scholarly journals Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery

2020 ◽  
Vol 12 (19) ◽  
pp. 3228 ◽  
Author(s):  
Zhengchao Qiu ◽  
Haitao Xiang ◽  
Fei Ma ◽  
Changwen Du
Keyword(s):  
Vegetation Index ◽  
Estimation Accuracy ◽  
Growth Stages ◽  
Estimation Model ◽  
Area Index ◽  
Rice Growth ◽  
Jointing Stage ◽  
Model Database ◽  
Different Growth Stages ◽  
Growth Indicators

The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.

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 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 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 Modification of Difference Vegetation Index (DVI) in middle and late growing period of winter wheat and its application in soil moisture inversion

2019 ◽  
Vol 131 ◽  
pp. 01098
Author(s):  
Zhang Hong-wei ◽  
Huai-liang Chen ◽  
Fei-na Zha
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Vegetation Coverage ◽  
Growth Stages ◽  
Wheat Growth ◽  
Inversion Result ◽  
Growing Period ◽  
The Difference ◽  
Different Growth Stages

In the middle and late growing period of winter wheat, soil moisture is easily affected by saturation when using MODIS data to retrieve soil moisture. In this paper, in order to reduce the effect of the saturation caused by increasing vegetation coverage in middle and late stage of winter wheat, the Difference Vegetation Index (DVI) model was modified with different coefficients in different growth stages of winter wheat based on MODIS spectral data and LAI characteristics of variation. LAI was divided into three stages, LAI ≤ 1 < LAI ≤, 3 < LAI, and the adjusting coefficient of α=1, α=3, α=5, were taken to modifying the Difference Vegetation Index(DVI). The results show that the Modified Difference Vegetation Index (MDVIα) can effectively reduce the interference of saturation, and the inversion result of soil moisture in the middle and late period of winter wheat growth is obviously superior to the uncorrected inversion model of DVI.

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.

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.

scholarly journals Changes of soil moisture during apple growth based on TVDI index

2021 ◽  
pp. 955-961
Author(s):  
Hui Kong ◽  
Dan Wu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Vegetation Index ◽  
Growth Stages ◽  
Temporal Consistency ◽  
Vegetation Water Content ◽  
Content Index ◽  
Research Findings ◽  
Vegetation Water ◽  
Different Growth Stages

Based on MODIS data, soil moisture data and field survey data from 2014 to 2018, the consistency of temperature vegetation drought index (TVDL), normalized vegetation water content index (NDWL), vegetation water supply index (VSWI) and soil moisture at 15cm depth (SM) in apple growth in Fuxian county was investigated. Results showed that the spatial and temporal consistency between VSWI and SM calculated by the enhanced vegetation index (EVI) was best; the sensitivity of remote sensing indexes to soil moisture was different in different apple growth stages. The sensitivity of VSWI was the most obvious in different growth stages, and the sensitivity of soil moisture was higher than that of germination, flowering, fruit expansion and maturity. The research findings were consistent with the law of water demand in different growth stages of apple in Fuxian county and the characteristics of precipitation and drought in Fuxian county. The present results could provide a reference for soil moisture monitoring of apple growth by remote sensing. Bangladesh J. Bot. 50(3): 955-961, 2021 (September) Special

Leaf area durations and grain yields of two tropical rice varieties

1968 ◽  
Vol 8 (34) ◽  
pp. 587
Author(s):  
PC Owen
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Photosynthetic Efficiency ◽  
Temperate Climate ◽  
Growth Stages ◽  
Rice Varieties ◽  
Area Index ◽  
Grain Yields ◽  
Different Growth Stages

A series of differing leaf area index regimes during the growth of two tropical rice varieties was produced by partial defoliation at different growth stages. In addition, part of the crop was completely defoliated after panicle emergence. Comparison of the effects of the range of leaf area durations (D) thus produced showed that these rice varieties differed from temperate climate cereals. Grain yields were least associated with D after panicle emergence, but were most influenced by D before emergence. This effect is mainly via an influence upon the number of spikelets formed per panicle. Grain : leaf ratio, a measure of photosynthetic efficiency, was considerably lower than values reported for wheat.

scholarly journals A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index

2019 ◽  
Vol 12 (1) ◽  
pp. 16 ◽  
Author(s):  
Naichen Xing ◽  
Wenjiang Huang ◽  
Qiaoyun Xie ◽  
Yue Shi ◽  
Huichun Ye ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Saturation Effect ◽  
Estimation Accuracy ◽  
Growth Monitoring ◽  
Area Index ◽  
Multispectral Data ◽  
Red Edge

Leaf area index (LAI) is a key parameter in plant growth monitoring. For several decades, vegetation indices-based empirical method has been widely-accepted in LAI retrieval. A growing number of spectral indices have been proposed to tailor LAI estimations, however, saturation effect has long been an obstacle. In this paper, we classify the selected 14 vegetation indices into five groups according to their characteristics. In this study, we proposed a new index for LAI retrieval-transformed triangular vegetation index (TTVI), which replaces NIR and red bands of triangular vegetation index (TVI) into NIR and red-edge bands. All fifteen indices were calculated and analyzed with both hyperspectral and multispectral data. Best-fit models and k-fold cross-validation were conducted. The results showed that TTVI performed the best predictive power of LAI for both hyperspectral and multispectral data, and mitigated the saturation effect. The R2 and RMSE values were 0.60, 1.12; 0.59, 1.15, respectively. Besides, TTVI showed high estimation accuracy for sparse (LAI < 4) and dense canopies (LAI > 4). Our study provided the value of the Red-edge bands of the Sentinel-2 satellite sensors in crop LAI retrieval, and demonstrated that the new index TTVI is applicable to inverse LAI for both low-to-moderate and moderate-to-high vegetation cover.

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