Comparison of red-edge parameters for spring wheat chlorophyll content at different growth stages in irrigated and dry-land regions

Leaf chlorophyll content (LCC) is one of the most important factors affecting photosynthetic capacity and nitrogen status, both of which influence crop harvest. However, the development of rapid and nondestructive methods for leaf chlorophyll estimation is a topic of much interest. Hence, this study explored the use of the machine learning approach to enhance the estimation of leaf chlorophyll from spectral reflectance data. The objective of this study was to evaluate four different approaches for estimating the LCC of apple tree leaves at five growth stages (the 1st, 2nd, 3rd, 4th and 5th growth stages): (1) univariate linear regression (ULR); (2) multivariate linear regression (MLR); (3) support vector regression (SVR); and (4) random forest (RF) regression. Samples were collected from the leaves on the eastern, western, southern and northern sides of apple trees five times (1st, 2nd, 3rd, 4th and 5th growth stages) over three consecutive years (2016–2018), and experiments were conducted in 10–20-year-old apple tree orchards. Correlation analysis results showed that LCC and ST, LCC and vegetation indices (VIs), and LCC and three edge parameters (TEP) had high correlations with the first-order differential spectrum (FODS) (0.86), leaf chlorophyll index (LCI) (0.87), and (SDr − SDb)/ (SDr + SDb) (0.88) at the 3rd, 3rd, and 4th growth stages, respectively. The prediction models of different growth stages were relatively good. The MLR and SVR models in the LCC assessment of different growth stages only reached the highest R2 values of 0.79 and 0.82, and the lowest RMSEs were 2.27 and 2.02, respectively. However, the RF model evaluation was significantly better than above models. The R2 value was greater than 0.94 and RMSE was less than 1.37 at different growth stages. The prediction accuracy of the 1st growth stage (R2 = 0.96, RMSE = 0.95) was best with the RF model. This result could provide a theoretical basis for orchard management. In the future, more models based on machine learning techniques should be developed using the growth information and physiological parameters of orchards that provide technical support for intelligent orchard management.

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Estimation of winter wheat leaf area index at different growth stages using optimized red-edge hyperspectral vegetation indices

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/509/1/012027 ◽

2020 ◽

Vol 509 ◽

pp. 012027

Author(s):

Naichen Xing ◽

Wenjiang Huang ◽

Yingying Dong ◽

Huichun Ye ◽

Stefano Pignatti ◽

...

Keyword(s):

Winter Wheat ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Indices ◽

Growth Stages ◽

Area Index ◽

Red Edge ◽

Wheat Leaf ◽

Different Growth Stages

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Effect of morpho-physiological traits on grain yield of sorghum grown under stress at different growth stages, and stability analysis

The Journal of Agricultural Science ◽

10.1017/s002185961200072x ◽

2012 ◽

Vol 151 (5) ◽

pp. 630-647 ◽

Cited By ~ 3

Author(s):

R. SANKARAPANDIAN ◽

S. AUDILAKSHMI ◽

V. SHARMA ◽

K. GANESAMURTHY ◽

H. S. TALWAR ◽

...

Keyword(s):

Water Stress ◽

Stomatal Conductance ◽

Drought Tolerance ◽

Grain Yield ◽

Chlorophyll Content ◽

Root Length ◽

Growth Stages ◽

Flowering Stage ◽

Drought Tolerant ◽

Different Growth Stages

SUMMARYRecent trends in climate change resulting in global warming and extreme dry spells during rainy seasons are having a negative impact on grain and fodder production in rain-fed crops in India. Understanding the mechanisms of drought tolerance at various growth stages will help in developing tolerant genotypes. Crosses were made between elite and drought-tolerant sorghums, and F2and F3progenies were evaluated for drought tolerance in multiple locations. Twenty-five F4/F5derivatives along with drought-tolerant check plants (two high-yielding genotypes showing moderate drought tolerance: C43 (male parent of the commercial hybrid CSH 16, tolerant to drought) and CSV 17, (a pure line commercial cultivar released for drought-prone areas) were screened for drought tolerance under a factorial randomized block design with three replications during the rain-free months of April–June in 2007 and 2008 at Tamil Nadu Agricultural University, Kovilpatti, India. In each generation/year, four trials were conducted and water stress at different phases of crop growth,viz. vegetative, flowering and post-flowering (maturity), was imposed by withholding irrigation. Observations were recorded on grain and straw yields, plant height, number of roots, root length, leaf relative water content (LRWC), chlorophyll content and stomatal conductance under all treatments. The traits, grain yield, plant height, average root length and stomatal conductance showed significant mean sums of squares (SSs) for genotype × environment (G × E), suggesting that genotypes had significant differential response to the changing environments. Significant mean SSs due to G × E (linear) were obtained for straw yield, LRWC and chlorophyll content, indicating that the variability is partly genetic and partly influenced by environment. Grain yield was correlated with chlorophyll content (r = 0·43) at the vegetative stage, with number of roots (r = 0·49), LRWC (r = 0·51), chlorophyll content (r = 0·46) and stomatal conductance (r = −0·51) at the pre-flowering stage, and with LRWC (r = 0·50) and stomatal conductance (r = −0·40) at the post-flowering stage, under water stress. Partial least square (PLS) analysis showed that different traits were important for grain yield under water stress at different growth stages. Pyramiding the genes for the traits responsible for high grain yield under stress will help in developing stable genotypes at different stages of plant growth.

