New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases

2014 ◽  
Vol 7 (6) ◽  
pp. 2516-2524 ◽  
Author(s):  
Wenjiang Huang ◽  
Qingsong Guan ◽  
Juhua Luo ◽  
Jingcheng Zhang ◽  
Jinling Zhao ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Spectral Indices ◽  
Wheat Diseases

scholarly journals UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

Agronomy ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 202
Author(s):  
Zhen Chen ◽  
Qian Cheng ◽  
Fuyi Duan ◽  
Xiuqiao Huang ◽  
Honggang Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Winter Wheat ◽  
Prediction Model ◽  
Grain Filling ◽  
Yield Prediction ◽  
Spectral Indices ◽  
Learner Model ◽  
Ensemble Machine Learning ◽  
Base Learner

Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

scholarly journals Evaluating spectral indices for winter wheat health status monitoring in Bloemfontein using Lsat 8 data

2016 ◽  
Vol 5 (2) ◽  
pp. 227 ◽  
Author(s):  
Zinhle Mashaba ◽  
George Chirima ◽  
Joel Botai ◽  
Ludwig Combrinck ◽  
Cilence Munghemezulu
Keyword(s):  
Health Status ◽  
Winter Wheat ◽  
Spectral Indices ◽  
Status Monitoring

Comparison of the effectiveness of standard and drift-reducing nozzles for control of some winter wheat diseases

2008 ◽  
Vol 115 (1) ◽  
pp. 23-31 ◽  
Author(s):  
S. Vajs ◽  
G. Leskošek ◽  
A. Simončič ◽  
M. Lešnik
Keyword(s):  
Winter Wheat ◽  
Wheat Diseases

scholarly journals Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm

2019 ◽  
Vol 11 (5) ◽  
pp. 535 ◽  
Author(s):  
Yuanhuizi He ◽  
Changlin Wang ◽  
Fang Chen ◽  
Huicong Jia ◽  
Dong Liang ◽  
...  
Keyword(s):  
Random Forest ◽  
Winter Wheat ◽  
High Precision ◽  
Agricultural Land ◽  
Global Climate ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Spectral Indices ◽  
Random Forest Algorithm ◽  
Sentinel 2

Winter wheat cropland is one of the most important agricultural land-cover types affected by the global climate and human activity. Mapping 30-m winter wheat cropland can provide beneficial reference information that is necessary for understanding food security. To date, machine learning algorithms have become an effective tool for the rapid identification of winter wheat at regional scales. Algorithm implementation is based on constructing and selecting many features, which makes feature set optimization an important issue worthy of discussion. In this study, the accurate mapping of winter wheat at 30-m resolution was realized using Landsat-8 Operational Land Imager (OLI), Sentinel-2 Multispectral Imager (MSI) data, and a random forest algorithm. This paper also discusses the optimal combination of features suitable for cropland extraction. The results revealed that: (1) the random forest algorithm provided robust performance using multi-features (MFs), multi-feature subsets (MFSs), and multi-patterns (MPs) as input parameters. Moreover, the highest accuracy (94%) for winter wheat extraction occurred in three zones, including: pure farmland, urban mixed areas, and forest areas. (2) Spectral reflectance and the crop growth period were the most essential features for crop extraction. The MFSs combined with the three to four feature types enabled the high-precision extraction of 30-m winter wheat plots. (3) The extraction accuracy of winter wheat in three zones with multiple geographical environments was affected by certain dominant features, including spectral bands (B), spectral indices (S), and time-phase characteristics (D). Therefore, we can improve the winter wheat mapping accuracy of the three regional types by improving the spectral resolution, constructing effective spectral indices, and enriching vegetation information. The results of this paper can help effectively construct feature sets using the random forest algorithm, thus simplifying the feature construction workload and ensuring high-precision extraction results in future winter wheat mapping research.

scholarly journals Cellulolytic activity of leached black earth for no-till in connection with the question of winter wheat diseases

2018 ◽  
pp. 37-41
Author(s):  
A.P. Shutko ◽  
◽  
E.E. Zaschepkin ◽  
L.V. Tuturzhans ◽  
V.M. Perederieva ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Cellulolytic Activity ◽  
Wheat Diseases ◽  
No Till

An attempt to understand corn and winter wheat temporal behavior by means of Sentinel-1 and Sentinel-2 data and its relation with phonological stages.

