scholarly journals Hyperspectral Estimation Methods for Chlorophyll Content of Apple Based on Random Forest

2019 ◽  
pp. 194-207
Author(s):  
Haojie Pei ◽  
Changchun Li ◽  
Haikuan Feng ◽  
Guijun Yang ◽  
Mingxing Liu ◽  
...  
Keyword(s):  
Random Forest ◽  
Chlorophyll Content ◽  
Estimation Methods

Utilizing Data-Driven Models to Predict Brittleness in Tuscaloosa Marine Shale: A Machine Learning Approach

2021 ◽  
Author(s):  
Jamal Ahmadov
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brittleness Index ◽  
Estimation Methods ◽  
Gradient Boosting ◽  
Average Error ◽  
Support Vector ◽  
Marine Shale ◽  
Effective Manner ◽  
Selection Of

Abstract The Tuscaloosa Marine Shale (TMS) formation is a clay- and liquid-rich emerging shale play across central Louisiana and southwest Mississippi with recoverable resources of 1.5 billion barrels of oil and 4.6 trillion cubic feet of gas. The formation poses numerous challenges due to its high average clay content (50 wt%) and rapidly changing mineralogy, making the selection of fracturing candidates a difficult task. While brittleness plays an important role in screening potential intervals for hydraulic fracturing, typical brittleness estimation methods require the use of geomechanical and mineralogical properties from costly laboratory tests. Machine Learning (ML) can be employed to generate synthetic brittleness logs and therefore, may serve as an inexpensive and fast alternative to the current techniques. In this paper, we propose the use of machine learning to predict the brittleness index of Tuscaloosa Marine Shale from conventional well logs. We trained ML models on a dataset containing conventional and brittleness index logs from 8 wells. The latter were estimated either from geomechanical logs or log-derived mineralogy. Moreover, to ensure mechanical data reliability, dynamic-to-static conversion ratios were applied to Young's modulus and Poisson's ratio. The predictor features included neutron porosity, density and compressional slowness logs to account for the petrophysical and mineralogical character of TMS. The brittleness index was predicted using algorithms such as Linear, Ridge and Lasso Regression, K-Nearest Neighbors, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost and Gradient Boosting. Models were shortlisted based on the Root Mean Square Error (RMSE) value and fine-tuned using the Grid Search method with a specific set of hyperparameters for each model. Overall, Gradient Boosting and Random Forest outperformed other algorithms and showed an average error reduction of 5 %, a normalized RMSE of 0.06 and a R-squared value of 0.89. The Gradient Boosting was chosen to evaluate the test set and successfully predicted the brittleness index with a normalized RMSE of 0.07 and R-squared value of 0.83. This paper presents the practical use of machine learning to evaluate brittleness in a cost and time effective manner and can further provide valuable insights into the optimization of completion in TMS. The proposed ML model can be used as a tool for initial screening of fracturing candidates and selection of fracturing intervals in other clay-rich and heterogeneous shale formations.

scholarly journals Link quality estimation based on over-sampling and weighted random forest

2021 ◽  
pp. 41-41
Author(s):  
Linlan Liu ◽  
Yi Feng ◽  
Shengrong Gao ◽  
Jian Shu
Keyword(s):  
Random Forest ◽  
Decision Trees ◽  
Estimation Method ◽  
Classification Performance ◽  
Link Quality ◽  
Estimation Methods ◽  
Quality Level ◽  
Estimation Model ◽  
Quality Estimation ◽  
Link Quality Estimation

Aiming at the imbalance problem of wireless link samples, we propose the link quality estimation method which combines the K-means synthetic minority over-sampling technique (K-means SMOTE) and weighted random forest. The method adopts the mean, variance and asymmetry metrics of the physical layer parameters as the link quality parameters. The link quality is measured by link quality level which is determined by the packet receiving rate. K-means is used to cluster link quality samples. SMOTE is employed to synthesize samples for minority link quality samples, so as to make link quality samples of different link quality levels reach balance. Based on the weighted random forest, the link quality estimation model is constructed. In the link quality estimation model, the decision trees with worse classification performance are assigned smaller weight, and the decision trees with better classification performance are assigned bigger weight. The experimental results show that the proposed link quality estimation method has better performance with samples processed by K-means SMOTE. Furthermore, it has better estimation performance than the ones of Naive Bayesian, Logistic Regression and K-nearest Neighbour estimation methods.

scholarly journals A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

2021 ◽  
Vol 14 (1) ◽  
pp. 98
Author(s):  
Quanjun Jiao ◽  
Qi Sun ◽  
Bing Zhang ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
...  
Keyword(s):  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Leaf Angle ◽  
Transfer Model ◽  
Growth Monitoring ◽  
Random Forest Algorithm ◽  
Area Index ◽  
Important Indicator

