Modelling methane emissions from pig manure using statistical and machine learning methods

Basak, Jayanta Kumar; Arulmozhi, Elanchezhıan; Moon, Byeong Eun; Bhujel, Anil; Kim, Hyeon Tae

doi:10.1007/s11869-022-01169-0

Cited by 17 publications

(12 citation statements)

References 58 publications

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“…The existence of linear relationships between dependent and independent variables is essential for developing regression-based models to predict outcomes [ 50 ]. A number of studies used simple linear models due to their simplicity of nature and easily interpretation of outcomes [ 42 , 52 , 76 ]. In the present study, we used a linear regression model, i.e., multiple linear regression (MLR) model, in predicting TSS and pH values.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the prediction capabilities of ML models are also influenced by the data partition in the training and testing stages. Several studies used 70:30 (training: testing), 80:20 and 90:10 partition to develop and validate model [ 49 , 50 , 51 , 52 ]. After examining the model performance using the three data partitions (70:30, 80:20 and 90:10), the current study utilized 80% data during the training stage and 20% data during the testing stage.…”

Section: Methodsmentioning

confidence: 99%

“…It is widely used in various fields including yield prediction of crops, weather forecasting, demand calculation of electricity, business forecast, etc. [52,54,55]. The formula of MLR model is represented by the Equation (8).…”

Section: Development Of Multiple Linear Regression (Mlr)mentioning

confidence: 99%

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Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models

Basak

Madhavi

Paudel

et al. 2022

Foods

Self Cite

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Determination of internal qualities such as total soluble solids (TSS) and pH is a paramount concern in strawberry cultivation. Therefore, the main objective of the current study was to develop a non-destructive approach with machine learning algorithms for predicting TSS and pH of strawberries. Six hundred samples (100 samples in each ripening stage) in six ripening stages were collected randomly for measuring the biometrical characteristics, i.e., length, diameters, weight and TSS and pH values. An image of each strawberry fruit was captured for colour feature extraction using an image processing technique. Channels of each colour space (RGB, HSV and HSL) were used as input variables for developing multiple linear regression (MLR) and support vector machine regression (SVM-R) models. The result of the study indicated that SVM-R model with HSV colour space performed slightly better than MLR model for TSS and pH prediction. The HSV based SVM-R model could explain a maximum of 84.1% and 79.2% for TSS and 78.8% and 72.6% for pH of the variations in measured and predicted data in training and testing stages, respectively. Further experiments need to be conducted with different strawberry cultivars for the prediction of more internal qualities along with the improvement of model performance.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models

Basak

Madhavi

Paudel

et al. 2022

Foods

Self Cite

View full text Add to dashboard Cite

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“…Regression models are a standard technique for statistical procedures of prediction that are extensively used in numerous applications and fields [19]. However, machine learning models have been used to overcome the relationship of data and achieve improved prediction accuracy more efficiently [20], [21]. It can be said that one of the key benefits of utilizing statistical models is that they provide intuitive data visualizations that support the discovery of correlations between variables and the formulation of predictions.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Modeling agricultural and methane emission data: a finite mixture regression approach

Thongnim,

Charoenwanit

2024

IJEECS

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In this paper, the method for unsupervised learning of finite mixture regression (FMR) models is presented for evaluation using agricultural and emissions data sets. The FMR models can be written as problems with incomplete data, and the expectation–maximization (EM) algorithm can be used to estimate unknown variables. The goals of this research are to find the best clustering model with different sets of training and test data and examine the relationship between crop production index and methane emissions in 22 countries from 1990 to 2019 using FMR. In this study also use machine learning process for a FMR model from real world data. According to the findings, the performance of the random training data (RDM) in time series is preferable to that of the fixed training data (FXM). In addition, both RDM and FXM are capable of classifying the 22 countries into two distinct groups and constructing the parameters for the regression model. However, selecting training and test data will result in a good prediction; it is dependent on the data collected. Picking the right training and test data is crucial for accurate predictions-it all comes down to having good data in the first place.

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“…Gautam et al [92] developed an offline prediction model to estimate future thermal conditions from building data collected in operation. Basak et al [93,94] used statistical and machine-learning methods to model CH 4 and CO 2 emissions from pig manure. Rodriguez et al [95] developed a CO 2 emission prediction model using neural networks.…”

Section: Applications Of Machine Learningmentioning

confidence: 99%

Environmental Factor Detection and Analysis Technologies in Livestock and Poultry Houses: A Review

et al. 2023

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The environment in livestock and poultry houses plays an important role in the growth and reproduction of livestock and poultry. In order to obtain the environmental conditions of livestock and poultry houses in a timely and reliable manner, and eliminate adverse environmental factors, scholars have been exploring various methods to obtain and predict environmental factors. This paper reviewed the literature from the last 10 years, specifically focusing on technologies for detecting environmental factors in livestock and poultry houses, which can be mainly divided into three categories: research on the environmental monitoring and control of livestock and poultry houses based on detection equipment and wireless sensor technology; research on the distribution and regularity of environmental factors in livestock and poultry houses based on a mathematical model; research on the environmental simulation and detection of livestock and poultry houses based on computer technology. The current testing methods have their advantages and disadvantages. When studying environmental factors, researchers should choose the most appropriate method for data acquisition according to the actual situation. The proposed recommendations for achieving this goal are as follows: (1) The control of environmental factors should be combined with the physiological response of livestock and poultry. The needs of animals should be considered; (2) Novel approaches need to be developed to integrate energy requirements into the environmental regulation of livestock and poultry houses; (3) It is necessary to research and develop control models and strategies that can predict the environment in the houses, and the transient simulation method should be further explored; (4) Improve environmental detection and control accuracy through the coupling of different technologies.

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Modelling methane emissions from pig manure using statistical and machine learning methods

Cited by 17 publications

References 58 publications

Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models

Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models

Modeling agricultural and methane emission data: a finite mixture regression approach

Environmental Factor Detection and Analysis Technologies in Livestock and Poultry Houses: A Review

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