Damage Assessment of Rice Crop after Toluene Exposure Based on the Vegetation Index (VI) and UAV Multispectral Imagery

Chemical spill accidents lead to environmental problems, especially for plants. Plant vegetation assessment is necessary after a chemical accident; however, conventional methods can be inaccurate and time-consuming. This study used the vegetation index (VI) extracted from unmanned aerial vehicle (UAV) multispectral imagery for crop damage assessment after chemical exposure. The chemical accident simulations were conducted by exposure of rice at five growth stages to four levels of toluene. The VI was measured at five days after damage and 67 days after planting. Physiological characteristics (chlorophyll content and grain yield) were also measured. As a result, the mean normalized difference VI (NDVI) of toluene-exposed rice was significantly decreased with respect to toluene exposure concentration increases at most growth stages. Recovery after toluene exposure was lower in rice exposed to higher concentrations at the earlier growth stages. The chlorophyll content and grain yield were also decreased after toluene exposure with respect to increasing toluene concentrations and showed positive correlations with the NDVI. It indicates that the NDVI is capable of reflecting the plant response to chemical exposure. Thus, the results demonstrated that the VI based on UAV multispectral imagery is feasible as an alternative for crop monitoring, damage assessment after chemical exposure, and yield prediction.

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Normalized Difference Vegetation Index and Chlorophyll Content for Precision Nitrogen Management in Durum Wheat Cultivars under Semi-Arid Conditions

Sustainability ◽

10.3390/su13073725 ◽

2021 ◽

Vol 13 (7) ◽

pp. 3725

Author(s):

Ferhat Kizilgeci ◽

Mehmet Yildirim ◽

Mohammad Sohidul Islam ◽

Disna Ratnasekera ◽

Muhammad Aamir Iqbal ◽

...

Keyword(s):

Grain Yield ◽

Durum Wheat ◽

Chlorophyll Content ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Screening Tools ◽

Growth Stages ◽

Quality Traits ◽

Wheat Genotypes ◽

N Deficiency

To impart sustainability to modern intensive farming systems, environmental pollution caused by nitrogenous fertilizers in needs to be reduced by optimizing their doses. To estimate the grain yield and nutrtional quallity of wheat, the normalized difference vegetation index (NDVI) and chlorophyll content (SPAD) are potential screening tools to identify the N deficiency and screen out the promising cultivars. The two-year field study was comprised with five levels of nitrogen (N) (control, 50, 100, 150 and 200 kg N ha−1) and two durum wheat genotypes (Sena and Svevo). The experimental design was split-plot, in which N levels were placed in the main plots, while wheat genotypes were arranged in sub-plots. To predict the yield and quality traits, the NDVI and SPAD values recorded at heading, anthesis and milky growth stages were taken as response variables. The results revealed that N fertilization significantly influenced the SPAD and NDVI attributed traits of durum wheat, except NDVI at milky stage (NDVI-M) during the first year. The maximum value of NDVI was recorded by 150 kg N ha−1, while control treatment gave the minimum value. The grain yield was increased with the increasing dose of the N up to 100 kg N ha−1 (4121 kg ha−1), and thereafter, it was declined with further increased of N levels. However, the variation between the genotypes was not significant, except NDVI and SPAD values at the milky stage. The genotype Svevo had the highest NDVI values at all growth stages, while the genotype Sena recorded the maximum SPAD values during both years. Similarly, the N levels significantly influenced the quality traits (protein, wet gluten, starch test weight and Zeleny sedimentation) of both genotypes. The highly significant relationship of SPAD and NDVI with the grain yield and yield attributes showed their reliability as indicators for determining the N deficiency and selection of superior wheat genotypes for ensuring food security under climate change scenario.

