scholarly journals Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing

2019 ◽  
Vol 11 (23) ◽  
pp. 2752 ◽  
Author(s):  
Xiaoyan Zhang ◽  
Jinming Zhao ◽  
Guijun Yang ◽  
Jiangang Liu ◽  
Jiqiu Cao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Growth Stage ◽  
Vegetation Index ◽  
Prediction Models ◽  
Hyperspectral Remote Sensing ◽  
Yield Prediction ◽  
Breeding Programs ◽  
Soybean Breeding ◽  
Breeding Lines ◽  
Plot Yield

Yield evaluation of breeding lines is the key to successful release of cultivars, which is becoming a serious issue due to soil heterogeneity in enlarged field tests. This study aimed at establishing plot-yield prediction models using unmanned aerial vehicle (UAV)-based hyperspectral remote sensing for yield-selection in large-scale soybean breeding programs. Three sets of soybean breeding lines (1103 in total) were tested in blocks-in-replication experiments for plot yield and canopy spectral reflectance on 454~950 nm bands at different growth stages using a UAV-based hyperspectral spectrometer (Cubert UHD185 Firefly). The four elements for plot-yield prediction model construction were studied respectively and concluded as: the suitable reflectance-sampling unit-size in a plot was its 20%–80% central part; normalized difference vegetation index (NDVI) and ration vegetation index (RVI) were the best combination of vegetation indices; the initial seed-filling stage (R5) was the best for a single stage prediction, while another was the best combination for a two growth-stage prediction; and multi-variate linear regression was suitable for plot-yield prediction. In model establishment for each material-set, a random half was used for modelling and another half for verification. Twenty-one two growth-stage two vegetation-index prediction models were established and compared for their modelling coefficient of determination (RM2) and root mean square error of the model (RMSEM), verification RV2 and RMSEV, and their sum RS2 and RMSES. Integrated with the coincidence rate between the model predicted and the practical yield-selection results, the models, MA1-2, MA4-2 and MA6-2, with coincidence rates of 56.8%, 58.5% and 52.4%, respectively, were chosen for yield-prediction in yield-test nurseries. The established model construction elements and methods can be used as local models for pre-harvest yield-selection and post-harvest integrated yield-selection in advanced breeding nurseries as well as yield potential prediction in plant-derived-line nurseries. Furthermore, multiple models can be used jointly for plot-yield prediction in soybean breeding programs.

scholarly journals Fire Blight Disease Detection for Apple Trees: Hyperspectral Analysis of Healthy, Infected and Dry Leaves

2020 ◽  
Vol 12 (13) ◽  
pp. 2101 ◽  
Author(s):  
Hubert Skoneczny ◽  
Katarzyna Kubiak ◽  
Marcin Spiralski ◽  
Jan Kotlarz ◽  
Artur Mikiciński ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fire Blight ◽  
Vegetation Index ◽  
Pearson Correlation ◽  
Hyperspectral Remote Sensing ◽  
Bacterial Disease ◽  
Apple Trees ◽  
Remote Sensing Technique ◽  
Spectral Bands ◽  
Swir Band

The effective and rapid detection of Fire Blight, an important bacterial disease caused by the quarantine pest E.amylovora, is crucial for today’s horticulture. This study explored the application of non-invasive proximal hyperspectral remote sensing (RS) in order to differentiate the healthy (H), infected (I) and dry (D) leaves of apple trees. Analysis of variance was employed in order to determine which hyperspectral narrow spectral bands exhibited the most significant differences. Spectral signatures for the range of 400–2500 nm were acquired with Thermo Scientific Evolution 220 and iS50NIR spectrometers. The selected spectral bands were then used to evaluate several RS indices, including ARI (Anthocyanin Reflectance Index), RDVI (Renormalized Difference Vegetation Index), MSR (Modified Simple Ratio) and NRI (Nitrogen Reflectance Index), for Fire Blight detection in apple tree leaves. Furthermore, a new index was proposed, namely QFI. The spectral indices were tested on apple trees infected by Fire Blight in a quarantine greenhouse. Results indicated that the short-wavelength infrared (SWIR) band located at 1450 nm was able to distinguish (I) and (H) leaves, while the SWIR band at 1900 nm differentiated all three leaf types. Moreover, tests using the Pearson correlation indicated that ARI, MSR and QFI exhibited the highest correlations with the infection progress. Our results prove that our hyperspectral remote sensing technique is able to differentiate (H), (I) and (D) leaves of apple trees for the reliable and precise detection of Fire Blight.

