Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images

The accurate estimation of grain yield in rice breeding is crucial for breeders to screen and select qualified cultivars. In this study, a low-cost unmanned aerial vehicle (UAV) platform mounted with an RGB camera was carried out to capture high-spatial resolution images of rice canopy in rice breeding. The random forest (RF) regression techniques were used to establish yield models by using (1) only color vegetation indices (VIs), (2) only phenological data, and (3) fusion of VIs and phenological data as inputs, respectively. Then, the performances of RF models were compared with the manual observation and CERES-Rice model. The results indicated that the RF model using VIs only performed poorly for estimating yield; the optimized RF model that combined the use of phenological data and color VIs performed much better, which demonstrated that the phenological data significantly improved the model performance. Furthermore, the yield estimation accuracy of 21 rice cultivars that were continuously planted over three years in the optimal RF model had no significant difference (p > 0.05) with that of the CERES-Rice model. These findings demonstrate that the RF model, by combining phenological data and color Vis, is a potential and cost-effective way to estimate yield in rice breeding.

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Combined Use of Low-Cost Remote Sensing Techniques and δ13C to Assess Bread Wheat Grain Yield under Different Water and Nitrogen Conditions

Agronomy ◽

10.3390/agronomy9060285 ◽

2019 ◽

Vol 9 (6) ◽

pp. 285 ◽

Cited By ~ 4

Author(s):

Salima Yousfi ◽

Adrian Gracia-Romero ◽

Nassim Kellas ◽

Mohamed Kaddour ◽

Ahmed Chadouli ◽

...

Keyword(s):

Remote Sensing ◽

Grain Yield ◽

Water Status ◽

Low Cost ◽

Vegetation Indices ◽

Carbon Isotopic Composition ◽

Canopy Temperature ◽

N Fertilization ◽

Canopy Area ◽

Rgb Images

Vegetation indices and canopy temperature are the most usual remote sensing approaches to assess cereal performance. Understanding the relationships of these parameters and yield may help design more efficient strategies to monitor crop performance. We present an evaluation of vegetation indices (derived from RGB images and multispectral data) and water status traits (through the canopy temperature, stomatal conductance and carbon isotopic composition) measured during the reproductive stage for genotype phenotyping in a study of four wheat genotypes growing under different water and nitrogen regimes in north Algeria. Differences among the cultivars were reported through the vegetation indices, but not with the water status traits. Both approximations correlated significantly with grain yield (GY), reporting stronger correlations under support irrigation and N-fertilization than the rainfed or the no N-fertilization conditions. For N-fertilized trials (irrigated or rainfed) water status parameters were the main factors predicting relative GY performance, while in the absence of N-fertilization, the green canopy area (assessed through GGA) was the main factor negatively correlated with GY. Regression models for GY estimation were generated using data from three consecutive growing seasons. The results highlighted the usefulness of vegetation indices derived from RGB images predicting GY.

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Cotton Yield Estimation from UAV-Based Plant Height

Transactions of the ASABE ◽

10.13031/trans.13067 ◽

2019 ◽

Vol 62 (2) ◽

pp. 393-404 ◽

Cited By ~ 1

Author(s):

Aijing Feng ◽

Meina Zhang ◽

Kenneth A. Sudduth ◽

Earl D. Vories ◽

Jianfeng Zhou

Keyword(s):

