Non-destructive Proximal Sensing for Early Detection of Citrus Nutrient and Water Stress

2012 ◽  
pp. 113-123 ◽  
Author(s):  
Paolo Menesatti ◽  
Federico Pallottino ◽  
Francesca Antonucci ◽  
Giancarlo Roccuzzo ◽  
Francesco Intrigliolo ◽  
...  
Keyword(s):  
Water Stress ◽  
Early Detection ◽  
Proximal Sensing ◽  
Non Destructive

scholarly journals The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4550
Author(s):  
Huajian Liu ◽  
Brooke Bruning ◽  
Trevor Garnett ◽  
Bettina Berger
Keyword(s):  
Hyperspectral Imaging ◽  
High Throughput ◽  
Management Practices ◽  
Leaf Tissue ◽  
Plant Phenotyping ◽  
Proximal Sensing ◽  
N Content ◽  
Hyperspectral Sensors ◽  
Wheat Leaves ◽  
Non Destructive

The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

UV excited fluorescence image-based non-destructive method for early detection of strawberry (Fragaria × ananassa) spoilage

2021 ◽  
pp. 130776
Author(s):  
Zichen Huang ◽  
Ken Abamba Omwange ◽  
Lok Wai Jacky Tsay ◽  
Yoshito Saito ◽  
Eri Maai ◽  
...  
Keyword(s):  
Early Detection ◽  
Fragaria Ananassa ◽  
Fluorescence Image ◽  
Non Destructive

Early Detection of Plant Water Stress

2020 ◽  
Vol 12 (2) ◽  
pp. 145-154
Author(s):  
Mi-Ri Kim ◽  
Tae-Yun Kim ◽  
Joo-Ah Park ◽  
Ye-Jin Seo ◽  
En-Seo Jung ◽  
...  
Keyword(s):  
Water Stress ◽  
Early Detection ◽  
Plant Water ◽  
Plant Water Stress

scholarly journals Active hyperspectral mid-infrared imaging based on a widely tunable quantum cascade laser for early detection of plant water stress

2021 ◽  
Vol 60 (02) ◽  
Author(s):  
Chaimae El Fakir ◽  
Maroun Hjeij ◽  
Ronan Le Page ◽  
Luiz Poffo ◽  
Bastien Billiot ◽  
...  
Keyword(s):  
Water Stress ◽  
Early Detection ◽  
Quantum Cascade Laser ◽  
Infrared Imaging ◽  
Plant Water ◽  
Mid Infrared ◽  
Plant Water Stress ◽  
Quantum Cascade

U.S. Industry Activities on Inspection of CRDM Penetrations

2002 ◽  
Author(s):  
Tom Alley ◽  
Kim Kietzman ◽  
Frank Ammirato
Keyword(s):  
Water Stress ◽  
Base Material ◽  
Reactor Vessel ◽  
Nuclear Industry ◽  
Considerable Effort ◽  
Control Rod ◽  
Head Penetration ◽  
Primary Water ◽  
Non Destructive ◽  
The U.S

The discovery of primary water stress corrosion cracking (PWSCC) in control rod drive mechanisms (CRDM) penetrations in U.S. and European plants prompted the U.S. nuclear industry to focus considerable effort on development and implementation of effective inspection methods. In particular, cracking was discovered in butt welds connecting reactor vessel nozzles to main coolant piping and in control rod drive mechanism (CRDM) head penetration base material and attachment welds. The EPRI Materials Reliability Program (EPRI-MRP) formed an Inspection Committee to address development of industry guidance for inspection of these components, development of effective non-destructive examination (NDE) methods, and demonstration of inspection processes. This paper discusses the MRP activities pertaining to inspection of CRDM penetrations. Results of demonstrations and field inspections conducted will also be summarized.

scholarly journals Banana Biomass Estimation and Yield Forecasting from Non-Destructive Measurements for Two Contrasting Cultivars and Water Regimes

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1435 ◽  
Author(s):  
Bert Stevens ◽  
Jan Diels ◽  
Allan Brown ◽  
Stanley Bayo ◽  
Patrick A. Ndakidemi ◽  
...  
Keyword(s):  
Water Stress ◽  
Regression Models ◽  
Growth Yield ◽  
Moisture Stress ◽  
Least Square ◽  
Biomass Estimation ◽  
Fresh Weight ◽  
Dry Biomass ◽  
Vegetative Biomass ◽  
Non Destructive

The largest abiotic constraint threatening banana (Musa spp.) production is water stress, impacting biomass buildup and yields; however, so far no studies have investigated the effects of water stress on allometric equations in banana. Weighted least square regression models were built for (i) estimating aboveground vegetative dry biomass (ABGVD) and corm dry biomass (cormD) and (ii) forecasting bunch fresh weight (bunchF), based on non-destructive parameters for two cultivars, Mchare Huti-Green Bell (HG, AA) and Cavendish Grande Naine (GN, AAA), under two irrigation regimes: full irrigation (FI) and rainfed (RF). FI affected growth, yield, and phenological parameters in the field (p < 0.05) depending on the onset of moisture stress. Pseudostem volume (Vpseudo) proved a good predictor for estimating ABGVD (R2adj = 0.88–0.92; RRMSE = 0.14–0.19), but suboptimal for cormD (R2adj = 0.90–0.89, RRMSE = 0.21–0.26 for HG; R2adj = 0.34–0.57, RRMSE = 0.38–0.43 for GN). Differences between RF and FI models (p < 0.05) were small as 95%CI overlapped. Vpseudo at flowering predicted bunchF in FI plots correctly (R2adj = 0.70 for HG, R2adj = 0.43 for GN; RRMSE = 0.12–0.15 for HG and GN). Differences between FI and RF models were pronounced as 95%CI did not overlap (p < 0.05). Bunch allometry was affected by irrigation, proving bunchF forecasting needs to include information on moisture stress during bunch filling or information on bunch parameters. Our allometric relationships can be used for rapid and non-destructive aboveground vegetative biomass (ABGVD) assessment over time and to forecast bunch potentials based on Vpseudo at flowering.

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