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Tolerance and Selectivity of Cereal Species and Cultivars to Postemergence Weed Harrowing

Weed Science ◽

10.1614/ws-08-109.1 ◽

2009 ◽

Vol 57 (3) ◽

pp. 338-345 ◽

Cited By ~ 17

Author(s):

Jesper Rasmussen ◽

Helle H. Nielsen ◽

Hanne Gundersen

Keyword(s):

Grain Yield ◽

Weed Control ◽

Field Experiments ◽

Yield Loss ◽

Soil Cover ◽

Crop Damage ◽

Growth Stages ◽

Crop Tolerance ◽

Barley Cultivars ◽

Cereal Species

POST weed harrowing and other cultivation methods to control weeds in early crop growth stages may result in crop damage due to low selectivity between crop and weeds. Crop tolerance to cultivation plays an important role but it has not been clearly defined and analyzed. We introduce a procedure for analyzing crop tolerance on the basis of digital image analysis. Crop tolerance is defined as the ability of the crop to avoid yield loss from cultivation in the absence of weeds, and it has two components: resistance and recovery. Resistance is the ability of the crop to resist soil covering and recovery is the ability to recover from it. Soil covering is the percentage of the crop that has been buried because of cultivation. We analyzed data from six field experiments, four experiments with species of small grains, barley, oat, wheat, and triticale, and two experiments with barley cultivars with different abilities to suppress weeds. The order of species' tolerance to weed harrowing was triticale > wheat > barley > oat and the differences were mainly caused by different abilities to recover from soil covering. At 25% soil covering, grain yield loss in triticale was 0.5%, in wheat 2.5%, in barley 3.7%, and in oat 6.5%. Tolerance, resistance, and recovery, however, were influenced by year, especially for oat and barley. There was no evidence of differences between barley cultivars in terms of tolerance indicating that differences among species are more important than differences among cultivars. Selectivity analysis made it possible to calculate the crop yield loss due to crop damage associated with a certain percentage of weed control. In triticale, 80% weed control was associated with 22% crop soil cover on average, which reduced grain yield 0.4% on average in the absence of weeds. Corresponding values for wheat, barley, and oat were 23, 21, and 20% crop soil cover and 2.3, 3.6, and 5.1% grain yield loss.

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Yield prediction and nitrogen recommendation in maize using normalized difference vegetation index

Agronomy Journal of Nepal ◽

10.3126/ajn.v3i0.9009 ◽

2013 ◽

Vol 3 ◽

pp. 82-88 ◽

Cited By ~ 1

Author(s):

TB Karki

Keyword(s):

Grain Yield ◽

Growth Stage ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Correlation Coefficients ◽

Growth Stages ◽

Strong Positive Correlation ◽

Positive Correlation ◽

Nutrient Levels ◽

Seed Sowing

A study was carried out using three maize genotypes with three levels of nitrogen (30 kg, 60 kg and 120 kg per hectare) during the summer season of 2010 and 2011with the aim of predicting maize (Zea mays L.) yield through the Normalized difference vegetation index (NDVI). The NDVI was recorded at different times throughout the growing season using a Greenseeker™ handheld sensor. Significant effect of genotypes and nutrient levels on the NDVI was observed at different growth stages of maize. There was positive correlation between the NDVI and grain yield. In the first season, the correlation coefficients were 0.90, 0.92, 0.76 and 0.73, respectively at 15, 45, 75 and 110 days after seeding. In the second season, the correlation coefficients were 0.80, 0.92, 0.77 and 0.75 respectively at 15, 45, 75 and 110 days after seeding. The NDVI based N calculator showed that irrespective of genotypes, yield potentials under farmers' levels of nutrient management were almost half of the recommended doses of nitrogen. The amount of N to be top dressed decreased with increased crop duration. Grain yield varied significantly due to season, genotypes and nutrient levels. NDVI was affected due to season, stages of the crop (DAS), genotypes and nutrient levels. Interaction effects were significant for season x genotype, growth stage x genotype, growth stage x nutrient levels, genotype x nutrient levels and genotype x growth stage x nutrient levels. There was a strong positive correlation between NDVI and grain yields of hybrid maize at 15 and 45 DAS, but this correlation declined thereafter. This means that N top-dressed at or after 75 days of seed sowing will not increase grain yield as significantly as N applied earlier in the season. In contrast, topdressed N was producing significant effects on the open pollinated Rampur Composite even after 75 days of seed sowing. Further confirmation of the finding could be useful for top dressing N in the maize crop. Agronomy Journal of Nepal (Agron JN) Vol. 3. 2013, Page 82-88 DOI: http://dx.doi.org/10.3126/ajn.v3i0.9009