Remote Sensing ◽

Plant Height ◽

Crop Yield ◽

Low Cost ◽

Reference Points ◽

Cotton Field ◽

Accurate Estimation ◽

Cotton Yield ◽

Yield Estimation ◽

Sensing System

Abstract. Accurate estimation of crop yield before harvest, especially in early growth stages, is important for farmers and researchers to optimize field management and evaluate crop performance. However, existing in-field methods for estimating crop yield are not efficient. The goal of this research was to evaluate the performance of a UAV-based remote sensing system with a low-cost RGB camera to estimate cotton yield based on plant height. The UAV system acquired images at 50 m above ground level over a cotton field at the first flower growth stage. Waypoints and flight speed were selected to allow >70% image overlap in both forward and side directions. Images were processed to develop a geo-referenced orthomosaic image and a digital elevation model (DEM) of the field that was used to extract plant height by calculating the difference in elevation between the crop canopy and bare soil surface. Twelve ground reference points with known height were deployed in the field to validate the UAV-based height measurement. Geo-referenced yield data were aligned to the plant height map based on GPS and image features. Correlation analysis between yield and plant height was conducted row-by-row with and without row registration. Pearson correlation coefficients between yield and plant height with row registration for all individual rows were in the range of 0.66 to 0.96 and were higher than those without row registration (0.54 to 0.95). A linear regression model using plant height was able to estimate yield with root mean square error of 550 kg ha-1 and mean absolute error of 420 kg ha-1. Locations with low yield were analyzed to identify the potential reasons, and it was found that water stress and coarse soil texture, as indicated by low soil apparent electricity conductivity (ECa), might contribute to the low yield. The findings indicate that the UAV-based remote sensing system equipped with a low-cost digital camera was potentially able to monitor plant growth status and estimate cotton yield with acceptable errors. Keywords: Cotton, Geo-registration, Plant height, UAV-based remote sensing, Yield estimation.

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Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique

Applied Sciences ◽

10.3390/app112412164 ◽

2021 ◽

Vol 11 (24) ◽

pp. 12164

Author(s):

Changchun Li ◽

Yilin Wang ◽

Chunyan Ma ◽

Weinan Chen ◽

Yacong Li ◽

...

Keyword(s):

Prediction Model ◽

Grain Yield ◽

Growth Stage ◽

Prediction Models ◽

Vegetation Indices ◽

Model Performance ◽

Grain Filling ◽

Growth Stages ◽

Wheat Grain ◽

Area Index

Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to improve the performance of the winter wheat grain yield (GY) prediction model. For this purpose, crop canopy hyperspectral reflectance and leaf area index (LAI) data were obtained at the jointing, flagging, anthesis and grain filling stages. In this case, 15 vegetation indices and LAI were used as input features of the elastic network to construct GY prediction models for single growth stage. Based on Stacking technique, the GY prediction results of four single growth stages were integrated to construct the ensemble learning framework. The results showed that vegetation indices coupled LAI could effectively overcome the spectral saturation phenomenon, the validated R2 of each growth stage was improved by 10%, 22.5%, 3.6% and 10%, respectively. The stacking method provided more stable information with higher prediction accuracy than the individual fertility results (R2 = 0.74), and the R2 of the model validation phase improved by 236%, 51%, 27.6%, and 12.1%, respectively. The study can provide a reference for GY prediction of other crops.

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Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles

Agriculture ◽

10.3390/agriculture10070277 ◽

2020 ◽

Vol 10 (7) ◽

pp. 277 ◽

Cited By ~ 3

Author(s):

Héctor García-Martínez ◽

Héctor Flores-Magdaleno ◽

Roberto Ascencio-Hernández ◽

Abdul Khalil-Gardezi ◽

Leonardo Tijerina-Chávez ◽

...

Keyword(s):