scholarly journals Hyperspectral Remote Sensing for Determining Water and Nitrogen Stress in Maize during Rabi Season

2020 ◽  
pp. 1-9
Author(s):  
H. R. Naveen ◽  
B. Balaji Naik ◽  
G. Sreenivas ◽  
Ajay Kumar ◽  
J. Adinarayana ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Hyperspectral Remote Sensing ◽  
Nitrogen Stress ◽  
Fixed Amount ◽  
Nitrogen Levels ◽  
Stress Environment

Aims/Objectives: Is to examine the use of spectral reflectance characteristics and explore the effectiveness of spectral indices under water and nitrogen stress environment. Study Design: Split-plot. Place and Duration of Study: Agro Climate Research Center, A.R.I., P.J.T.S. Agricultural University, Rajendranagar, Hyderabad, India in 2018-19. Methodology: Fixed amount of 5 cm depth of water was applied to each plot when the ratio of irrigation water and cumulative pan evaporation (IW/CPE) arrives at pre-determined levels of 0.6, 0.8 & 1.2 as main-plot and 3 nitrogen levels viz. 100, 200 & 300 kg N ha-1 as a subplot to create water and nitrogen stress environment. Spectral reflectance from each treatment was measured using Spectroradiometer and analyzed using statistical software package SPSS 17, SAS and trial version of UNSCRABLER. Results: At tasseling and dough stages, the reflectance pattern of maize was found to be higher in visible light spectrum of 400 to700 nm whereas lower in near-infrared region (700 to 900) in both underwater (IW/CPE ratio of 0.6) and nitrogen stress (100 kg N ha-1) environment as compared to moderate and no stress irrigation (IW/CPE ratio of 0.8 & 1.2) and nitrogen (200 and 300 kg N ha-1) treatments. The discriminant analysis of NDVI, GNDVI, WBI and SR indicated that 72.2% and 66.7% of the original grouped cases and 55.6% and 38.9% of the cross-validated grouped cases under irrigation and nitrogen levels, respectively were correctly classified. Conclusion: Hyperspectral remote sensing can be used as a tool to detect and quantify the water and nitrogen stress in maize non-destructively. Spectral vegetation indices viz. Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were found effective to distinguish water and nitrogen stress severity in maize.

Hyperspectral remote sensing for growth-stage-specific water use in wheat

2013 ◽  
Vol 144 ◽  
pp. 179-191 ◽  
Author(s):  
Sudipta Chattaraj ◽  
Debashis Chakraborty ◽  
R.N. Garg ◽  
G.P. Singh ◽  
V.K. Gupta ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Use ◽  
Growth Stage ◽  
Hyperspectral Remote Sensing

scholarly journals Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn

2020 ◽  
Vol 12 (15) ◽  
pp. 2392
Author(s):  
Razieh Barzin ◽  
Rohit Pathak ◽  
Hossein Lotfi ◽  
Jac Varco ◽  
Ganesh C. Bora
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Prediction Models ◽  
Feature Selection Method ◽  
Spectral Information ◽  
Gradient Boosting ◽  
Spatial And Temporal Variability ◽  
Yield Prediction ◽  
Phenological Stages ◽  
Red Edge

