scholarly journals Estimation and validation of leaf chlorophyll concentration in winter wheat at heading to anthesis stage using ground-based and aerial hyperspectral data

2008 ◽  
Vol 47 (2) ◽  
pp. 39-49 ◽  
Author(s):  
Shinya TANAKA ◽  
Seijiro GOTO ◽  
Masayasu MAKI ◽  
Tsuyoshi AKIYAMA ◽  
Yasunori MURAMOTO ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Chlorophyll Concentration ◽  
Hyperspectral Data ◽  
Leaf Chlorophyll ◽  
Leaf Chlorophyll Concentration

scholarly journals Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning

2020 ◽  
Vol 12 (13) ◽  
pp. 2082 ◽  
Author(s):  
Sourav Bhadra ◽  
Vasit Sagan ◽  
Maitiniyazi Maimaitijiang ◽  
Matthew Maimaitiyiming ◽  
Maria Newcomb ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Least Squares ◽  
Chlorophyll Concentration ◽  
Feature Space ◽  
Hyperspectral Data ◽  
Leaf Chlorophyll ◽  
Reflectance Factor ◽  
Leaf Level ◽  
Leaf Chlorophyll Concentration

Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make comparisons with reflectance-based data. Within the modeling pipeline, three feature selection methods were assessed: Pearson’s correlation coefficient (PCC), partial least squares based variable importance in the projection (VIP), and random forest-based mean decrease impurity (MDI). Finally, we used partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) to estimate the LCC of sorghum. Results showed that: (1) increasing derivative order can show improved model performance until certain order for reflectance-based analysis; however, it is inconclusive to state that a particular order is optimal for estimating LCC of sorghum; (2) VI-based modeling outperformed derivative augmented reflectance factor-based modeling; (3) mean decrease impurity was found effective in selecting sensitive features from large feature space (reflectance-based analysis), whereas simple Pearson’s correlation coefficient worked better with smaller feature space (VI-based analysis); and (4) SVR outperformed all other models within reflectance-based analysis; alternatively, ELR with VIs from original reflectance yielded slightly better results compared to all other models.

scholarly journals Tolerance of Prunus Rootstock to Potassium Carbonate-induced Chlorosis

1995 ◽  
Vol 120 (2) ◽  
pp. 283-285 ◽  
Author(s):  
Yan Shi ◽  
D. H. Byrne
Keyword(s):  
Prunus Persica ◽  
Chlorophyll Concentration ◽  
Commercial Fertilizer ◽  
Nutrient Source ◽  
Solution Ph ◽  
Tolerance Level ◽  
Leaf Chlorophyll ◽  
Standardized Screening ◽  
Fe Chlorosis ◽  
Leaf Chlorophyll Concentration

A standardized screening procedure for tolerance to bicarbonate-induced Fe chlorosis using a commercial fertilizer mix (Plantex) as the nutrient source, high solution pH (8.5) and 1.5 m m bicarbonate to simulate a calcareous soil situation was used with a 1 vermiculite:1 perlite (v/v) support media, small pots and topping (pinching back the tops of shoots). The tolerance level of peach [Prunus persica (L.) Batsch] rootstock could be assessed by leaf visual-chlorosis ratings and Spad-502 chlorophyll readings instead of extractable leaf-chlorophyll concentration or plant Fe concentration. Although most of the tolerant genotypes had almond [P. amygdalus (Mill.) D.A. Webb] in their parentage, a few peaches (`Swat', NJ672281007) showed high to moderate levels of tolerance.

scholarly journals Growth and Photosynthetic Response of Two Persimmon Rootstocks (Diospyros kaki and D. virginiana) under Different Salinity Levels

2014 ◽  
Vol 42 (2) ◽  
pp. 386-391 ◽  
Author(s):  
Meral INCESU ◽  
Berken CIMEN ◽  
Turgut YESILOGLU ◽  
Bilge YILMAZ
Keyword(s):  
Gas Exchange ◽  
Leaf Gas Exchange ◽  
Chlorophyll Concentration ◽  
Dry Mass ◽  
Diospyros Kaki ◽  
Gas Exchange Parameters ◽  
Leaf Chlorophyll ◽  
Salinity Levels ◽  
Leaf Chlorophyll Concentration ◽  
Exchange Parameters

Salinity continues to be a major factor in reduced crop productivity and profit in many arid and semiarid regions. Seedlings of Diospyros kaki Thunb. and D. virginiana L. are commonly used as rootstock in persimmon cultivation. In this study we have evaluated the effects of different salinity levels on photosynthetic capacity and plant development of D. kaki and D. virginiana. Salinity was provided by adding 50 mM, 75 mM and 100 mM NaCl to nutrient solution. In order to determine the effects of different salinity levels on plant growth, leaf number, plant height, shoot and root dry mass were recorded. Besides leaf Na, Cl, K and Ca concentrations were determined. Also leaf chlorophyll concentration, chlorophyll fluorescence (Fv’/Fm’) and leaf gas exchange parameters including leaf net photosynthetic rate (PN), stomatal conductance (gS), leaf transpiration rate (E), and CO2 substomatal concentration (Ci) were investigated. Significant decrease of leaf number, shoot length and plant dry mass by increasing salinity levels was observed in both rootstocks. D. virginiana was less affected in terms of plant growth under salinity stress. Leaf chlorophyll concentration reduction was higher in the leaves of D. kaki in comparison to D. virginiana in 100 mM NaCl treatment. By increasing salinity levels PN, gS and E markedly decreased in both rootstocks and D. kaki was more affected from salinity in terms of leaf gas exchange parameters. In addition there was no significant difference but slight decreases were recorded in leaf chlorophyll fluorescences of both rootstocks.

Transfer-learning-based approach for leaf chlorophyll content estimation of winter wheat from hyperspectral data

2021 ◽  
Vol 267 ◽  
pp. 112724
Author(s):  
Yao Zhang ◽  
Jian Hui ◽  
Qiming Qin ◽  
Yuanheng Sun ◽  
Tianyuan Zhang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Chlorophyll Content ◽  
Transfer Learning ◽  
Hyperspectral Data ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll

scholarly journals Relationship between Flag Leaf Characteristics and Main Yield Components in Oat (Avena sativa L.)

2019 ◽  
Vol 76 (1) ◽  
pp. 71
Author(s):  
Ionuṭ RACZ ◽  
Rozalia KADAR ◽  
Sorin VȂTCĂ ◽  
Ioana Virginia BERINDEAN ◽  
Adrian CECLAN ◽  
...  
Keyword(s):  
Grain Yield ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Avena Sativa ◽  
Yield Components ◽  
Flag Leaf ◽  
Chlorophyll Concentration ◽  
Area Index ◽  
Leaf Chlorophyll ◽  
Leaf Chlorophyll Concentration

The objective of this study was to investigate relationships between leaf area index, leaf chlorophyll concentration, yield components and grain yield in oat (Avena sativa L.). Ten oat varieties were analyzed in field conditions regarding those traits. Flag leaf chlorophyll concentration range between 451.51 and 747.79 units of μmol of chlorophyll per m2. Also, leaf area index range between 13.68 to 32.84 cm2. Significant correlation indices were highlighted between yield components and leaf area index, yield/yield components and chlorophyll concentration of flag leaf.

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