Neural Network ◽

Grain Yield ◽

Correlation Coefficient ◽

Plant Density ◽

Vegetation Indices ◽

Canopy Cover ◽

Relative Importance ◽

Yield Estimation ◽

Corn Grain ◽

Corn Grain Yield

Corn yields vary spatially and temporally in the plots as a result of weather, altitude, variety, plant density, available water, nutrients, and planting date; these are the main factors that influence crop yield. In this study, different multispectral and red-green-blue (RGB) vegetation indices were analyzed, as well as the digitally estimated canopy cover and plant density, in order to estimate corn grain yield using a neural network model. The relative importance of the predictor variables was also analyzed. An experiment was established with five levels of nitrogen fertilization (140, 200, 260, 320, and 380 kg/ha) and four replicates, in a completely randomized block design, resulting in 20 experimental polygons. Crop information was captured using two sensors (Parrot Sequoia_4.9, and DJI FC6310_8.8) mounted on an unmanned aerial vehicle (UAV) for two flight dates at 47 and 79 days after sowing (DAS). The correlation coefficient between the plant density, obtained through the digital count of corn plants, and the corn grain yield was 0.94; this variable was the one with the highest relative importance in the yield estimation according to Garson’s algorithm. The canopy cover, digitally estimated, showed a correlation coefficient of 0.77 with respect to the corn grain yield, while the relative importance of this variable in the yield estimation was 0.080 and 0.093 for 47 and 79 DAS, respectively. The wide dynamic range vegetation index (WDRVI), plant density, and canopy cover showed the highest correlation coefficient and the smallest errors (R = 0.99, mean absolute error (MAE) = 0.028 t ha−1, root mean square error (RMSE) = 0.125 t ha−1) in the corn grain yield estimation at 47 DAS, with the WDRVI index and the density being the variables with the highest relative importance for this crop development date. For the 79 DAS flight, the combination of the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), WDRVI, excess green (EXG), triangular greenness index (TGI), and visible atmospherically resistant index (VARI), as well as plant density and canopy cover, generated the highest correlation coefficient and the smallest errors (R = 0.97, MAE = 0.249 t ha−1, RMSE = 0.425 t ha−1) in the corn grain yield estimation, where the density and the NDVI were the variables with the highest relative importance, with values of 0.295 and 0.184, respectively. However, the WDRVI, plant density, and canopy cover estimated the corn grain yield with acceptable precision (R = 0.96, MAE = 0.209 t ha−1, RMSE = 0.449 t ha−1). The generated neural network models provided a high correlation coefficient between the estimated and the observed corn grain yield, and also showed acceptable errors in the yield estimation. The spectral information registered through remote sensors mounted on unmanned aerial vehicles and its processing in vegetation indices, canopy cover, and plant density allowed the characterization and estimation of corn grain yield. Such information is very useful for decision-making and agricultural activities planning.

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Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing

Remote Sensing ◽

10.3390/rs13173390 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3390

Author(s):

Fumin Wang ◽

Xiaoping Yao ◽

Lili Xie ◽

Jueyi Zheng ◽

Tianyue Xu

Keyword(s):

Remote Sensing ◽

Field Experiments ◽

Rice Yield ◽

Vegetation Indices ◽

Estimation Method ◽

Estimation Accuracy ◽

Spectral Information ◽

Growth Stages ◽

Yield Estimation ◽

First Derivative

Rice floret number per unit area as one of the key yield structure parameters is directly related to the final yield of rice. Previous studies paid little attention to the effect of the variations in vegetation indices (VIs) caused by rice flowering on rice yield estimation. Unmanned aerial vehicles (UAV) equipped with hyperspectral cameras can provide high spatial and temporal resolution remote sensing data about the rice canopy, providing possibilities for flowering monitoring. In this study, two consecutive years of rice field experiments were conducted to explore the performance of florescence spectral information in improving the accuracy of VIs-based models for yield estimates. First, the florescence ratio reflectance and florescence difference reflectance, as well as their first derivative reflectance, were defined and then their correlations with rice yield were evaluated. It was found that the florescence spectral information at the seventh day of rice flowering showed the highest correlation with the yield. The sensitive bands to yield were centered at 590 nm, 690 nm and 736 nm–748 nm, 760 nm–768 nm for the first derivative florescence difference reflectance, and 704 nm–760 nm for the first derivative florescence ratio reflectance. The florescence ratio index (FRI) and florescence difference index (FDI) were developed and their abilities to improve the estimation accuracy of models basing on vegetation indices at single-, two- and three-growth stages were tested. With the introduction of florescence spectral information, the single-growth VI-based model produced the most obvious improvement in estimation accuracy, with the coefficient of determination (R2) increasing from 0.748 to 0.799, and the mean absolute percentage error (MAPE) and the root mean squared error (RMSE) decreasing by 11.8% and 10.7%, respectively. Optimized by flowering information, the two-growth stage VIs-based model gave the best performance (R2 = 0.869, MAPE = 3.98%, RMSE = 396.02 kg/ha). These results showed that introducing florescence spectral information at the flowering stage into conventional VIs-based yield estimation models is helpful in improving rice yield estimation accuracy. The usefulness of florescence spectral information for yield estimation provides a new idea for the further development and improvement of the crop yield estimation method.

