A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters

Elavarasan, Dhivya; Vincent, Pascal

doi:10.1007/s12652-020-02752-y

Cited by 54 publications

(29 citation statements)

References 46 publications

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“…ML-based models such as SVM took less than 4.31 seconds and 0.72 seconds for [4,12], respectively. Less computation time is required for real-time decision-making [18][19][20][21][22][23]. The future scope of the present investigation is to utilize more image datasets and reduce computation time by optimizing parameters and utilizing TPU based cloud computing.…”

Section: Limitations and Computational Complexity Of Proposed Modelmentioning

confidence: 99%

CNN Based Automated Weed Detection System Using UAV Imagery

Haq¹

2022

Computer Systems Science and Engineering

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The problem of weeds in crops is a natural problem for farmers. Machine Learning (ML), Deep Learning (DL), and Unmanned Aerial Vehicles (UAV) are among the advanced technologies that should be used in order to reduce the use of pesticides while also protecting the environment and ensuring the safety of crops. Deep Learning-based crop and weed identification systems have the potential to save money while also reducing environmental stress. The accuracy of ML/DL models has been proven to be restricted in the past due to a variety of factors, including the selection of an efficient wavelength, spatial resolution, and the selection and tuning of hyperparameters. The purpose of the current research is to develop a new automated weed detecting system that uses Convolution Neural Network (CNN) classification for a real dataset of 4400 UAV pictures with 15336 segments. Snapshots were used to choose the optimal parameters for the proposed CNN LVQ model. The soil class achieved the user accuracy of 100% with the proposed CNN LVQ model, followed by soybean (99.79%), grass (98.58%), and broadleaf (98.32%). The developed CNN LVQ model showed an overall accuracy of 99.44% after rigorous hyperparameter tuning for weed detection, significantly higher than previously reported studies.

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Section: Limitations and Computational Complexity Of Proposed Modelmentioning

confidence: 99%

CNN Based Automated Weed Detection System Using UAV Imagery

Haq¹

2022

Computer Systems Science and Engineering

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“…Producing enough food for evolving population explosion has become the major concerns for the global world. Agriculture in aspect of core contributor in food production is ensuring to meet the sustainable food availability [1]. Food security has been considered as the foremost global threat, and therefore, it is essential to steer strategies to determine policies for future food security and sustainable food availability [2,3].…”

Section: Significances Motivations and Objectives Of The Studymentioning

confidence: 99%

A Prediction Model Optimization Critiques through Centroid Clustering by Reducing the Sample Size, Integrating Statistical and Machine Learning Techniques for Wheat Productivity

Islam

Shehzad

2022

Scientifica

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Machine learning algorithms are rapidly deploying and have made manifold breakthroughs in various fields. The optimization of algorithms got abundant attention of researchers being a core component for deploying the machine learning model (MLM) abled to learn the parameters in significant ways for the given data. Modeling crop productivity through innumerable agronomical constraints has become a crucial task for evolving sustainable agricultural policies. The cross-sectional datasets of 26430 (D1) crop-cut experiments are taken by 2nd-stage area frame sampling, collected from crop reporting service. This research is taken as follows: firstly three more effective numerical optimized datasets are generated (D1, D2, and D3) from D1 by taking the centroid points of features which decrease the sample size; secondly MLM is integrated with the traditional statistical models (TSMs) for multiple linear regression (MLR), and thirdly decision tree regression (DTR) and random forest regression (RFR) are deployed to get the optimized models able to predict the wheat productivity well with 75% datasets to train and 25% to test the model using the evaluation metrics (R2, RMSE), information criterion (AIC) with weights (AICW), evidence ration (E.R), and decompositions of prediction error. The MLR outperformed for MLM than TSM. The performance capability of MLM and TSM got upswing for generated datasets. RFR got optimized and superperformed for D1, D2, D3, and D4. This study demonstrated strong evidences for deploying MLM for prediction of wheat productivity as an alternative of traditional statistical modeling.

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“…RF reduces the dimensionality in the database by using a built-in feature selection system that can regulate multiple parameters without removing some of them (Tella et al, 2021). By estimating the increase in prediction error in the dataset, RF can compute the variable importance scores of each constituent tree and also the entire database (Elavarasan and Vincent, 2021;Pal and Paul, 2020). Studies like Chan and Paelinckx, (2008) and Pal and Mather, (2003) reported that RF (uses bagging) is more sensitive to noise than the algorithms based on boosting technique.…”

Section: Random Forest Algorithm Uses Bootstrap Aggregation Technique While Growing Multiple Decisionmentioning

confidence: 99%

Point and pixel inclusive machine learning models for exploring gully erosion susceptibility

Pal

Debanshi

Paul³

2022

Preprint

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Sample point based spatial model derived from Machine Learning (ML) algorithms often generalizes the spatial pattern of event. The present study has tried to highlight how far it is acceptable and can it replace pixel based modelling? The present study has presented a comparative view of pixel and sample point based modelling of gully erosion susceptibility of the upper Mayurakshi basin to assess the predictabilities. Random forest (RF), Support Vector Machine (SVM) and (ADB) were applied for developing pixel and point based models in WEKA and Python software environments respectively. From the models, it is found that 14-25% area located mainly in the upper parts of the study unit is very highly susceptible to gully erosion. Based on the accuracy and performance level using Area under curve (AUC) of Receiver operating curve, sensitivity, precision, F1 score, MCC pixel based ensemble models are superior to point based modelling. Often, the point based models have a very poor agreement with training and testing data. So, pixel based models could not be replaced with point based models. RF is found as the best representative model. The study, therefore, recommends using pixel based modelling for this or a similar purpose. Since the models have figured out the gully erosion susceptible areas, it would be a useful tool for related planning processes.

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A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters

Cited by 54 publications

References 46 publications

CNN Based Automated Weed Detection System Using UAV Imagery

CNN Based Automated Weed Detection System Using UAV Imagery

A Prediction Model Optimization Critiques through Centroid Clustering by Reducing the Sample Size, Integrating Statistical and Machine Learning Techniques for Wheat Productivity

Point and pixel inclusive machine learning models for exploring gully erosion susceptibility

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