Comparative analysis of data using machine learning algorithms: A hydroponics system use case
This paper makes a comparison of machine learning algorithms for the analysis of four hydroponic datasets. Data have been gathered daily from hydroponic systems to predict the output of the hydroponic systems. This research compares the performance of the federated split Learning, Deep neural networ...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375523000370 |
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| author | Godwin Idoje Christos Mouroutoglou Tasos Dagiuklas Anastasios Kotsiras Iqbal Muddesar Panagiotis Alefragkis |
| author_facet | Godwin Idoje Christos Mouroutoglou Tasos Dagiuklas Anastasios Kotsiras Iqbal Muddesar Panagiotis Alefragkis |
| author_sort | Godwin Idoje |
| collection | DOAJ |
| description | This paper makes a comparison of machine learning algorithms for the analysis of four hydroponic datasets. Data have been gathered daily from hydroponic systems to predict the output of the hydroponic systems. This research compares the performance of the federated split Learning, Deep neural network, extreme Gradient Boosting (XGBoost), and Linear regression algorithms on four different hydroponic systems. These algorithms have been used to analyze the datasets of Nutrient Film Technic (NFT), Floating (FL), Aggregate (AG) and Aeroponic (AER) hydroponic systems. The results have indicated the performance of each model for each hydroponic system and how each algorithm have used the various multiple input features to make predictions of the onion bulb diameter and the errors encountered by each model. From the results obtained, it has been observed that the R square score is varied for each hydroponic system. This variation in the result has been also reflected in the Mean absolute errors obtained. This research determines which of the algorithms predict the optimal Onion bulb diameter (mm) using days after transplant (days), Temperature (°C), water consumption (Litres), Number of Leaves (NL), Nitrogen (mg/g), Phosphorus (mg/g), Potassium (mg/g), Calcium (mg/g), Magnesium (mg/g), Sulphur (mg/g), Sodium (mg/g) as independent variables. The results will be a guide in the choice of hydroponic system to adopt for food production based on the climatic parameters of the location, which is one of the numerous contributions of this research. |
| first_indexed | 2024-04-09T15:43:13Z |
| format | Article |
| id | doaj.art-8af360128d2340be817f3d444e3f21a4 |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-04-09T15:43:13Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-8af360128d2340be817f3d444e3f21a42023-04-27T06:08:56ZengElsevierSmart Agricultural Technology2772-37552023-08-014100207Comparative analysis of data using machine learning algorithms: A hydroponics system use caseGodwin Idoje0Christos Mouroutoglou1Tasos Dagiuklas2Anastasios Kotsiras3Iqbal Muddesar4Panagiotis Alefragkis5Computer Science and Informatics Division, London South Bank University, United Kingdom; Corresponding author at: Computer Science, London South Bank University, 103 Borough Road, London, SE1 0AA, UK.Department of Agriculture, University of Peloponnese, Kalamata, GreeceComputer Science and Informatics Division, London South Bank University, United KingdomDepartment of Agriculture, University of Peloponnese, Kalamata, GreeceComputer Science and Informatics Division, London South Bank University, United KingdomDepartment of Electrical and Computer Engineering, University of Peloponnese, Kalamata, GreeceThis paper makes a comparison of machine learning algorithms for the analysis of four hydroponic datasets. Data have been gathered daily from hydroponic systems to predict the output of the hydroponic systems. This research compares the performance of the federated split Learning, Deep neural network, extreme Gradient Boosting (XGBoost), and Linear regression algorithms on four different hydroponic systems. These algorithms have been used to analyze the datasets of Nutrient Film Technic (NFT), Floating (FL), Aggregate (AG) and Aeroponic (AER) hydroponic systems. The results have indicated the performance of each model for each hydroponic system and how each algorithm have used the various multiple input features to make predictions of the onion bulb diameter and the errors encountered by each model. From the results obtained, it has been observed that the R square score is varied for each hydroponic system. This variation in the result has been also reflected in the Mean absolute errors obtained. This research determines which of the algorithms predict the optimal Onion bulb diameter (mm) using days after transplant (days), Temperature (°C), water consumption (Litres), Number of Leaves (NL), Nitrogen (mg/g), Phosphorus (mg/g), Potassium (mg/g), Calcium (mg/g), Magnesium (mg/g), Sulphur (mg/g), Sodium (mg/g) as independent variables. The results will be a guide in the choice of hydroponic system to adopt for food production based on the climatic parameters of the location, which is one of the numerous contributions of this research.http://www.sciencedirect.com/science/article/pii/S2772375523000370Federated split learningNutrient Film TechnicAggregateAeroponicsFloating hydroponicsMean absolute error |
| spellingShingle | Godwin Idoje Christos Mouroutoglou Tasos Dagiuklas Anastasios Kotsiras Iqbal Muddesar Panagiotis Alefragkis Comparative analysis of data using machine learning algorithms: A hydroponics system use case Smart Agricultural Technology Federated split learning Nutrient Film Technic Aggregate Aeroponics Floating hydroponics Mean absolute error |
| title | Comparative analysis of data using machine learning algorithms: A hydroponics system use case |
| title_full | Comparative analysis of data using machine learning algorithms: A hydroponics system use case |
| title_fullStr | Comparative analysis of data using machine learning algorithms: A hydroponics system use case |
| title_full_unstemmed | Comparative analysis of data using machine learning algorithms: A hydroponics system use case |
| title_short | Comparative analysis of data using machine learning algorithms: A hydroponics system use case |
| title_sort | comparative analysis of data using machine learning algorithms a hydroponics system use case |
| topic | Federated split learning Nutrient Film Technic Aggregate Aeroponics Floating hydroponics Mean absolute error |
| url | http://www.sciencedirect.com/science/article/pii/S2772375523000370 |
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