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Comparison of Remotely Sensed Evapotranspiration Models Over Two Typical Sites in an Arid Riparian Ecosystem of Northwestern China

Remote Sensing ◽

10.3390/rs12091434 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1434 ◽

Cited By ~ 1

Author(s):

Tao Du ◽

Guofu Yuan ◽

Li Wang ◽

Xiaomin Sun ◽

Rui Sun

Keyword(s):

Meteorological Data ◽

Regional Scale ◽

Vegetation Indices ◽

Model Performance ◽

Remotely Sensed ◽

Northwestern China ◽

Primary Sources ◽

Estimation Accuracy ◽

Riparian Ecosystem ◽

Arid And Semiarid

Accurate estimates of evapotranspiration (ET) are essential for the conservation of ecosystems and sustainable management of water resources in arid and semiarid regions. Over the last two decades, several empirical remotely sensed ET models (ERSETMs) had been developed and extensively used for regional-scale ET estimation in arid and semiarid ecosystems. These ERSETMs were constructed by combining datasets from different sites and relating measured daily ET to corresponding meteorological data and vegetation indices at the site scale. Then, regional-scale ET on a pixel basis can be estimated, using the established ERSETMs. The estimation accuracy of these ERSETMs at the site scale plays a fundamental and crucial role in regional-scale ET estimation. Recent studies have revealed that ET estimates from some of these models have significant uncertainties at different spatiotemporal scales. However, little information is available on the performance of these ERSETMs at the site scale. In this study, we compared eight ERSETMs, using ET measurements from 2013 to 2018 for two typical eddy covariance sites (Tamarix site and Populus site) in an arid riparian ecosystem of Northwestern China, intending to provide a guide for the selection of these models. Results showed that the Nagler-2013 model and the Yuan-2016 model outperformed the other models. There were substantial differences in the ET estimation of the eight ERSETMs at daily, monthly, and seasonal scales. The mean ET of the growing season from 2013 to 2018 ranged from 465.93 to 519.65 mm for the Tamarix site and from 386.22 to 437.05 mm for the Populus site, respectively. The differences in model structures and characterization of both meteorological conditions and vegetation factors were the primary sources of different model performance. Our findings provide useful information for choosing models and obtaining accurate ET estimation in arid regions.

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LABORATORY TESTS OF THYROID FUNCTION IN HYPERTHYROIDISM II.

Acta Endocrinologica ◽

10.1530/acta.0.064s023 ◽

1969 ◽

Vol 62 (4_Suppla) ◽

pp. S23-S35

Author(s):

B.-A. Lamberg ◽

O. P. Heinonen ◽

K. Liewendahl ◽

G. Kvist ◽

M. Viherkoski ◽

...

Keyword(s):

Thyroid Function ◽

Laboratory Tests ◽

Goodness Of Fit ◽

Point Of View ◽

Accurate Estimation ◽

Diagnostic Efficiency ◽

Normal Limits ◽

Significant Difference ◽

Crossing Point ◽

Hyperthyroid Patients

ABSTRACT The distributions of 13 variables based on 10 laboratory tests measuring thyroid function were studied in euthyroid controls and in patients with toxic diffuse or toxic multinodular goitre. Density functions were fitted to the empirical data and the goodness of fit was evaluated by the use of the χ2-test. In a few instances there was a significant difference but the material available was in some respects too small to allow a very accurate estimation. The normal limits for each variable was defined by the 2.5 and 97.5 percentiles. It appears that in some instances these limits are too rigorous from the practical point of view. It is emphasized that the crossing point of the functions for euthyroid controls and hyperthyroid patients may be a better limit to use. In a preliminary analysis of the diagnostic efficiency the variables of total or free hormone concentration in the blood proved clearily superior to all other variables.

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Lowland Rice Yield as Affected by Straw Incorporation and Inorganic Fertilizer Over Cropping Seasons in Fluvisol

JOURNAL OF ADVANCES IN AGRICULTURE ◽

10.24297/jaa.v3i1.5409 ◽

2014 ◽

Vol 3 (1) ◽

pp. 129-141

Author(s):

Brahima Koné ◽

Zadi Florent ◽

Gala bi Trazié Jeremie ◽

Akassimadou Edja Fulgence ◽

Konan Kouamé Firmin ◽

...

