scholarly journals Effects of Growth Stage Development on Paddy Rice Leaf Area Index Prediction Models

2019 ◽  
Vol 11 (3) ◽  
pp. 361 ◽  
Author(s):  
Li Wang ◽  
Qingrui Chang ◽  
Fenling Li ◽  
Lin Yan ◽  
Yong Huang ◽  
...  
Keyword(s):  
Growth Stage ◽  
Vegetation Index ◽  
Multivariate Calibration ◽  
Growing Season ◽  
Paddy Rice ◽  
Growth Stages ◽  
Area Index ◽  
First Derivative ◽  
Derivative Spectra ◽  
Stage Development

A in situ hyperspectral dataset containing multiple growth stages over multiple growing seasons was used to build paddy rice leaf area index (LAI) estimation models with a special focus on the effects of paddy rice growth stage development. The univariate regression method applied to the vegetation index (VI), the traditional multivariate calibration method of partial least squares regression (PLSR), and modern machine learning methods such as support vector regression (SVR), random forests (RF), and artificial neural networks (ANN) based on the original and first-derivative hyperspectral data were evaluated in this study for paddy rice LAI estimation. All the models were built on the whole growing season and on each separate vegetative, reproductive and ripening growth stage of paddy rice separately. To ensure a fair comparison, the models of the whole growing season were also validated on data for each separate growth stage of the standalone validation dataset. Moreover, the optimal band pairs for calculating narrowband difference vegetative index (DVI), normalized difference vegetation index (NDVI) and simple ratio vegetation index (SR) were determined for the whole growing season and for each separate growth stage separately. The results showed that for both the whole growing season and for each single growth stage, the red-edge and near-infrared band pairs are optimal for formulating the narrowband DVI, NDVI and SR. Among the four multivariate calibration methods, SVR and RF yielded more accurate results than the other two methods. The SVR and RF models built on first-derivative spectra provided more accurate results than the corresponding models on the original spectra for both whole growing season models and separate growth stage models. Comparing the prediction accuracy based on the whole growing season revealed that the RF and SVR models showed an advantage over the VI models. However, comparing the prediction accuracy based on each growth stage separately showed that the VI models provided more accurate results for the vegetative growth stages. The SVR and RF models provided more accurate results for the ripening growth stage. However, the whole growing season RF model on first-derivative spectra could provide reasonable accuracy for each single growth stage.

scholarly journals Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season

2021 ◽  
Vol 13 (15) ◽  
pp. 3001
Author(s):  
Kaili Yang ◽  
Yan Gong ◽  
Shenghui Fang ◽  
Bo Duan ◽  
Ningge Yuan ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Morphological Changes ◽  
Texture Features ◽  
Growing Season ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Spectral Information ◽  
Growth Stages ◽  
Area Index ◽  
Texture Information

Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most widely used method in LAI estimation because of its fast speed and simple calculation. However, VI only reflects the spectral information and ignores the texture information of images, so it is difficult to adapt to the unique and complex morphological changes of rice in different growth stages. In this study we put forward a novel method by combining the texture information derived from the local binary pattern and variance features (LBP and VAR) with the spectral information based on VI to improve the estimation accuracy of rice LAI throughout the entire growing season. The multitemporal images of two study areas located in Hainan and Hubei were acquired by a 12-band camera, and the main typical bands for constituting VIs such as green, red, red edge, and near-infrared were selected to analyze their changes in spectrum and texture during the entire growing season. After the mathematical combination of plot-level spectrum and texture values, new indices were constructed to estimate rice LAI. Comparing the corresponding VI, the new indices were all less sensitive to the appearance of panicles and slightly weakened the saturation issue. The coefficient of determination (R2) can be improved for all tested VIs throughout the entire growing season. The results showed that the combination of spectral and texture features exhibited a better predictive ability than VI for estimating rice LAI. This method only utilized the texture and spectral information of the UAV image itself, which is fast, easy to operate, does not need manual intervention, and can be a low-cost method for monitoring crop growth.

scholarly journals Estimation of Paddy Rice Nitrogen Content and Accumulation Both at Leaf and Plant Levels from UAV Hyperspectral Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 2956
Author(s):  
Li Wang ◽  
Shuisen Chen ◽  
Dan Li ◽  
Chongyang Wang ◽  
Hao Jiang ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Regression Models ◽  
Growth Stage ◽  
Vegetation Index ◽  
Leaf Nitrogen ◽  
Paddy Rice ◽  
Growth Stages ◽  
Nitrogen Accumulation ◽  
Plant Nitrogen ◽  
Stage Models

Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-based hyperspectral images. Additionally, the effects of the growth stage were evaluated. Univariate regression models on vegetation indices (VIs), the traditional multivariate calibration method, partial least squares regression (PLSR) and modern machine learning (ML) methods, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM), were evaluated both over the whole growing season and in each single growth stage (including the tillering, jointing, booting and heading growth stages). The results indicate that the correlation between the four nitrogen traits and the other three biochemical traits—leaf chlorophyll content, canopy chlorophyll content and aboveground biomass—are affected by the growth stage. Within a single growth stage, the performance of selected VIs is relatively constant. For the full-growth-stage models, the performance of the VI-based models is more diverse. For the full-growth-stage models, the transformed chlorophyll absorption in the reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI) performs best for LNC, PNC and PNA estimation, while the three band vegetation index (TBVITian) performs best for LNA estimation. There are no obvious patterns regarding which method performs the best of the PLSR, ANN, RF and SVM in either the growth-stage-specific or full-growth-stage models. For the growth-stage-specific models, a lower mean relative error (MRE) and higher R2 can be acquired at the tillering and jointing growth stages. The PLSR and ML methods yield obviously better estimation accuracy for the full-growth-stage models than the VI-based models. For the growth-stage-specific models, the performance of VI-based models seems optimal and cannot be obviously surpassed. These results suggest that building linear regression models on VIs for paddy rice nitrogen traits estimation is still a reasonable choice when only a single growth stage is involved. However, when multiple growth stages are involved or missing the phenology information, using PLSR or ML methods is a better option.

scholarly journals RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

2020 ◽  
Vol 12 (18) ◽  
pp. 2982 ◽  
Author(s):  
Christelle Gée ◽  
Emmanuel Denimal
Keyword(s):  
Biomass Production ◽  
Precision Agriculture ◽  
Weed Management ◽  
Vegetation Index ◽  
Management Strategies ◽  
Weed Competition ◽  
Growth Stages ◽  
Area Index ◽  
Crop Weed Competition ◽  
The Impact

In precision agriculture, the development of proximal imaging systems embedded in autonomous vehicles allows to explore new weed management strategies for site-specific plant application. Accurate monitoring of weeds while controlling wheat growth requires indirect measurements of leaf area index (LAI) and above-ground dry matter biomass (BM) at early growth stages. This article explores the potential of RGB images to assess crop-weed competition in a wheat (Triticum aestivum L.) crop by generating two new indicators, the weed pressure (WP) and the local wheat biomass production (δBMc). The fractional vegetation cover (FVC) of the crop and the weeds was automatically determined from the images with a SVM-RBF classifier, using bag of visual word vectors as inputs. It is based on a new vegetation index called MetaIndex, defined as a vote of six indices widely used in the literature. Beyond a simple map of weed infestation, the map of WP describes the crop-weed competition. The map of δBMc, meanwhile, evaluates the local wheat above-ground biomass production and informs us about a potential stress. It is generated from the wheat FVC because it is highly correlated with LAI (r2 = 0.99) and BM (r2 = 0.93) obtained by destructive methods. By combining these two indicators, we aim at determining whether the origin of the wheat stress is due to weeds or not. This approach opens up new perspectives for the monitoring of weeds and the monitoring of their competition during crop growth with non-destructive and proximal sensing technologies in the early stages of development.

scholarly journals The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4937 ◽  
Author(s):  
Ziqing Xia ◽  
Yiping Peng ◽  
Shanshan Liu ◽  
Zhenhua Liu ◽  
Guangxing Wang ◽  
...  
Keyword(s):  
Regression Models ◽  
Vegetation Index ◽  
Southern China ◽  
Regional Scale ◽  
Growth Stages ◽  
Maturity Stage ◽  
Cultivated Land ◽  
Flowering Stage ◽  
Area Index ◽  
Land Quality

This study proposes a method for determining the optimal image date to improve the evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference vegetation index (DVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI) are first retrieved using the PROSAIL model and Gaofen-1 (GF-1) images. The indices are then introduced into four regression models at different growth stages for assessing CLQ. The optimal image date of CLQ evaluation is finally determined according to the root mean square error (RMSE). This method is tested and validated in a rice growth area of Southern China based on 115 sample plots and five GF-1 images acquired at the tillering, jointing, booting, heading to flowering, and milk ripe and maturity stage of rice in 2015, respectively. The results show that the RMSEs between the measured and estimated CLQ from four vegetation index-based regression models at the heading to flowering stage are smaller than those at the other growth stages, indicating that the image date corresponding with the heading to flowering stage is optimal for CLQ evaluation. Compared with other vegetation index-based models, the LAI-based logarithm model provides the most accurate estimates of CLQ. The optimal model is also driven using the GF-1 image at the heading to flowering stage to map CLQ of the study area, leading to a relative RMSE of 14.09% at the regional scale. This further implies that the heading to flowering stage is the optimal image time for evaluating CLQ. This study is the first effort to provide an applicable method of selecting the optimal image date to improve the estimation of CLQ and thus advanced the literature in this field.