Changes in spatial and temporal variability in yield estimation are detectable through plant biophysical characteristics observed at different phenological development stages of corn. A multispectral red-edge sensor mounted on an Unmanned Aerial Systems (UAS) can provide spatial and temporal information with high resolution. Spectral analysis of UAS acquired spatiotemporal images can be used to develop a statistical model to predict yield based on different phenological stages. Identifying critical vegetation indices (VIs) and significant spectral information could lead to increased yield prediction accuracy. The objective of this study was to develop a yield prediction model at specific phenological stages using spectral data obtained from a corn field. The available spectral bands (red, blue, green, near infrared (NIR), and red-edge) were used to analyze 26 different VIs. The spectral information was collected from a cornfield at Mississippi State University using a MicaSense multispectral red-edge sensor, mounted on a UAS. In this research, a new empirical method used to reduce the effects of bare soil pixels in acquired images was introduced. The experimental design was a randomized complete block that consisted of 16 blocks with 12 rows of corn planted in each block. Four treatments of nitrogen (N) including 0, 90, 180, and 270 kg/ha were applied randomly. Random forest was utilized as a feature selection method to choose the best combination of variables for different stages. Multiple linear regression and gradient boosting decision trees were used to develop yield prediction models for each specific phenological stage by utilizing the most effective variables at each stage. At the V3 (3 leaves with visible leaf collar) and V4-5 (4-5 leaves with visible leaf collar) stages, the Optimized Soil Adjusted Vegetation Index (OSAVI) and Simplified Canopy Chlorophyll Content Index (SCCCI) were the single dominant variables in the yield predicting models, respectively. A combination of the Green Atmospherically Resistant Index (GARI), Normalized Difference Red-Edge (NDRE), and green Normalized Difference Vegetation Index (GNDVI) at V6-7, SCCCI, and Soil-Adjusted Vegetation Index (SAVI) at V10,11, and SCCCI, Green Leaf Index (GLI), and Visible Atmospherically Resistant Index (VARIgreen) at tasseling stage (VT) were the best indices for predicting grain yield of corn. The prediction models at V10 and VT had the greatest accuracy with a coefficient of determination of 0.90 and 0.93, respectively. Moreover, the SCCCI as a combined index seemed to be the most proper index for predicting yield at most of the phenological stages. As corn development progressed, the models predicted final grain yield more accurately.

scholarly journals Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1406
Author(s):  
Zhonglin Ji ◽  
Yaozhong Pan ◽  
Xiufang Zhu ◽  
Jinyun Wang ◽  
Qiannan Li
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Arid Regions ◽  
Vegetation Index ◽  
Corn Yield ◽  
Yield Prediction ◽  
Multivariate Models ◽  
Ndvi Time Series ◽  
Phenological Metrics ◽  
Semi Arid

Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided into semi-arid and non-semi-arid regions. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product MOD09Q1 was used to calculate the normalized difference vegetation index (NDVI) time series. According to the NDVI time series, we divided the corn growing season into four growth phases, calculated phenological information metrics (duration and rate) for each growth phase, and obtained the maximum correlation NDVI (Max-R2). Duration and rate represent crop growth days and rate, respectively. Max-R2 is the NDVI value with the most significant correlation with corn yield in the NDVI time series. We built three groups of yield regression models, including univariate models using phenological metrics and Max-R2, and multivariate models using phenological metrics, and multivariate models using phenological metrics combined with Max-R2 in the whole, semi-arid, and non-semi-arid regions, respectively, and compared the performance of these models. The results show that most phenological metrics had a statistically significant (p < 0.05) relationship with corn yield (maximum R2 = 0.44). Models established with phenological metrics realized yield prediction before harvest in the three regions with R2 = 0.64, 0.67, and 0.72. Compared with the univariate Max-R2 models, the accuracy of models built with Max-R2 and phenology metrics improved. Thus, the phenology metrics obtained from MODIS-NDVI accurately reflect the corn characteristics and can be used for large-scale yield prediction. Overall, this study showed that phenology metrics derived from remote sensing vegetation indexes could be used as crop yield prediction variables and provide a reference for data organization and yield prediction with physical crop significance.

