Statistical analysis for a non-invasive methane gas and carbon dioxide measurer for ruminants

Cruz-Rivero, Lidilia; Mateo-Diaz, Nicolas Francisco; Purroy-Vásquez, Rubén; Angeles-Herrera, D.; Osorio-Cruz, Francisco

doi:10.1109/icev50249.2020.9289668

Cited by 3 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 10 shows the correlation between variables-the brighter the color, the higher the correlation between variables. We can see a high correlation between PM 2.5 and PM 10 . We can see the correlation between each of the pollutant concentrations with respect to the cattle count.…”

Section: Data Statisticsmentioning

confidence: 76%

“…Our Detectron2 model had an input dimension of 256 × 256 × 3 with deep convolutional layers. RetinaNet, a bottom-up pathway and onestage detection model that uses a focus loss function, was used for calculating the cattle count [10]. The input dimensions on the first convolutional layer were given as 64 × 64 × 3.…”

Section: Model Development 41 Model Proposalmentioning

confidence: 99%

“…The collection, monitoring, and analysis of data on the impact of cattle farms on air quality and environmental effects are necessary to understand the situation better and take appropriate actions [9]. It can also help food firms produce healthier food methods that release less hazardous emissions [10]. With the help of remote sensing data, the severity of each of the air contaminants was estimated and plotted on a map for specific geographic areas.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven Air Quality and Environmental Evaluation for Cattle Farms

Jagtap

Ravichandran

et al. 2023

Atmosphere

View full text Add to dashboard Cite

The expansion of agricultural practices and the raising of animals are key contributors to air pollution. Cattle farms contain hazardous gases, so we developed a cattle farm air pollution analyzer to count the number of cattle and provide comprehensive statistics on different air pollutant concentrations based on severity over various time periods. The modeling was performed in two parts: the first stage focused on object detection using satellite data of farm images to identify and count the number of cattle; the second stage predicted the next hour air pollutant concentration of the seven cattle farm air pollutants considered. The output from the second stage was then visualized based on severity, and analytics were performed on the historical data. The visualization illustrates the relationship between cattle count and air pollutants, an important factor for analyzing the pollutant concentration trend. We proposed the models Detectron2, YOLOv4, RetinaNet, and YOLOv5 for the first stage, and LSTM (single/multi lag), CNN-LSTM, and Bi-LSTM for the second stage. YOLOv5 performed best in stage one with an average precision of 0.916 and recall of 0.912, with the average precision and recall for all models being above 0.87. For stage two, CNN-LSTM performed well with an MAE of 3.511 and an MAPE of 0.016, while a stacked model had an MAE of 5.010 and an MAPE of 0.023.

show abstract

Section: Data Statisticsmentioning

confidence: 76%

Section: Model Development 41 Model Proposalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Air Quality and Environmental Evaluation for Cattle Farms

Jagtap

Ravichandran

et al. 2023

Atmosphere

View full text Add to dashboard Cite

show abstract

“…This aspect is mainly relevant when a cross comparison has to be operated among animals in terms of genetic make-up or feeding. In [26], a prototype that has been developed for measuring the single cattle emission of CH4 and CO2 is presented. The prototype consists of: (i) two gas sensors (i.e., one for CH4 and one CO2), (ii) a transceiver, (iii) a support type structure attached to the head of the animal comprising straps that are attached to this structure, and a (iv) a battery.…”

Section: • Capability To Relate the Emission Measurement To Individua...mentioning

confidence: 99%

A measurement system for enteric CH4 emissions monitoring from ruminants in livestock farming

Verde,

Guerriero,

Bonavolonta

et al. 2023

Acta IMEKO

View full text Add to dashboard Cite

In this paper, a proposal for an Internet-of-Things (IoT) based measurement system dealing with the enteric methane (CH4) emission monitoring from ruminants is presented. Herein, a brief overview of the recent advances in sensors technologies and their IoT integration for realizing measurement systems able to monitor the CH4 emissions in ruminants is also presented. Nowadays, it is confirmed that CH4 emissions, which are mainly produced during normal fermentation of feeds by the rumen microorganisms, are part of the Green-House Gas (GHG) emissions. Therefore, a classification of the existing measurement methods, sensing technologies and their impact on the animal’s welfare is presented. The proposed measurement system, together with its sensing elements and the developed data acquisition system are also reported in this paper. A preliminary disposal and field trials of the developed system in a farm facility is given.

show abstract

“…The prototype was statistically analyzed to verify that the proposed design is functional [9]. This device presents an alternative to measure GHG without harming the animal; however, it is necessary to carry out functionality studies, so an FMEA is used to evaluate the possibilities of failures, to explain the possible causes and the effects they can represent.…”

Section: Introductionmentioning

confidence: 99%

Functional Evaluation Using Fuzzy FMEA for a Non-Invasive Measurer for Methane and Carbone Dioxide

et al. 2022

Self Cite

View full text Add to dashboard Cite

This paper combines the use of two tools: Failure Mode and Effect Analysis (FMEA) and Fuzzy Logic (FL), to evaluate the functionality of a quantifier prototype of Methane gas (CH4) and Carbon Dioxide (CO2), developed specifically to measure the emissions generated by cattle. Unlike previously reported models for the same purpose, this device reduces damage to the integrity of the animal and does not interfere with the activities of livestock in their development medium. FMEA and FL are used to validate the device’s functionality, which involves identifying possible failure modes that represent a more significant impact on the operation and prevent the prototype from fulfilling the function for which it was created. As a result, this document presents the development of an intelligent fuzzy system type Mamdani, supported in the Fuzzy Inference System Toolbox of MatLabR2018b®, for generating a risk priority index. A Fuzzy FMEA model was obtained to validate the prototype for measuring Methane and Carbon Dioxide emissions, which allows considering this prototype as a reliable alternative for the reliable measurement of these gases. This study was necessary as a complementary part in the validation of the design of the prototype quantifier of CH4 and CO2 emissions. The methods used (classic FMEA and Fuzzy FMEA) to evaluate the RPN show asymmetric graphs due to data disparity. Values in the classical method are mostly lower than the Mamdani model results due to the description of the criteria with which it is evaluated.

show abstract

Statistical analysis for a non-invasive methane gas and carbon dioxide measurer for ruminants

Cited by 3 publications

References 14 publications

Data-Driven Air Quality and Environmental Evaluation for Cattle Farms

Data-Driven Air Quality and Environmental Evaluation for Cattle Farms

A measurement system for enteric CH4 emissions monitoring from ruminants in livestock farming

Functional Evaluation Using Fuzzy FMEA for a Non-Invasive Measurer for Methane and Carbone Dioxide

Contact Info

Product

Resources

About