Keyword(s):

Grain Yield ◽

Rice Variety ◽

Mineral Fertilizer ◽

Lowland Rice ◽

Inorganic Fertilizer ◽

Cropping Season ◽

Significant Difference ◽

Straw Incorporation ◽

Cropping Seasons ◽

Fertilizer Effect

Grain yield stabilization of lowland rice over cropping seasons was explored using different compositions of inorganic fertilizers (NPK, NPKCa, NPKMg, NPKZn, NPKCaMg, NPKCaZn and NPKCaMgZn) and straw incorporation (3, 6, 9, 12 and 15 tha-1 ). No fertilizer and no straw amended plot was the control in a split-plot design with three replications laid in a Fluvisol of Guinea savanna in Centre Cote d’Ivoire. Three weeks old nursery rice variety NERICA L19 was transplanted. No significant difference of grain yield was observed between the different treatments excluding the highest yields recorded for treatments NPKMg (5.09 tha-1 ), NPKZn (5.15 tha-1 ) and NPKCaéMg (5.31 tha-1 ) compared with 12 (3.95 tha1 ) and 15 tha-1 (4.14 tha-1 ) as straw rates respectively. Grain yield declining trend was more pronounced for mineral fertilizer treatments showing twice greater depressive effect of cropping cycle compared with the straw especially, for treatments characterized by highest grain yield in the first cropping season and similar grain yields were recorded for both sources of nutrient in the third cropping cycle. Of slowness of nutrients releasing by straw, highest grain yield was expected for this soil amender within a longer period of cultivation whereas, unbalance soil micronutrients should be relevant to studious declining yield under inorganic fertilizer effect. Nevertheless, the straw rate of 12 tha-1 supplying 0.58% of NPK as mineral fertilizer equivalent can be recommended for sustaining lowland rice production in the studied agro-ecosystems unless for three cropping seasons.

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Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Atmosphere ◽

10.3390/atmos12010091 ◽

2021 ◽

Vol 12 (1) ◽

pp. 91

Author(s):

Santiago Lopez-Restrepo ◽

Andres Yarce ◽

Nicolás Pinel ◽

O.L. Quintero ◽

Arjo Segers ◽

...

Keyword(s):

Air Quality ◽

Data Assimilation ◽

Transport Model ◽

Cost Model ◽

Low Cost ◽

Model Performance ◽

Monitoring Network ◽

Correlation Factor ◽

Chemical Transport Model ◽

High Quality

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

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Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O2 Battery

Energies ◽

10.3390/en14020324 ◽

2021 ◽

Vol 14 (2) ◽

pp. 324

Author(s):

Haobin Jiang ◽

Xijia Chen ◽

Yifu Liu ◽

Qian Zhao ◽

Huanhuan Li ◽

...

Keyword(s):

Stress Test ◽

Robustness Analysis ◽

Open Circuit ◽

Combined Cycle ◽

State Of Charge ◽

Constant Current ◽

Estimation Accuracy ◽

Accurate Estimation ◽

Battery Management System ◽

Liquid Dynamics

Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas–liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O2 battery. The extended Kalman Filter (EKF) algorithm is elaborated to couple the offline model and online model to achieve the goal of quickly eliminating initial errors in the online SOC estimation. An implementation of the hybrid pulse power characterization test is performed to identify the offline parameters and determine the open-circuit voltage vs. SOC curve. Apart from the standard cycles including Constant Current cycle, Federal Urban Driving Schedule cycle, Urban Dynamometer Driving Schedule cycle and Dynamic Stress Test cycle, a combined cycle is constructed for experimental validation. Furthermore, the study of the effect of sampling time on estimation accuracy and the robustness analysis of the initial value are carried out. The results demonstrate that the proposed method realizes the accurate estimation of SOC with a maximum mean absolute error at 0.50% in five working conditions and shows strong robustness against the sparse sampling and input error.

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