Canopy chlorophyll content (CCC) is an important indicator for crop-growth monitoring and crop productivity estimation. The hybrid method, involving the PROSAIL radiative transfer model and machine learning algorithms, has been widely applied for crop CCC retrieval. However, PROSAIL’s homogeneous canopy hypothesis limits the ability to use the PROSAIL-based CCC estimation across different crops with a row structure. In addition to leaf area index (LAI), average leaf angle (ALA) is the most important canopy structure factor in the PROSAIL model. Under the same LAI, adjustment of the ALA can make a PROSAIL simulation obtain the same canopy gap as the heterogeneous canopy at a specific observation angle. Therefore, parameterization of an adjusted ALA (ALAadj) is an optimal choice to make the PROSAIL model suitable for specific row-planted crops. This paper attempted to improve PROSAIL-based CCC retrieval for different crops, using a random forest algorithm, by introducing the prior knowledge of crop-specific ALAadj. Based on the field reflectance spectrum at nadir, leaf area index, and leaf chlorophyll content, parameterization of the ALAadj in the PROSAIL model for wheat and soybean was carried out. An algorithm integrating the random forest and PROSAIL simulations with prior ALAadj information was developed for wheat and soybean CCC retrieval. Ground-measured CCC measurements were used to validate the CCC retrieved from canopy spectra. The results showed that the ALAadj values (62 degrees for wheat; 45 degrees for soybean) that were parameterized for the PROSAIL model demonstrated good discrimination between the two crops. The proposed algorithm improved the CCC retrieval accuracy for wheat and soybean, regardless of whether continuous visible to near-infrared spectra with 50 bands (RMSE from 39.9 to 32.9 μg cm−2; R2 from 0.67 to 0.76) or discrete spectra with 13 bands (RMSE from 43.9 to 33.7 μg cm−2; R2 from 0.63 to 0.74) and nine bands (RMSE from 45.1 to 37.0 μg cm−2; R2 from 0.61 to 0.71) were used. The proposed hybrid algorithm, based on PROSAIL simulations with ALAadj, has the potential for satellite-based CCC estimation across different crop types, and it also has a good reference value for the retrieval of other crop parameters.

Contactless and non-destructive chlorophyll content prediction by random forest regression: A case study on fresh-cut rocket leaves

2017 ◽  
Vol 140 ◽  
pp. 303-310 ◽  
Author(s):  
Dario Pietro Cavallo ◽  
Maria Cefola ◽  
Bernardo Pace ◽  
Antonio Francesco Logrieco ◽  
Giovanni Attolico
Keyword(s):  
Random Forest ◽  
Chlorophyll Content ◽  
Random Forest Regression ◽  
Fresh Cut ◽  
Non Destructive

scholarly journals Response of Different Band Combinations in Gaofen-6 WFV for Estimating of Regional Maize Straw Resources Based on Random Forest Classification

2021 ◽  
Vol 13 (9) ◽  
pp. 4603
Author(s):  
Huawei Mou ◽  
Huan Li ◽  
Yuguang Zhou ◽  
Renjie Dong
Keyword(s):  
Spatial Distribution ◽  
Random Forest ◽  
Classification Accuracy ◽  
Information Source ◽  
Accurate Determination ◽  
Estimation Methods ◽  
Support Vector ◽  
Wide Field ◽  
Imaging Characteristics ◽  
Maize Straw

Maize straw is a valuable renewable energy source. The rapid and accurate determination of its yield and spatial distribution can promote improved utilization. At present, traditional straw estimation methods primarily rely on statistical analysis that may be inaccurate. In this study, the Gaofen 6 (GF-6) satellite, which combines high resolution and wide field of view (WFV) imaging characteristics, was used as the information source, and the quantity of maize straw resources and spatial distribution characteristics in Qihe County were analyzed. According to the phenological characteristics of the study area, seven classification classes were determined, including maize, buildings, woodlands, wastelands, water, roads, and other crops, to explore the influence of sample separation and test the responsiveness to different land cover types with different waveband combinations. Two supervised classification methods, support vector machine (SVM) and random forest (RF), were used to classify the study area, and the influence of the newly added band of GF-6 WFV on the classification accuracy of the study area was analyzed. Furthermore, combined with field surveys and agricultural census data, a method for estimating the quantity of maize straw and analyzing the spatial distribution based on a single-temporal remote sensing image and random forests was proposed. Finally, the accuracy of the measurement results is evaluated at the county level. The results showed that the RF model made better use of the newly added bands of GF-6 WFV and improved the accuracy of classification, compared with the SVM model; the two red-edge bands improved the accuracy of crop classification and recognition; the purple and yellow bands identified non-vegetation more effectively than vegetation, thus minimizing the “salt-and-pepper noise” of classification results. However, the changes to total classification accuracy were not obvious; the theoretical quantity of maize straw in Qihe County in 2018 was 586.08 kt, which reflects an error of only 2.42% compared to the statistical result. Hence, the RF model based on single-temporal GF-6 WFV can effectively estimate regional maize straw yield and spatial distribution, which lays a theoretical foundation for straw recycling.