Relative effectiveness of various sources, methods, times and rates of copper fertilizers in improving grain yield of wheat on a Cu-deficient soil

2005 ◽  
Vol 85 (1) ◽  
pp. 59-65 ◽  
Author(s):  
S. S. Malhi ◽  
L. Cowell ◽  
H. R. Kutcher
Keyword(s):  
Grain Yield ◽  
Protein Concentration ◽  
Growth Stage ◽  
Foliar Application ◽  
Leaf Growth ◽  
Flag Leaf ◽  
Relative Effectiveness ◽  
Growing Season ◽  
Growth Stages ◽  
Cu Deficiency

A field experiment was conducted to determine the relative effectiveness of various sources, methods, times and rates of Cu fertilizers on grain yield, protein concentration in grain, concentration of Cu in grain and uptake of Cu in grain of wheat (Triticum aestivum L.), and residual concentration of DTPA-extractable Cu in soil on a Cu-deficient soil near Porcupine Plain in northeastern Saskatchewan. The experiment was conducted from 1999 to 2002 on the same site, but the results for 2002 were not presented because of very low grain yield due to drought in the growing season. The 25 treatments included soil application of four granular Cu fertilizers (Cu lignosulphonate, Cu sulphate, Cu oxysulphate I and Cu oxysulphate II) as soil-incorporated (at 0.5 and 2.0 kg Cu ha-1), seedrow-placed (at 0.25 and 1.0 kg Cu ha-1) and foliar application of four solution Cu fertilizers (Cu chelate-EDTA, Cu sequestered I, Cu sulphate/chelate and Cu sequestered II at 0.25 kg Cu ha-1) at the four-leaf and flag-leaf growth stages, plus a zero-Cu check. Soil was tilled only once to incorporate all designated Cu and blanket fertilizers into the soil a few days prior to seeding. Wheat plants in the zero-Cu treatment exhibited Cu deficiency in all years. For foliar application at the flag-leaf stage, grain yield increased with all four of the Cu fertilizers in 2000 and 2001, and in all but Cu sequestered II in 1999. Foliar application at the four-leaf growth stage of three Cu fertilizers (Cu chelate-EDTA, Cu sequestered I and Cu sulphate/chelate), soil incorporation of all Cu fertilizers at 2 kg Cu ha-1 and two Cu fertilizers (Cu lignosulphonate and Cu sulphate) at 0.5 kg Cu ha-1 rate, and seedrow placement of two Cu fertilizers (Cu lignosulphonate and Cu sulphate) at 1 kg Cu ha-1 increased grain yield of wheat only in 2001. There was no effect of Cu fertilization on protein concentration in grain. The increase in concentration and uptake of Cu in grain from Cu fertilization usually showed a trend similar to grain yield. There was some increase in residual DTPA-extractable Cu in the 0–60 cm soil in Cu lignosulphonate, Cu sulphate and Cu oxysulphate II soil incorporation treatments, particularly at the 2 kg Cu ha-1 rate. In summary, the results indicate that foliar application of Cu fertilizers at the flag-leaf growth stage can be used for immediate correction of Cu deficiency in wheat. Because Cu deficiency in crops often occurs in irregular patches within fields, foliar application may be the most practical and economical way to correct Cu deficiency during the growing season, as lower Cu rates can correct Cu deficiency. Key words: Application time, Cu source, foliar application, granular Cu, growth stage, placement method, rate of Cu, seedrow-placed Cu, soil incorporation

The effect of foliar copper application on grain yield and quality of wheat

2004 ◽  
Vol 84 (1) ◽  
pp. 47-56 ◽  
Author(s):  
R. E. Karamanos ◽  
Q. Pomarenski ◽  
T. B. Goh ◽  
N. A Flore
Keyword(s):  
Grain Yield ◽  
Growth Stage ◽  
Foliar Application ◽  
Growing Season ◽  
Quality Parameters ◽  
Growth Stages ◽  
Appreciable Effect ◽  
Cu Deficiency ◽  
Seed Placement ◽  
Grain Yield And Quality

