scholarly journals Predicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC)

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Vaughn Reed ◽  
Daryl B. Arnall ◽  
Bronc Finch ◽  
Joao Luis Bigatao Souza
Keyword(s):  
Winter Wheat ◽  
Prediction Model ◽  
Optical Sensors ◽  
Prediction Models ◽  
Canopy Cover ◽  
Yield Prediction ◽  
State University ◽  
Normalized Difference Vegetative Index ◽  
Highly Correlated ◽  
N Rates

Optical sensors have grown in popularity for estimating plant health, and they form the basis of midseason yield estimations and nitrogen (N) fertilizer recommendations, such as the Oklahoma State University (OSU) nitrogen fertilization optimization algorithm (NFOA). That algorithm uses measurements of normalized difference vegetative index (NDVI), yet not all producers have access to the sensors required to make these measurements. In contrast, most producers have access to smartphones, which can measure fractional green canopy cover (FGCC) using the Canopeo app, but the usefulness of these measurements for midseason yield estimations remains untested. Our objectives were to (1) quantify the relationship between NDVI and FGCC, (2) assess the potential for using FGCC values in place of NDVI values in the current OSU Yield Prediction Model, and (3) compare the performance of NDVI and FGCC-based yield prediction models from the collected dataset. This project, implemented on 13 winter wheat sites over the 2019-2020 growing season, used a range of nitrogen (N) rates (0, 34, 67, 101, and 134 kg N ha−1) to provide different levels of yield. Our results indicated that while NDVI and FGCC are highly correlated (r2 = 0.76), FGCC is not suitable for direct insertion into the current yield prediction model. However, a yield prediction model derived from FGCC provided similar estimates of yield compared to NDVI (Nash Sutcliffe Efficiency = −3.3). This new FGCC-based model will give more producers access to sensor-based yield prediction and N rate recommendations.

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 Prediction Models of Corn Yield by NDVI in Function of the Spacing Arrangement

2019 ◽  
Vol 11 (6) ◽  
pp. 493
Author(s):  
Mailson Freire de Oliveira ◽  
Antonio Tassio Santana Ormond ◽  
Rafael Henrique de Freitas Noronha ◽  
Adão Felipe dos Santos ◽  
Cristiano Zerbato ◽  
...  
Keyword(s):  
Optical Sensors ◽  
Prediction Models ◽  
Corn Yield ◽  
Yield Prediction ◽  
Agricultural Crops ◽  
Plant Populations ◽  
Significant Information ◽  
Vegetation Indexes ◽  
Crop Development ◽  
Productive Potential

There is a need for the use of tools to estimate productive potential during corn crop development. Thus, the assistence by means of active optical sensors for the generating of vegetation indexes can provide significant information for the knowledge of the behavior and temporal relation of this index with productive parameters of the agricultural crops. It was aimed to evaluate the temporal behavior of NDVI and its relation with yield of corn in order to generate yield prediction models in plant populations (55, 60 and 65 thousand plants ha-1) in spacing of conventional seeding and twin rows. A factorial 2 × 3 was utilized with four replicates, with a total of 24 experimental plots of 10 m2 in randomized blocks, performing reading NDVI at 5 seasons (30, 45, 60, 75 and 90 days after emergence of the plants DAE). The spacing in twin rows at 30 and 90 DAE for populations of 55 and 60 thousand plants ha-1, respectively, allowed to generate models for the prediction of productivity based on corn NDVI, while the population of 65 thousand plants ha-1 at 45 and 60 DAE there was no adjustment by the prediction model of yield by values close to NDVI for different productivities. In the conventional spacing generating models for the prediction of yield was possible in the populations of 55 and 60 thousand plants ha-1 respectively at 30 and 90 DAE.

Winter Wheat Yield Prediction Using Normalized Difference Vegetative Index and Agro-Climatic Parameters in Oklahoma

2017 ◽  
Vol 109 (6) ◽  
pp. 2700-2713 ◽  
Author(s):  
Ning Zhang ◽  
Chen Zhao ◽  
Steven M. Quiring ◽  
Jinlin Li
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Yield Prediction ◽  
Climatic Parameters ◽  
Normalized Difference Vegetative Index ◽  
Winter Wheat Yield

scholarly journals Selection of microbial biomarkers with genetic algorithm and principal component analysis

2019 ◽  
Vol 20 (S6) ◽  
Author(s):  
Ping Zhang ◽  
Nicholas P. West ◽  
Pin-Yen Chen ◽  
Mike W. C. Thang ◽  
Gareth Price ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Models ◽  
Bacterial Species ◽  
Principal Component ◽  
Biological Studies ◽  
Microbial Biomarkers ◽  
Highly Correlated ◽  
Selection Of

Abstract Background Principal components analysis (PCA) is often used to find characteristic patterns associated with certain diseases by reducing variable numbers before a predictive model is built, particularly when some variables are correlated. Usually, the first two or three components from PCA are used to determine whether individuals can be clustered into two classification groups based on pre-determined criteria: control and disease group. However, a combination of other components may exist which better distinguish diseased individuals from healthy controls. Genetic algorithms (GAs) can be useful and efficient for searching the best combination of variables to build a prediction model. This study aimed to develop a prediction model that combines PCA and a genetic algorithm (GA) for identifying sets of bacterial species associated with obesity and metabolic syndrome (Mets). Results The prediction models built using the combination of principal components (PCs) selected by GA were compared to the models built using the top PCs that explained the most variance in the sample and to models built with selected original variables. The advantages of combining PCA with GA were demonstrated. Conclusions The proposed algorithm overcomes the limitation of PCA for data analysis. It offers a new way to build prediction models that may improve the prediction accuracy. The variables included in the PCs that were selected by GA can be combined with flexibility for potential clinical applications. The algorithm can be useful for many biological studies where high dimensional data are collected with highly correlated variables.

Drought indices and prediction models for winter wheat

2011 ◽  
Vol 19 (4) ◽  
pp. 860-865
Author(s):  
Xi-Yan KANG ◽  
Guang-Qin GU ◽  
Yin-Shan SHI ◽  
Guo-Qiang TIAN ◽  
Yong-Li GU
Keyword(s):  
Winter Wheat ◽  
Prediction Models ◽  
Drought Indices

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

scholarly journals A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 285
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Prediction Model ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Prediction ◽  
Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

scholarly journals The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model Evidence from India

2021 ◽  
Vol 14 (7) ◽  
pp. 333
Author(s):  
Shilpa H. Shetty ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Ownership Structure ◽  
Financial Distress ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Financial Measures ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Distress Prediction

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

scholarly journals Early Detection of Fungal Diseases in Winter Wheat by Multi-optical Sensors

2014 ◽  
Vol 8 ◽  
pp. 199-203 ◽  
Author(s):  
Yuxuan Wang ◽  
Shamaila Zia ◽  
Sebastian Owusu-Adu ◽  
Roland Gerhards ◽  
Joachim Müller
Keyword(s):  
Early Detection ◽  
Winter Wheat ◽  
Optical Sensors ◽  
Fungal Diseases
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