2021 ◽  
Author(s):  
Marta Pasternak ◽  
Kamila Pawluszek-Filipiak
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Strong Correlation ◽  
Vegetation Index ◽  
Radar Data ◽  
Temporal Behavior ◽  
Spectral Indices ◽  
Chlorophyll Index ◽  
Sar Data ◽  
Sentinel 2

<p>Crops are of the fundamental food sources for humanity. Due to the population growth as well as climate change, monitoring of the crops is important to sustain agriculture and conserve natural resources. Development of the remote sensing techniques especially in terms of revisiting time opens new avenues to study crops temporal behaviors from space. Moreover, thanks to the Copernicus program, which guarantees optical as well as radar data to be freely available, there are opportunities to utilize them in an operative way. Additionally, utilization of spectral as well as radar data allows for the synergetic application of both datasets. However, to utilize this data in the operational crop monitoring, it is very important to understand the temporal variations of the remote sensing signal. Therefore, we make an attempt to understand spectral as well as radar remote sensing temporal behavior and its relation with phonological stages.</p><p>For the analysis, 14 cloud-free Sentinel-2 (S-2) acquisitions as well as 34 Sentinel-1 (S-1) acquisitions are utilized. S-2 data were collected with 2A-level while S-1 data was captured in the format of Single Look Complex (SLC) in the Interferometric Wide (IW) swath mode. SLC products consist of complex SAR data preserving phase information which allows studying polarimetric indicators. All remote sensing (spectral as well as SAR) data cover the time period from 04/05/2020 to 07/11/2020. During this time, also 14 field visits were carried out to capture information about phonological stages of corn and wheat according to the BBCH scale (Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie). Additionally, to better understand the temporal behavior of S-1/S-2 signal, weather information from the Institute of Meteorology and Water Management (IMGW) was captured.</p><p>Based on various spectral bands of S-2 data, 12 spectral indices were calculated e.g., GNDVI (Green Normalized Vegetation Index), IRECI (Inverted Red-Edge Chlorophyll Index), MCARI (Modified Chlorophyll Absorption in Reflectance Index), MSAVI (Modified Soil-Adjusted Vegetation Index), MTCI (MERIS Terrestrial Chlorophyll Index), NDVI (Normalized Difference Vegetation Index), PSSRa (Pigment Specific Simple Ratio) and others. After radiometric calibration and the Lee speckle filtering, backscattering coefficients (σ<sub>VV</sub><sup>o</sup> ,σ<sub>VH</sub><sup>o</sup>) of S-1 images were calculated as well as its backscattering ratio (σ<sub>VH</sub><sup>o</sup>/ σ<sub>VV</sub><sup>o</sup>).  All images were then converted from linear to decibel (dB). Additionally, 2 × 2 covariance matrix delivered from S-1 was extracted from the scattering matrix of each SLC image using PolSARpro version 6.0.2 software. After speckle filtration, total scattered power was derived which allows calculating the Shannon Entropy. This value measures the randomness of the scattering within a pixel.</p><p>Time series of many S-2 indices reveal the strong correlation between the development of phenology stages of corn and wheat and the increase of S2 delivered values of spectral indices. However, such a strong correlation cannot be observed within many of S-1 indices. Some of them very poorly indicate the correlation between the development of phenology stages of corn and wheat and increase of S-1 indices values. Additionally, it was observed that values of S1/S2 indices for the same phenology stage very between corn and winter wheat.</p><p> </p>

scholarly journals Water and Temperature Parameters Associated with Winter Wheat Diseases Caused by Soilborne Pathogens

Plant Disease ◽  
2009 ◽  
Vol 93 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Richard W. Smiley
Keyword(s):  
Winter Wheat ◽  
Field Experiments ◽  
Disease Incidence ◽  
Simulation Models ◽  
Crown Rot ◽  
Yield Potential ◽  
Planting Dates ◽  
Wheat Diseases ◽  
Rhizoctonia Root Rot ◽  
Take All

Wheat in eastern Oregon is produced mostly as a 2-year rotation of winter wheat and summer fallow. Maximum agronomic yield potential is expected with early September planting dates but actual yields are generally highest for plantings made in mid-October. Field experiments with sequential planting dates from early September to December were performed over 4 years. Associations among yield, disease incidence, and 19 moisture and temperature parameters were evaluated. Incidence of Cephalosporium stripe, crown rot, eyespot, and take-all decreased as planting was delayed. Crown rot and eyespot were negatively correlated more significantly and more frequently with temperature than moisture parameters, and take-all was more associated with moisture than temperature. Rhizoctonia root rot was unrelated to planting date and climatic parameters. Crown rot was identified most frequently (4 of 5 tests) as an important contributor to yield suppression but yield was most closely associated (R2 > 0.96) with effects from a single disease in only two of five location–year tests. Yield was most related to combinations of diseases in three of five tests, complicating development of disease modules for wheat growth-simulation models.

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