Applicability of Machine-Learned Regression Models to Estimate Internal Air Temperature and CO2 Concentration of a Pig House

Yeo, Uk-Hyeon; Jo, Seng-Kyoun; Kim, Se-Han; Park, Dae-Heon; Jeong, Deuk-Young; Park, Se-Jun; Shin, Hakjong; Kim, Rack-woo

doi:10.3390/agronomy13020328

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…To evaluate the performance of these various algorithms [62][63][64][65], the authors used several metrics, including mean-squared error (MSE), root-mean-squared error (RMSE), mean absolute error (MAE), and R-squared (R 2 ) as…”

Section: Selection Of Best Suited Modelsmentioning

confidence: 99%

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Pukrongta,

Taparugssanagorn,

Sangpradit

2024

Applied Sciences

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This research introduces the PEnsemble 4 model, a weighted ensemble prediction model that integrates multiple individual machine learning models to achieve accurate maize yield forecasting. The model incorporates unmanned aerial vehicle (UAV) imagery and Internet of Things (IoT)-based environmental data, providing a comprehensive and data-driven approach to yield prediction in maize cultivation. Considering the projected growth in global maize demand and the vulnerability of maize crops to weather conditions, improved prediction capabilities are of paramount importance. The PEnsemble 4 model addresses this need by leveraging comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the model effectively analyzes temporal patterns in vegetation indices, in particular CIre and NDRE, which serve as reliable indicators of canopy density and plant height. Notably, the PEnsemble 4 model demonstrates a remarkable accuracy rate of 91%. It advances the timeline for yield prediction from the conventional reproductive stage (R6) to the blister stage (R2), enabling earlier estimation and enhancing decision-making processes in farming operations. Moreover, the model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. By synergistically integrating IoT and machine learning technologies, the PEnsemble 4 model presents a novel and promising solution for maize yield prediction. Its application holds the potential to revolutionize crop management and protection, contributing to efficient and sustainable farming practices.

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Section: Selection Of Best Suited Modelsmentioning

confidence: 99%

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Pukrongta,

Taparugssanagorn,

Sangpradit

2024

Applied Sciences

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“…Research has been conducted on predicting CO 2 mass concentration based on traditional machine learning methods, and some results have been obtained in predicting CO 2 concentrations in pig houses [ 14 ], composting environments [ 15 ], and building construction environments [ 16 , 17 ], and with regard to urban carbon emissions [ 18 , 19 ], crop CO 2 emissions [ 20 ], and ambient air pollution [ 21 , 22 ]. Although these CO 2 mass concentration prediction models can express the trends of internal changes of CO 2 in the environment and achieve certain prediction results, they require large amounts of valid data as experimental support, which creates a large and tedious workload.…”

Section: Introductionmentioning

confidence: 99%

A Method to Predict CO2 Mass Concentration in Sheep Barns Based on the RF-PSO-LSTM Model

Cen

et al. 2023

Animals

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In large-scale meat sheep farming, high CO2 concentrations in sheep sheds can lead to stress and harm the healthy growth of meat sheep, so a timely and accurate understanding of the trend of CO2 concentration and early regulation are essential to ensure the environmental safety of sheep sheds and the welfare of meat sheep. In order to accurately understand and regulate CO2 concentrations in sheep barns, we propose a prediction method based on the RF-PSO-LSTM model. The approach we propose has four main parts. First, to address the problems of data packet loss, distortion, singular values, and differences in the magnitude of the ambient air quality data collected from sheep sheds, we performed data preprocessing using mean smoothing, linear interpolation, and data normalization. Second, to address the problems of many types of ambient air quality parameters in sheep barns and possible redundancy or overlapping information, we used a random forests algorithm (RF) to screen and rank the features affecting CO2 mass concentration and selected the top four features (light intensity, air relative humidity, air temperature, and PM2.5 mass concentration) as the input of the model to eliminate redundant information among the variables. Then, to address the problem of manually debugging the hyperparameters of the long short-term memory model (LSTM), which is time consuming and labor intensive, as well as potentially subjective, we used a particle swarm optimization (PSO) algorithm to obtain the optimal combination of parameters, avoiding the disadvantages of selecting hyperparameters based on subjective experience. Finally, we trained the LSTM model using the optimized parameters obtained by the PSO algorithm to obtain the proposed model in this paper. The experimental results show that our proposed model has a root mean square error (RMSE) of 75.422 μg·m−3, a mean absolute error (MAE) of 51.839 μg·m−3, and a coefficient of determination (R2) of 0.992. The model prediction curve is close to the real curve and has a good prediction effect, which can be useful for the accurate prediction and regulation of CO2 concentration in sheep barns in large-scale meat sheep farming.

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“…Korean scientists have assessed the impact of livestock farms on carbon dioxide emissions using machine learning models, including ElasticNet, RFR, and SVR. They found that the random forest model provides the best reproducibility of the experimental data [19].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in the Analysis of Carbon Dioxide Flow on a Site with Heterogeneous Vegetation

Kulakova,

Muravyova

2023

Information

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The article presents the results of studies of carbon dioxide flow in the territory of section No. 5 of the Eurasian Carbon Polygon (Russia, Republic of Bashkortostan). The gas analyzer Sniffer4D V2.0 (manufactured in Shenzhen, China) with an installed CO2 sensor, quadrocopter DJI MATRICE 300 RTK (manufactured in Shenzhen, China) were used as control devices. The studies were carried out on a clear autumn day in conditions of green vegetation and on a frosty November day with snow cover. Statistical characteristics of experimental data arrays are calculated. Studies of the influence of temperature, humidity of atmospheric air on the current value of CO2 have been carried out. Graphs of the distribution of carbon dioxide concentration in the atmospheric air of section No. 5 on autumn and winter days were obtained. It has been established that when building a model of CO2 in the air, the parameters of the process of deposition by green vegetation should be considered. It was found that in winter, an increase in air humidity contributes to a decrease in gas concentration. At an ambient temperature of 21 °C, an increase in humidity leads to an increase in the concentration of carbon dioxide.

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Applicability of Machine-Learned Regression Models to Estimate Internal Air Temperature and CO2 Concentration of a Pig House

Cited by 4 publications

References 21 publications

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

A Method to Predict CO2 Mass Concentration in Sheep Barns Based on the RF-PSO-LSTM Model

Machine Learning in the Analysis of Carbon Dioxide Flow on a Site with Heterogeneous Vegetation

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