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Hyperspectral field reflectance measurements to estimate wheat grain yield and plant height

Scientia Agricola ◽

10.1590/s0103-90162006000200004 ◽

2006 ◽

Vol 63 (2) ◽

pp. 130-138 ◽

Cited By ~ 25

Author(s):

Alexandre Cândido Xavier ◽

Bernardo Friedrich Theodor Rudorff ◽

Mauricio Alves Moreira ◽

Brummer Seda Alvarenga ◽

José Guilherme de Freitas ◽

...

Keyword(s):

Grain Yield ◽

Plant Height ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Narrow Band ◽

Broad Band ◽

Growth Stages ◽

Sensing Applications ◽

Field Reflectance ◽

Reflectance Measurements

Hyperspectral crop reflectance data are useful for several remote sensing applications in agriculture, but there is still a need for studies to define optimal wavebands to estimate crop biophysical parameters. The objective of this work is to analyze the use of narrow and broad band vegetation indices (VI) derived from hyperspectral field reflectance measurements to estimate wheat (Triticum aestivum L.) grain yield and plant height. A field study was conducted during the winter growing season of 2003 in Campinas, São Paulo State, Brazil. Field canopy reflectance measurements were acquired at six wheat growth stages over 80 plots with four wheat cultivars (IAC-362, IAC-364, IAC-370, and IAC-373), five levels of nitrogen fertilizer (0, 30, 60, 90, and 120 kg of N ha-1) and four replicates. The following VI were analyzed: a) hyperspectral or narrow-band VI (1. optimum multiple narrow-band reflectance, OMNBR; 2. narrow-band normalized difference vegetation index, NB_NDVI; 3. first- and second-order derivative of reflectance; and 4. four derivative green vegetation index); and b) broad band VI (simple ratio, SR; normalized difference vegetation index, NDVI; and soil-adjusted vegetation index, SAVI). Hyperspectral indices provided an overall better estimate of biophysical variables when compared to broad band VI. The OMNBR with four bands presented the highest R² values to estimate both grain yield (R² = 0.74; Booting and Heading stages) and plant height (R² = 0.68; Heading stage). Best results to estimate biophysical variables were observed for spectral measurements acquired between Tillering II and Heading 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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Association between physiological parameters and yield in Triticum aestivum L. genotypes under rainfed conditions

Agricultural Science and Technology ◽

10.15547/ast.2020.02.018 ◽

2020 ◽

Vol 12 (2) ◽

pp. 107-113

Author(s):

İ. Öztürk

Keyword(s):