Available Cu concentrations in prairie soils (DTPA-extractable Cu) are extremely variable, thus resulting in areas within fields that are Cu deficient. These areas are difficult to characterize by a soil test based on a composite field sample; thus, when they are identified in the growing season, foliar Cu application possibly represents the only method of correcting them. A project, carried out over a period of 8 yr that consisted of four experiments and a total of 22 trials, was designed to ascertain whether foliar Cu applications indeed provide a satisfactory means of correcting Cu deficiency. Experiments included comparison of foliar applications at Feekes growth stages 6 (first node of stem visible at base of shoot) and 6 plus 10 (sheath of last leaf completely grown out) to soil broadcast and incorporation of 4 to 5.5 kg Cu ha-1 as copper sulphate (CuSO4·5H2O) or seed placement of 2 kg Cu ha-1 in three forms (two oxysulphates and one sulphate); foliar application of a variety of products representing different chemistries (chelate, lignin sulphonate, humic acid, oxychloride and citric acid) on a number of wheat cultivars at Feekes growth stage 10 or one cultivar at Feekes growth stages 2 (beginning of tillering), 6, 10 and 2 plus 10. Foliar applications appear to provide a solution to Cu deficiency that is identified during the growing season. However, foliar applications were not always as effective as broadcast and incorporation of at least 4 kg Cu ha-1 in the form of CuSO4·5H2O, which still remains the preferred method to correct a Cu deficiency. Foliar application at Feekes growth stage 2 was ineffective, whereas a single foliar application at Feekes growth stage 10 was not as satisfactory as a single one at Feekes growth stage 6. Thus, the latter stage appears to be preferable; however, maximum grain yield in some cases was obtained by the combination of two foliar Cu applications, one each at Feekes growth stages 6 and 10. Responses of wheat to foliar Cu application were obtained on soils that contained DTPA-extractable Cu concentration of less than 0.4 mg kg-1. Foliar Cu applications did not have an appreciable effect on grain quality parameters, such as hectolitre weight, moisture or protein content. Key words: DTPA-extractable, Feekes growth stage, deficient, marginal, plant tissue

scholarly journals Effect of Rhizobium inoculation of seeds and foliar fertilization on productivity of Pisum sativum L.

2013 ◽  
Vol 66 (2) ◽  
pp. 71-78 ◽  
Author(s):  
Tadeusz Zając ◽  
Agnieszka Klimek-Kopyra ◽  
Andrzej Oleksy
Keyword(s):  
Pisum Sativum ◽  
Photosynthetically Active Radiation ◽  
Vegetation Index ◽  
Foliar Application ◽  
Yield Potential ◽  
Grain Legume ◽  
Growth Stages ◽  
Area Index ◽  
Active Radiation ◽  
Experimental Field

Pea (<em>Pisum sativum</em> L.) is the second most important grain legume crop in the world which has a wide array of uses for human food and fodder. One of the major factors that determines the use of field pea is the yield potential of cultivars. Presently, pre-sowing inoculation of pea seeds and foliar application of microelement fertilizers are prospective solutions and may be reasonable agrotechnical options. This research was undertaken because of the potentially high productivity of the 'afila' morphotype in good wheat complex soils. The aim of the study was to determine the effect of vaccination with <em>Rhizobium</em> and foliar micronutrient fertilization on yield of the afila pea variety. The research was based on a two-year (2009–2010) controlled field experiment, conducted in four replicates and carried out on the experimental field of the Bayer company located in Modzurów, Silesian region. experimental field soil was Umbrisol – slightly degraded chernozem, formed from loess. Nitragina inoculant, as a source of symbiotic bacteria, was applied before sowing seeds. Green area index (GAI) of the canopy, photosynthetically active radiation (PAR), and normalized difference vegetation index (NDVI) were determined at characteristic growth stages. The presented results of this study on symbiotic nitrogen fixation by leguminous plants show that the combined application of Nitragina and Photrel was the best combination for productivity. Remote measurements of the pea canopy indexes indicated the formation of the optimum leaf area which effectively used photosynthetically active radiation. The use of Nitragina as a donor of effective <em>Rhizobium</em> for pea plants resulted in slightly higher GAI values and the optimization of PAR and NDVI. It is not recommended to use foliar fertilizers or Nitragina separately due to the slowing of pea productivity.

Comparison of UAV RGB Imagery and Hyperspectral Remote-sensing Data for Monitoring Winter-wheat Growth

2021 ◽  
Author(s):  
Haikuan Feng ◽  
Huilin Tao ◽  
Chunjiang Zhao ◽  
Zhenhai Li ◽  
Guijun Yang
Keyword(s):  
Remote Sensing ◽  
Spectral Index ◽  
Vegetation Index ◽  
Crop Growth ◽  
Hyperspectral Data ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Estimation Model ◽  
Area Index ◽  
Simple Ratio

Abstract Background: Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unnamed aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. Results: To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into a new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results show that (1) the RGB-imagery indices red reflectance (r), the excess-red index(EXR), the vegetation atmospherically resistant index(VARI), and the modified green-red vegetation index(MGRVI) , as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index(MSR), the simple ratio vegetation index(SR), and the normalized difference vegetation index (NDVI)are more strongly correlated with the CGI than a single growth-monitoring indicator (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stage in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.Conclusions: UAVs carrying RGB cameras and hyperspectral cameras have high inversion CGI accuracy and can judge the overall growth of wheat can provide a reference for monitoring the growth of wheat.

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

2013 ◽  
Vol 3 ◽  
pp. 82-88 ◽  
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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