Prediction of crop production using adaboost regression method

Koduri, Shirin Bhanu; Gunisetti, Loshma; Ramesh, Ch.; Mutyalu, K V; Ganesh, D.

doi:10.1088/1742-6596/1228/1/012005

Cited by 14 publications

(10 citation statements)

References 5 publications

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“…AdaBoost reduces the loss function; thus, outliers in the data can create vulnerability in the algorithm. The weak classifiers have their performance enhanced through the introduction of reinforced training on samples that are classified as erroneous [90]. The classification error function is utilized in AdaBoost to boost weak classifier weights.…”

Section: Dataset Acquisition and Processing In The Literaturementioning

confidence: 99%

Smart Water Quality Monitoring with IoT Wireless Sensor Networks

Singh,

Walingo

2024

Sensors

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Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a MAE¯ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a MAE¯ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll.

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Section: Dataset Acquisition and Processing In The Literaturementioning

confidence: 99%

Smart Water Quality Monitoring with IoT Wireless Sensor Networks

Singh,

Walingo

2024

Sensors

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“…The literature on these strategies has been thoroughly examined and debated. Khaki, S. et al [12] used and evaluated various classification models such as Ordinary Least Square (OLS) [13], Least Absolute Shrinkage and Selection operator (LASSO) [14], Back Propagation Neural Network (BPNN) [15], Gaussian Process Regression (GPR) [16], Ensembled Classifiers [17], Support Vector Machines Regression (SVR), RF [18], AdaBoost [19], General Regression Neural Network (GRNN) [20], Multiswarm Firefly Algorithm [21] and Deep Neural Network (DNN) [22] to estimate winter wheat production during the growing season in the United States at the county level and identified AdaBoost as the best approach.…”

Section: Literature Reviewmentioning

confidence: 99%

A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron

Ahmed

2023

Sustainability

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Predicting crop yields is one of agriculture’s most challenging issues. It is crucial in making national, provincial, and regional choices and estimates the government to meet the food demands of its citizens. Crop production is anticipated based on various factors such as soil conditions and meteorological, environmental, and crop variables. This study intends to develop an effective model that can accurately anticipate agricultural production in advance, assisting farmers in better planning. In the current study, the Crop Yield Prediction Dataset is normalized initially, and then feature engineering is performed to determine the significance of the feature in assessing the crop yield. Crop yield forecasting is performed using the Multi-Layer Perceptron model and the Spider Monkey Optimization method. The Multi-Layer Perceptron technique is efficient in dealing with the non-linear relations among the features in the data, and the Spider Monkey Optimization technique would assist in optimizing the corresponding feature weights. The current study uses data from the Food and Agriculture Organization and the World Data Bank to forecast maize yield in the Saudi Arabia region based on factors such as average temperature, average rainfall, and Hg/Ha production in past years. The suggested MLP-SMO model’s prediction effectiveness is being evaluated using several evaluation metrics such as Root-Mean-Square Error, R-Squared, Mean Absolute Error, and Mean Bias Error, where the model has outperformed in the prediction process with a Root-Mean-Square Error value of 0.11, which is lowest among all the techniques that are considered in the statical analysis in the current study.

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“…AB-R is a regression method that implements a set of weak learners such as a simple decision tree which is added sequentially at each iteration. The number of iterations was determined as needed [28]. At each iteration, the data was re-weighted based on the error from the previous weak learner.…”

Section: Machine Learning Testingmentioning

confidence: 99%

Data Augmentation Technique Using Two Step SMOTE for Electronic-nose Signal in Breath Ketone Level Detection

2023

IJIES

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Breath acetone concentrations were found to be correlated with blood ketone levels. Based on this evidence, predicting blood ketone levels using breath analysis and machine learning (ML) becomes possible. Nevertheless, a good ML model requires a large amount of training data. Under certain conditions, it is difficult to collect large amounts of data such as during the Covid-19 pandemic. To overcome this problem, we propose an augmentation technique to extend the number of training datasets using two step synthetic minority oversampling (SMOTE). The first step was to increase the amount of training data by combining it with synthetic data, while the second step was to balance the data at each ketone level. The strategy for using SMOTE with regression was further explained since this study aims to predict ketone levels with numerical output values and SMOTE is typically used in classification cases. The proposed method was evaluated by entering the data into several ML methods such as deep neural network regression (DNN-R), linear regression (ML-R), ransac regression (RC-R), K-nearest neighbour regression (KNN-R), decision tree regression (DT-R), random forest regression (RF-R), Ada boost regression (AD-R), Gradient boost regression (GB-R) and XG-boost regression (XGB-R). Based on the test results, when compared without the proposed method, an increase in accuracy was obtained on

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Prediction of crop production using adaboost regression method

Cited by 14 publications

References 5 publications

Smart Water Quality Monitoring with IoT Wireless Sensor Networks

Smart Water Quality Monitoring with IoT Wireless Sensor Networks

A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron

Data Augmentation Technique Using Two Step SMOTE for Electronic-nose Signal in Breath Ketone Level Detection

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