FARMIT: continuous assessment of crop quality using machine learning and deep learning techniques for IoT-based smart farming
The race for automation has reached farms and agricultural fields. Many of these facilities use the Internet of Things technologies to automate processes and increase productivity. Besides, Machine Learning and Deep Learning allow performing continuous decision making based on data analysis. In this...
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| Veröffentlicht in: | Cluster computing 2022-06, Vol.25 (3), p.2163-2178 |
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| container_title | Cluster computing |
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| creator | Perales Gómez, Ángel Luis López-de-Teruel, Pedro E. Ruiz, Alberto García-Mateos, Ginés Bernabé García, Gregorio García Clemente, Félix J. |
| description | The race for automation has reached farms and agricultural fields. Many of these facilities use the Internet of Things technologies to automate processes and increase productivity. Besides, Machine Learning and Deep Learning allow performing continuous decision making based on data analysis. In this work, we fill a gap in the literature and present a novel architecture based on IoT and Machine Learning / Deep Learning technologies for the continuous assessment of agricultural crop quality. This architecture is divided into three layers that work together to gather, process, and analyze data from different sources to evaluate crop quality. In the experiments, the proposed approach based on data aggregation from different sources reaches a lower percentage error than considering only one source. In particular, the percentage error achieved by our approach in the test dataset was 6.59, while the percentage error achieved exclusively using data from sensors was 6.71. |
| doi_str_mv | 10.1007/s10586-021-03489-9 |
| format | Article |
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| subjects | Agriculture Artificial intelligence Automation Communication Computer Communication Networks Computer Science Concurrency control Data analysis Data management Decision analysis Decision making Deep learning Edge computing Farms Greenhouses Internet of Things Machine learning Operating Systems Processor Architectures Product quality Quality assessment Sensors |
| title | FARMIT: continuous assessment of crop quality using machine learning and deep learning techniques for IoT-based smart farming |
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