Grain Yield ◽

Chlorophyll Content ◽

Plant Height ◽

Bread Wheat ◽

Growth Phase ◽

Canopy Temperature ◽

Physiological Parameters ◽

Growth Stages ◽

Wheat Genotypes ◽

Rainfed Conditions

Abstract. The purpose of the study was to assess the relationships between physiological parameters and grain yield of different bread wheat genotypes. In the present research a total of 25 bread wheat genotypes were tested during the 2016-2017 seasons under rainfed conditions. The experiment was conducted in a randomized complete blocks design with four replications. Grain yield, days of heading, plant height, biomass (NDVI) from GS25 up to GS85 growth stage, chlorophyll content (SPAD) during the heading stage, canopy temperature (CT) at GS60 and GS75 growth stages, and glaucousness were investigated. The results of variance analyses showed that there were significant differences (p<0.01) among genotypes for yield. The mean grain yield was 7948 kg ha-1 and yield ranged from 7033 kg ha-1 to 8759 kg ha-1, the highest grain yield performed by TE6744-16 line. According to the results, significant differences among cultivars in terms of plant height, days of heading, biomass, chlorophyll content, canopy temperature, glaucousness were found. TE6627-6 line had the highest chlorophyll content and also, chlorophyll content positively affected grain yield. Canopy temperature is generally related to yield under drought stress condition in bread wheat. In the study early maturing (days of heading) genotypes had lower canopy temperature. An increase in biomass after the heading phase has positively affected grain yield. In the study, no correlation was found between grain yield and biomass at GS25 and GS45 growth phase. There was a negative correlation between glaucousness with biomass at GS60, GS75 and GS85 growth phase. These results showed that physiological parameters such as biomass (at GS75 and GS85), canopy temperature (at GS60 and GS75), and chlorophyll content (at GS60), and glaucousness could be used for selection parameters under rainfed conditions for yield in bread wheat.

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Selective chemical control of capeweed in wheat and oats

Australian Journal of Experimental Agriculture ◽

10.1071/ea9720060 ◽

1972 ◽

Vol 12 (54) ◽

pp. 60 ◽

Cited By ~ 1

Author(s):

TG Reeves ◽

JM Lumb

Keyword(s):

Grain Yield ◽

Chemical Control ◽

Early Growth ◽

Crop Damage ◽

Growth Stages ◽

North Eastern ◽

Selective Chemical

In eight experiments conducted in north-eastern Victoria from 1965-1970, a range of herbicides were tested for selective post-emergence control of capeweed (Arctotheca calendula) in wheat and oats. Linuron, diquat, and bromoxynil were the best treatments tested and generally reduced capeweed density when applied in the early growth stages, causing little or no crop damage. Significant grain yield increases from spraying were obtained in each experiment with wheat, but in oats only one significant increase was obtained. Post-tillering treatments with 2,4-D and picloram plus 2,4-D did not increase grain yield.

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Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale

Agronomy ◽

10.3390/agronomy10111842 ◽

2020 ◽

Vol 10 (11) ◽

pp. 1842

Author(s):

Ewa Panek ◽

Dariusz Gozdowski ◽

Michał Stępień ◽

Stanisław Samborski ◽

Dominik Ruciński ◽

...

Keyword(s):

Winter Wheat ◽

Grain Yield ◽

Precision Agriculture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Correlation Coefficients ◽

Soil Nutrient ◽

Growth Stages ◽

Plant Growth Stages

The aims of this study were to: (i) evaluate the relationships between vegetation indices (VIs) derived from Sentinel-2 imagery and grain yield (GY) and the number of spikes per square meter (SN) of winter wheat and triticale; (ii) determine the dates and plant growth stages when the above relationships were the strongest at individual field scale, thus allowing for accurate yield prediction. Observations of GY and SN were performed at harvest on six fields (three locations in two seasons: 2017 and 2018) in three regions of Poland, i.e., northeastern (A—Brożówka), central (B—Zdziechów) and southeastern Poland (C—Kryłów). Vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), modified SAVI (mSAVI), modified SAVI 2 (mSAVI2), Infrared Percentage Vegetation Index (IPVI), Global Environmental Monitoring Index (GEMI), and Ratio Vegetation Index (RVI)) calculated for sampling points from mid-March until mid-July, covering within-field soil and topographical variability, were included in the analysis. Depending on the location, the highest correlation coefficients (of about 0.6–0.9) for most of VIs with GY and SN were obtained about 4–6 weeks before harvest (from the beginning of shooting to milk maturity). Therefore, satellite-derived VIs are useful for the prediction of within-field cereal GY as well as SN variability. Information on GY, predicted together with the results for soil nutrient availability, is the basis for the formulation of variable fertilize rates in precision agriculture. All examined VIs were similarly correlated with GY and SN via the commonly used NDVI. The increase in NDVI by 0.1 unit was related to an average increase in GY by about 2 t ha−1.

