AdaBoost with Neural Networks for Yield and Protein Prediction in Precision Agriculture

Peerlinck, Amy; Sheppard, John W.; Senecal, Jacob

doi:10.1109/ijcnn.2019.8851976

Cited by 14 publications

(13 citation statements)

References 18 publications

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“…classifiers, e.g., k-nearest neighbor (KNN) [61], support vector machines (SVM) [62], random forest (RF) [63], gradient boosting decision tree (GBDT) [64], Naï ve Bayes classifier (NB) [65], logistic regression (LR) [66], light gradient boosting machine (LightGBM) [67], extreme gradient boosting (XGBoost) [54], and adaptive boosting (AdaBoost) [68]. Step…”

Section: Stacked Ensemble Classifiermentioning

confidence: 99%

StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

Zhang

Liu

Wang

et al. 2021

Applied Soft Computing

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DNA binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research.Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075，93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive  Corresponding author.

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Section: Stacked Ensemble Classifiermentioning

confidence: 99%

StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

Zhang

Liu

Wang

et al. 2021

Applied Soft Computing

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“…The results showed improvements over other shallow neural networks and simple linear and non-linear regression models. Peerlinck et al 14 also proposed to use an Approximation AdaBoost algorithm with feedforward neural networks (FNNs) as weak learners. This method approximates the loss function by using a threshold approach that discards small errors during the weight update of the weak learners.…”

Section: Related Workmentioning

confidence: 99%

“…Some of them rely only on remotely sensed data, such as Moderate Resolution Imaging Spectroradiometer (MODIS) or Sentinel satellite imagery, 11,12 while others incorporate on-ground data, such as soil electroconductivity or nitrogen rate. 13,14 The common goal is to train a regression model to estimate the crop yield in terms of bushels per acre (bu/ac) as accurately as possible given some input information. All previous works have focused on predicting the yield values of single georeferenced points of the field, which represent small regions of the field (e.g.…”

Section: Introductionmentioning

confidence: 99%

Two-dimensional Deep Regression for Early Yield Prediction of Winter Wheat

Morales¹,

Sheppard²

2021

Preprint

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Crop yield prediction is one of the tasks of Precision Agriculture that can be automated based on multi-source periodic observations of the fields. We tackle the yield prediction problem using a Convolutional Neural Network (CNN) trained on data that combines radar satellite imagery and on-ground information. We present a CNN architecture called Hyper3DNetReg that takes in a multi-channel input image and outputs a two-dimensional raster, where each pixel represents the predicted yield value of the corresponding input pixel. We utilize radar data acquired from the Sentinel-1 satellites, while the on-ground data correspond to a set of six raster features: nitrogen rate applied, precipitation, slope, elevation, topographic position index (TPI), and aspect. We use data collected during the early stage of the winter wheat growing season (March) to predict yield values during the harvest season (August). We present experiments over four fields of winter wheat and show that our proposed methodology yields better results than five compared methods, including multiple linear regression, an ensemble of feedforward networks using AdaBoost, a stacked autoencoder, and two other CNN architectures.

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Section: Stacked Ensemble Classifiermentioning

confidence: 99%

StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

Zhang

Liu

et al. 2020

Preprint

View full text Add to dashboard Cite

DNA binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research. Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075, 93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive ability to predict DBPs.

show abstract

AdaBoost with Neural Networks for Yield and Protein Prediction in Precision Agriculture

Cited by 14 publications

References 18 publications

StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

Two-dimensional Deep Regression for Early Yield Prediction of Winter Wheat

StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

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