Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System
E-agriculture is the integration of technology and digital mechanisms into agricultural processes for more efficient output. This study provided a machine learning–aided mobile system for farmland optimization, using various inputs such as location, crop type, soil type, soil pH, and spacing. Random...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Scientifica |
| Online Access: | http://dx.doi.org/10.1155/2020/9428281 |
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| author | Marion Olubunmi Adebiyi Roseline Oluwaseun Ogundokun Aneoghena Amarachi Abokhai |
| author_facet | Marion Olubunmi Adebiyi Roseline Oluwaseun Ogundokun Aneoghena Amarachi Abokhai |
| author_sort | Marion Olubunmi Adebiyi |
| collection | DOAJ |
| description | E-agriculture is the integration of technology and digital mechanisms into agricultural processes for more efficient output. This study provided a machine learning–aided mobile system for farmland optimization, using various inputs such as location, crop type, soil type, soil pH, and spacing. Random forest algorithm and BigML were employed to analyze and classify datasets containing crop features that generated subclasses based on random crop feature parameters. The subclasses were further grouped into three main classes to match the crops using data from the companion crops. The study concluded that the approach aided decision making and also assisted in the design of a mobile application using Appery.io. This Appery.io then took in some user input parameters, thereby offering various optimization sets. It was also deduced that the system led to users’ optimization of information when implemented on their farmlands. |
| format | Article |
| id | doaj-art-e7c6f711d7a241a4a21ce6d62226f471 |
| institution | Kabale University |
| issn | 2090-908X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Scientifica |
| spelling | doaj-art-e7c6f711d7a241a4a21ce6d62226f4712025-02-03T01:06:18ZengWileyScientifica2090-908X2020-01-01202010.1155/2020/94282819428281Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring SystemMarion Olubunmi Adebiyi0Roseline Oluwaseun Ogundokun1Aneoghena Amarachi Abokhai2Department of Computer Science, Landmark University, Omu-Aran, Kwara State, NigeriaDepartment of Computer Science, Landmark University, Omu-Aran, Kwara State, NigeriaDepartment of Computer Science, Landmark University, Omu-Aran, Kwara State, NigeriaE-agriculture is the integration of technology and digital mechanisms into agricultural processes for more efficient output. This study provided a machine learning–aided mobile system for farmland optimization, using various inputs such as location, crop type, soil type, soil pH, and spacing. Random forest algorithm and BigML were employed to analyze and classify datasets containing crop features that generated subclasses based on random crop feature parameters. The subclasses were further grouped into three main classes to match the crops using data from the companion crops. The study concluded that the approach aided decision making and also assisted in the design of a mobile application using Appery.io. This Appery.io then took in some user input parameters, thereby offering various optimization sets. It was also deduced that the system led to users’ optimization of information when implemented on their farmlands.http://dx.doi.org/10.1155/2020/9428281 |
| spellingShingle | Marion Olubunmi Adebiyi Roseline Oluwaseun Ogundokun Aneoghena Amarachi Abokhai Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System Scientifica |
| title | Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System |
| title_full | Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System |
| title_fullStr | Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System |
| title_full_unstemmed | Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System |
| title_short | Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System |
| title_sort | machine learning based predictive farmland optimization and crop monitoring system |
| url | http://dx.doi.org/10.1155/2020/9428281 |
| work_keys_str_mv | AT marionolubunmiadebiyi machinelearningbasedpredictivefarmlandoptimizationandcropmonitoringsystem AT roselineoluwaseunogundokun machinelearningbasedpredictivefarmlandoptimizationandcropmonitoringsystem AT aneoghenaamarachiabokhai machinelearningbasedpredictivefarmlandoptimizationandcropmonitoringsystem |