoring ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Body Composition Assessment ◽  
Infants And Young Children

Undernutrition in infants and young children is a major problem leading to millions of deaths every year. The objective of this study was to provide a new model for body composition assessment using near-infrared reflectance (NIR) to help correctly identify low body fat in infants and young children. Eligibility included infants and young children from 3–24 months of age. Fat mass values were collected from dual-energy x-ray absorptiometry (DXA), deuterium dilution (DD) and skin fold thickness (SFT) measurements, which were then compared to NIR predicted values. Anthropometric measures were also obtained. We developed a model using NIR to predict fat mass and validated it against a multi compartment model. One hundred and sixty-four infants and young children were included. The evaluation of the NIR model against the multi compartment reference method achieved an r value of 0.885, 0.904, and 0.818 for age groups 3–24 months (all subjects), 0–6 months, and 7–24 months, respectively. Compared with conventional methods such as SFT, body mass index and anthropometry, performance was best with NIR. NIR offers an affordable and portable way to measure fat mass in South African infants for growth monitoring in low-middle income settings.

scholarly journals A Handheld Grassland Vegetation Monitoring System Based on Multispectral Imaging

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1262
Author(s):  
Aiwu Zhang ◽  
Shaoxing Hu ◽  
Xizhen Zhang ◽  
Taipei Zhang ◽  
Mengnan Li ◽  
...  
Keyword(s):  
Monitoring System ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Grassland Management ◽  
Growth Monitoring ◽  
Radiometric Calibration ◽  
Grassland Vegetation ◽  
Vegetation Monitoring ◽  
Qinghai Province ◽  
Vegetation Growth

Monitoring grassland vegetation growth is of vital importance to scientific grazing and grassland management. People expect to be able to use a portable device, like a mobile phone, to monitor grassland vegetation growth at any time. In this paper, we propose a handheld grassland vegetation monitoring system to achieve the goal of monitoring grassland vegetation growth. The system includes two parts: the hardware unit is a hand-held multispectral imaging tool named ASQ-Discover based on a smartphone, which has six bands (wavelengths)—including three visible bands (450 nm, 550 nm, 650 nm), a red-edge band (750 nm), and two near-infrared bands (850 nm, 960 nm). The imagery data of each band has a size of 5120 × 3840 pixels with 8-bit depth. The software unit improves image quality through vignetting removal, radiometric calibration, and misalignment correction and estimates and analyzes spectral traits of grassland vegetation (Fresh Grass Ratio (FGR), NDVI, NDRE, BNDVI, GNDVI, OSAVI and TGI) that are indicators of vegetation growth in grassland. We introduce the hardware and software unit in detail, and we also experiment in five pastures located in Haiyan County, Qinghai Province. Our experimental results show that the handheld grassland vegetation growth monitoring system has the potential to revolutionize the grassland monitoring that operators can conduct when using a hand-held tool to achieve the tasks of grassland vegetation growth monitoring.

scholarly journals An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

2020 ◽  
Vol 12 (23) ◽  
pp. 3880
Author(s):  
Chiman Kwan ◽  
David Gribben ◽  
Bulent Ayhan ◽  
Jiang Li ◽  
Sergio Bernabe ◽  
...  
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Land Cover Classification ◽  
Support Vector ◽  
Growth Monitoring ◽  
Land Cover Types ◽  
Imaging Detection ◽  
Better Than

Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

Construction of a near-infrared responsive upconversion nanoplatform against hypoxic tumors via NO-enhanced photodynamic therapy

Nanoscale ◽  
2020 ◽  
Vol 12 (14) ◽  
pp. 7875-7887 ◽  
Author(s):  
Ying Lan ◽  
Xiaohui Zhu ◽  
Ming Tang ◽  
Yihan Wu ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Photodynamic Therapy ◽  
Near Infrared ◽  
Upconversion Nanoparticles

A near-infrared (NIR) activated theranostic nanoplatform based on upconversion nanoparticles (UCNPs) is developed in order to overcome the hypoxia-associated resistance in photodynamic therapy by photo-release of NO upon NIR illumination.

A nitroreductase-activatable near-infrared theranostic photosensitizer for photodynamic therapy under mild hypoxia

2020 ◽  
Vol 56 (43) ◽  
pp. 5819-5822
Author(s):  
Jing Zheng ◽  
Yongzhuo Liu ◽  
Fengling Song ◽  
Long Jiao ◽  
Yingnan Wu ◽  
...  
Keyword(s):  
Photodynamic Therapy ◽  
Triplet State ◽  
Near Infrared ◽  
Mild Hypoxia

In this study, a near-infrared (NIR) theranostic photosensitizer was developed based on a heptamethine aminocyanine dye with a long-lived triplet state.

Fluorescent proteins for in vivo imaging, where's the biliverdin?

2020 ◽  
Vol 48 (6) ◽  
pp. 2657-2667
Author(s):  
Felipe Montecinos-Franjola ◽  
John Y. Lin ◽  
Erik A. Rodriguez
Keyword(s):  
Hydrogen Peroxide ◽  
In Vivo Imaging ◽  
Near Infrared ◽  
Fluorescent Protein ◽  
Fluorescent Proteins ◽  
Fluorescent Imaging ◽  
Infrared Light ◽  
Red Fluorescent Protein ◽  
Color Palette

Noninvasive fluorescent imaging requires far-red and near-infrared fluorescent proteins for deeper imaging. Near-infrared light penetrates biological tissue with blood vessels due to low absorbance, scattering, and reflection of light and has a greater signal-to-noise due to less autofluorescence. Far-red and near-infrared fluorescent proteins absorb light >600 nm to expand the color palette for imaging multiple biosensors and noninvasive in vivo imaging. The ideal fluorescent proteins are bright, photobleach minimally, express well in the desired cells, do not oligomerize, and generate or incorporate exogenous fluorophores efficiently. Coral-derived red fluorescent proteins require oxygen for fluorophore formation and release two hydrogen peroxide molecules. New fluorescent proteins based on phytochrome and phycobiliproteins use biliverdin IXα as fluorophores, do not require oxygen for maturation to image anaerobic organisms and tumor core, and do not generate hydrogen peroxide. The small Ultra-Red Fluorescent Protein (smURFP) was evolved from a cyanobacterial phycobiliprotein to covalently attach biliverdin as an exogenous fluorophore. The small Ultra-Red Fluorescent Protein is biophysically as bright as the enhanced green fluorescent protein, is exceptionally photostable, used for biosensor development, and visible in living mice. Novel applications of smURFP include in vitro protein diagnostics with attomolar (10−18 M) sensitivity, encapsulation in viral particles, and fluorescent protein nanoparticles. However, the availability of biliverdin limits the fluorescence of biliverdin-attaching fluorescent proteins; hence, extra biliverdin is needed to enhance brightness. New methods for improved biliverdin bioavailability are necessary to develop improved bright far-red and near-infrared fluorescent proteins for noninvasive imaging in vivo.

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