scholarly journals Monitoring of Wheat Powdery Mildew under Different Nitrogen Input Levels Using Hyperspectral Remote Sensing

2021 ◽  
Vol 13 (18) ◽  
pp. 3753
Author(s):  
Wei Liu ◽  
Chaofei Sun ◽  
Yanan Zhao ◽  
Fei Xu ◽  
Yuli Song ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Powdery Mildew ◽  
Grain Yield ◽  
Disease Severity ◽  
Spectral Reflectance ◽  
Growth Stage ◽  
Hyperspectral Remote Sensing ◽  
Nitrogen Input ◽  
Wheat Powdery Mildew ◽  
Reflectance Data

Both wheat powdery mildew severities and nitrogen input levels can lead to changes in spectral reflectance, but they have been rarely studied simultaneously for their effect on spectral reflectance. To determine the effects and influences of different nitrogen input levels on monitoring wheat powdery mildew and estimating yield by near-ground hyperspectral remote sensing, Canopy hyperspectral reflectance data acquired at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 were used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels during the 2016–2017, 2017–2018, 2018–2019 and 2019–2020 seasons. The relationships of powdery mildew and grain yield with vegetation indices (VIs) derived from spectral reflectance data across the visible (VIS) and near-infrared (NIR) regions of the spectrum were studied. The relationships of canopy spectral reflectance or first derivative spectral reflectance with powdery mildew did not differ under different nitrogen input levels. However, the dynamics of VIs differed in their sensitivities to nitrogen input levels, disease severity, grain yield, The area of the red edge peak (Σdr680–760 nm) was a better overall predictor for both disease severity and grain yield through linear regression models. The slope parameter estimates did not differ between the two nitrogen input levels at each GSs. Hyperspectral indices can be used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels, but such models are dependent on GS and year, further research is needed to consider how to incorporate the growth stage and year-to-year variation into future applications.

scholarly journals A Regional Maize Yield Hierarchical Linear Model Combining Landsat 8 Vegetative Indices and Meteorological Data: Case Study in Jilin Province

2021 ◽  
Vol 13 (3) ◽  
pp. 356
Author(s):  
Bingxue Zhu ◽  
Shengbo Chen ◽  
Yijing Cao ◽  
Zhengyuan Xu ◽  
Yan Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hierarchical Linear Modeling ◽  
Prediction Models ◽  
Meteorological Data ◽  
Maize Yield ◽  
Jilin Province ◽  
Statistical Prediction ◽  
Landsat 8 ◽  
Yield Prediction ◽  
Prediction Problems

The use of satellite remote sensing could effectively predict maize yield. However, many statistical prediction models using remote sensing data cannot extend to the regional scale without considering the regional climate. This paper first introduced the hierarchical linear modeling (HLM) method to solve maize-yield prediction problems over years and regions. The normalized difference vegetation index (NDVI), calculated by the spectrum of the Landsat 8 operational land imager (OLI), and meteorological data were introduced as input parameters in the maize-yield prediction model proposed in this paper. We built models using 100 samples from 10 areas, and used 101 other samples from 34 areas to evaluate the model’s performance in Jilin province. HLM provided higher accuracy with an adjusted determination coefficient equal to 0.75, root mean square error (RMSEV) equal to 0.94 t/ha, and normalized RMSEV equal to 9.79%. Results showed that the HLM approach outperformed linear regression (LR) and multiple LR (MLR) methods. The HLM method based on the Landsat 8 OLI NDVI and meteorological data could flexibly adjust in different regional climatic conditions. They had higher spatiotemporal expansibility than that of widely used yield estimation models (e.g., LR and MLR). This is helpful for the accurate management of maize fields.

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