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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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LIMITES CRÍTICOS DE NDVI PARA ESTIMATIVA DO POTENCIAL PRODUTIVO DO MILHO

Revista Brasileira de Milho e Sorgo ◽

10.18512/1980-6477/rbms.v17n1p91-100 ◽

2018 ◽

Vol 17 (1) ◽

pp. 91 ◽

Cited By ~ 3

Author(s):

ANDRÉ LUIS VIAN ◽

CHRISTIAN BREDEMEIER ◽

PAULO REGIS FERREIRA DA SILVA ◽

ANTÔNIO LUIS SANTI ◽

CECÍLIA PAZ GIORDANO DA SILVA ◽

...

Keyword(s):

Zea Mays ◽

Grain Yield ◽

Avena Sativa ◽

Vegetation Index ◽

Agricultural Practices ◽

Growth Cycle ◽

Yield Potential ◽

Growth Stages ◽

Different Growth Stages ◽

Critical Limits

RESUMO - A estimativa do potencial produtivo da cultura do milho ao longo do ciclo de desenvolvimento é uma das novas práticas agrícolas que vêm sendo utilizadas para qualificar o manejo da cultura. Neste sentido, destaca-se a inserção de sensores de vegetação, com a finalidade de realizar o monitoramento do desenvolvimento e da condição nutricional da cultura ao longo do seu ciclo. O objetivo do presente trabalho foi determinar os limites críticos do Índice de vegetação por diferença normalizada (NDVI) para a determinação de classes de potencial produtivo do milho em diferentes estádios fenológicos, utilizando sensor óptico ativo de vegetação (Greenseeker). O experimento foi conduzido na EEA/UFRGS, durante a safra agrícola 2013/2014. Os tratamentos consistiram de diferentes épocas de dessecação da aveia branca (Avena sativa L.) antes da semeadura da cultura do milho. As avaliações com o sensor óptico ativo foram realizadas nos estádios fenológicos V3, V5, V6, V7 e V8. Os resultados mostraram que o NDVI medido pelo sensor Greenseeker foi eficiente na predição da produtividade de milho em diferentes estádios fenológicos. Os limites críticos de NDVI, os quais correspondem a diferentes classes de potencial produtivo, podem ser identificados de maneira rápida e precisa entre os estádios fenológicos V3 a V8 e esta informação pode ser empregada para a adubação nitrogenada em taxa variável de acordo com o potencial produtivo estimado. Palavras-chave: Greenseeker, índice de vegetação, Zea mays, NDVI. CRITICAL LIMITS OF NDVI FOR YIELD POTENTIAL ESTIMATION IN MAIZE ABSTRACT - The estimation of grain yield potential of maize along the growth cycle is one of new agricultural practices that have been used to qualify crop management. In this sense, the use of vegetation sensors can be highlighted, in order to carry out the monitoring of plant development and nutritional condition throughout its development. The objective of this study was to indicate NDVI critical limits for determining grain yield potential classes of maize in different growth stages using an active optical vegetation sensor (Greenseeker). The experiment was carried out in the 2013/14 growing season, in Eldorado do Sul, State of Rio Grande do Sul, southern Brazil. Treatments consisted of different dissecation timing of oat (Avena sativa L.) before corn sowing. Evaluations with the active optical sensor were done at growth stages V3, V5, V6, V7, and V8. Results showed that NDVI measured by the sensor Greenseeker was efficient in identifying maize grain productivity at different growth stages. NDVI critical limits that correspond to different yield potential levels in maize can be quickly and precisely identified between V3 and V8 growth stages and this information can be used for site-specific nitrogen fertilization according to the estimated yield potential. Keywords: Greenseeker, vegetation index, Zea mays, NDVI.

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