Hyperspectral remote sensing of plant pigments

GA Blackburn - Journal of experimental botany, 2007 - academic.oup.com
The dynamics of pigment concentrations are diagnostic of a range of plant physiological
properties and processes. This paper appraises the developing technologies and analytical …

Development of soft computing and applications in agricultural and biological engineering

Y Huang, Y Lan, SJ Thomson, A Fang… - … and electronics in …, 2010 - Elsevier
Soft computing is a set of “inexact” computing techniques, which are able to model and
analyze very complex problems. For these complex problems, more conventional methods …

Advances in hyperspectral remote sensing of vegetation and agricultural crops

PS Thenkabail, JG Lyon, A Huete - … , Sensor Systems, Spectral …, 2018 - taylorfrancis.com
Hyperspectral data (Table 1) is acquired as continuous narrowbands (eg, each band with 1
to 10 nanometer or nm bandwidths) over a range of electromagnetic spectrum (eg, 400 …

Non-destructive estimation of foliar chlorophyll and carotenoid contents: Focus on informative spectral bands

O Kira, R Linker, A Gitelson - … Journal of Applied Earth Observation and …, 2015 - Elsevier
Leaf pigment content provides valuable insight into the productivity, physiological and
phenological status of vegetation. Measurement of spectral reflectance offers a fast …

Estimation of plant nutritional status by Vis–NIR spectrophotometric analysis on orange leaves [Citrus sinensis (L) Osbeck cv Tarocco]

P Menesatti, F Antonucci, F Pallottino, G Roccuzzo… - Biosystems …, 2010 - Elsevier
Nutritional status in citrus plants, which is used as a guide for fertilisation, is normally
determined by chemical analysis of leaves. According to standardised procedures, this is a …

Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn

Y Miao, DJ Mulla, GW Randall, JA Vetsch, R Vintila - Precision agriculture, 2009 - Springer
The chlorophyll meter (CM) has been commonly used for in-season nitrogen (N)
management of corn (Zea mays L.). Nevertheless, it has limited potential for site-specific N …

Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices

M Liu, X Liu, M Li, M Fang, W Chi - Biosystems engineering, 2010 - Elsevier
Heavy metal stress in soils results in subtle changes in leaf chlorophyll concentration, which
are related to crop growth and crop yield. Accurate estimation of the chlorophyll …

Determination of significant wavelengths and prediction of nitrogen content for citrus

M Min, WS Lee - Transactions of the ASAE, 2005 - elibrary.asabe.org
This research was conducted as a preliminary step toward developing a real-time spectral-
based nitrogen sensorfor citrus trees. Diffuse reflectance of leaf samples, with five nitrogen …

Association between canopy reflectance indices and yield and physiological traits in bread wheat under drought and well-irrigated conditions

M Gutiérrez-Rodríguez, MP Reynolds… - Australian Journal of …, 2004 - CSIRO Publishing
Spectral reflectance (SR) indices [NDVI (R900–R680/R900+ R680); GNDVI (R780–
R550/R780+ R550); and water index, WI (R900/R970)]; and 6 chlorophyll indices …

Zinc concentration prediction in rice grain using back-propagation neural network based on soil properties and safe utilization of paddy soil: A large-scale field study in …

Y Wang, T Yu, Z Yang, H Bo, Y Lin, Q Yang… - Science of the Total …, 2021 - Elsevier
Zn is an essential nutrient for humans, with crucial biological functions. However, Zn
concentration in rice grains is generally low. Therefore, a cereal-based diet may lead to Zn …