scholarly journals MODELLING BIOPHYSICAL PARAMETERS OF MAIZE USING LANDSAT 8 TIME SERIES

2016 ◽  
Vol XLI-B2 ◽  
pp. 171-175
Author(s):  
Thorsten Dahms ◽  
Sylvia Seissiger ◽  
Christopher Conrad ◽  
Erik Borg
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Random Forest ◽  
Chlorophyll Content ◽  
Conditional Inference ◽  
Landsat 8 ◽  
Biophysical Parameters ◽  
Machine Learning Methods

Open and free access to multi-frequent high-resolution data (e.g. Sentinel – 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. <br><br> In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. <br><br> The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. <br><br> Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9. <br><br> Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model biophysical parameters.

scholarly journals MODELLING BIOPHYSICAL PARAMETERS OF MAIZE USING LANDSAT 8 TIME SERIES

2016 ◽  
Vol XLI-B2 ◽  
pp. 171-175
Author(s):  
Thorsten Dahms ◽  
Sylvia Seissiger ◽  
Christopher Conrad ◽  
Erik Borg
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Random Forest ◽  
Chlorophyll Content ◽  
Conditional Inference ◽  
Landsat 8 ◽  
Biophysical Parameters ◽  
Machine Learning Methods

Open and free access to multi-frequent high-resolution data (e.g. Sentinel – 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. &lt;br&gt;&lt;br&gt; In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. &lt;br&gt;&lt;br&gt; The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. &lt;br&gt;&lt;br&gt; Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9. &lt;br&gt;&lt;br&gt; Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model biophysical parameters.

Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations

2021 ◽  
Vol 13 (3) ◽  
pp. 470
Author(s):  
Qi Sun ◽  
Quanjun Jiao ◽  
Xiaojin Qian ◽  
Liangyun Liu ◽  
Xinjie Liu ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Chlorophyll Content ◽  
Satellite Data ◽  
Vegetation Indices ◽  
Sensor Data ◽  
Crop Canopy ◽  
Random Forest Regression ◽  
Satellite Sensor Data ◽  
Satellite Sensor

Estimates of crop canopy chlorophyll content (CCC) can be used to monitor vegetation productivity, manage crop resources, and control disease and pests. However, making these estimates using conventional ground-based methods is time-consuming and resource-intensive when deployed over large areas. Although vegetation indices (VIs), derived from satellite sensor data, have been used to estimate CCC, they suffer from problems related to spectral saturation, soil background, and canopy structure. A new method was, therefore, proposed for combining the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) and LAI-related vegetation indices (LAI-VIs) to increase the accuracy of CCC estimates for wheat and soybeans. The PROSAIL-D canopy reflectance model was used to simulate canopy spectra that were resampled to match the spectral response functions of the MERIS carried on the ENVISAT satellite. Combinations of the MTCI and LAI-VIs were then used to estimate CCC via univariate linear regression, binary linear regression and random forest regression. The accuracy using the field spectra and MERIS data was determined based on field CCC measurements. All the MTCI and LAI-VI combinations for the selected regression techniques resulted in more accurate estimates of CCC than the use of the MTCI alone (field spectra data for soybeans and wheat: R2 = 0.62 and RMSE = 77.10 μg cm−2; MERIS satellite data for soybeans: R2 = 0.24 and RMSE = 136.54 μg cm−2). The random forest regression resulted in better accuracy than the other two linear regression models. The combination resulting in the best accuracy was the MTCI and MTVI2 and random forest regression, with R2 = 0.65 and RMSE = 37.76 μg cm−2 (field spectra data) and R2 = 0.78 and RMSE = 47.96 μg cm−2 (MERIS satellite data). Combining the MTCI and a LAI-VI represents a further step towards improving the accuracy of estimation CCC based on multispectral satellite sensor data.

Estimation of leaf chlorophyll content in cotton based on the random forest approach

2019 ◽  
Vol 45 (1) ◽  
pp. 81
Author(s):  
ABLET Ershat ◽  
SAWUT Mamat ◽  
MAIMAITIAILI Baidengsha ◽  
Shen-Qun AN ◽  
Chun-Yue MA
Keyword(s):  
Random Forest ◽  
Chlorophyll Content ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll
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