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Exploring the utility of Sentinel-2 for estimating maize chlorophyll content and leaf area index across different growth stages

Journal of Spatial Science ◽

10.1080/14498596.2021.2000898 ◽

2021 ◽

pp. 1-13

Author(s):

Sabelo Madonsela ◽

Moses Azong Cho ◽

Laven Naidoo ◽

Russell Main ◽

Nobuhle Majozi

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Chlorophyll Content ◽

Growth Stages ◽

Area Index ◽

Different Growth Stages ◽

Sentinel 2

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Effect of spring wheat (Triticum aestivum L.) treatment with brassinosteroids on the content of cadmium and lead in plant aerial biomass and grain

Plant Soil and Environment ◽

10.17221/148/2009-pse ◽

2010 ◽

Vol 56 (No. 1) ◽

pp. 43-50 ◽

Cited By ~ 15

Author(s):

M. Kroutil ◽

A. Hejtmánková ◽

J. Lachman

Keyword(s):

Heavy Metals ◽

Spring Wheat ◽

Growth Stages ◽

The Third ◽

Different Types ◽

Plant Growth Stages ◽

Aas Method ◽

Cadmium And Lead ◽

Whole Plants ◽

Different Growth Stages

Spring wheat var. Vánek was cultivated in pots in a soil naturally contaminated with heavy metals. Experimental plants were treated with three different types of brassinosteroids (BRs; 24-epibrassinolide, 24-epicastasterone and 4154) during two different growth stages 29–31 DC (off shooting) and 59–60 DC (beginning of anthesis). Content of heavy metals (Cu, Cd, Pb and Zn) was determined using AAS method in the plant growth stages 47–49 DC (visible awns), 73–75 DC (30–50% of final grain size) and 90–92 DC (full ripeness). At the stages 47–49 DC and 73–75 DC, the content of the heavy metals was determined in the biomass of whole plants, while at the stage 90–92 DC it was determined separately in straw and grains. After the treatment of plants with BRs a decrease in heavy metals content was observed in the growth stage 73–75 DC (i.e. during the period when the plants are harvested for ensilage purposes. Likewise, a decrease of lead content in the grains by 70–74% in the plants treated at both stages 29–31 DC and 59–60 DC and by 48–70% in the plants of the third group (plants treated at stage 59–60 DC) was determined as compared with the untreated plants.

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Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield

Remote Sensing ◽

10.3390/rs14010120 ◽

2021 ◽

Vol 14 (1) ◽

pp. 120

Author(s):

Razieh Barzin ◽

Hossein Lotfi ◽

Jac J. Varco ◽

Ganesh C. Bora

Keyword(s):

Machine Learning ◽

Grain Yield ◽

Vegetation Index ◽

Growth Stages ◽

Fertilizer N ◽

Red Edge ◽

Leaf N Concentration ◽

Different Growth Stages ◽

N Status ◽

Leaf N

Applying the optimum rate of fertilizer nitrogen (N) is a critical factor for field management. Multispectral information collected by active canopy sensors can potentially indicate the leaf N status and aid in predicting grain yield. Crop Circle multispectral data were acquired with the purpose of measuring the reflectance data to calculate vegetation indices (VIs) at different growth stages. Applying the optimum rate of fertilizer N can have a considerable impact on grain yield and profitability. The objectives of this study were to evaluate the reliability of a handheld Crop Circle ACS-430, to estimate corn leaf N concentration and predict grain yield of corn using machine learning (ML) models. The analysis was conducted using four ML models to identify the best prediction model for measurements acquired with a Crop Circle ACS-430 field sensor at three growth stages. Four fertilizer N levels from deficient to excessive in 50/50 spilt were applied to corn at 1–2 leaves, with visible leaf collars (V1-V2 stage) and at the V6-V7 stage to establish widely varying N nutritional status. Crop Circle spectral observations were used to derive 25 VIs for different growth stages (V4, V6, and VT) of corn at the W. B. Andrews Agricultural Systems farm of Mississippi State University. Multispectral raw data, along with Vis, were used to quantify leaf N status and predict the yield of corn. In addition, the accuracy of wavelength-based and VI-based models were compared to examine the best model inputs. Due to limited observed data, the stratification approach was used to split data to train and test set to obtain balanced data for each stage. Repeated cross validation (RCV) was then used to train the models. Results showed that the Simplified Canopy Chlorophyll Content Index (SCCCI) and Red-edge ratio vegetation index (RERVI) were the most effective VIs for estimating leaf N% and that SCCCI, Red-edge chlorophyll index (CIRE), RERVI, Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index (NDVI) were the most effective VIs for predicting corn grain yield. Additionally, among the four ML models utilized in this research, support vector regression (SVR) achieved the most accurate results for estimating leaf N concentration using either spectral bands or VIs as the model inputs.

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