Autonomous, Multisensory Soil Monitoring System
The research investigates the advantages of real-time soil quality monitoring for various land management applications. We emphasize the crucial role of soil modeling and mapping by visualizing and understanding aridity trends across different regions. The primary objective is to develop an innovati...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/7/1/18 |
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| author | Valentina-Daniela Băjenaru Simona-Elena Istrițeanu Paul-Nicolae Ancuța |
| author_facet | Valentina-Daniela Băjenaru Simona-Elena Istrițeanu Paul-Nicolae Ancuța |
| author_sort | Valentina-Daniela Băjenaru |
| collection | DOAJ |
| description | The research investigates the advantages of real-time soil quality monitoring for various land management applications. We emphasize the crucial role of soil modeling and mapping by visualizing and understanding aridity trends across different regions. The primary objective is to develop an innovative soil monitoring system utilizing Internet of Things (IoT) technology. This system, equipped with intelligent sensors, will operate autonomously, collecting real-time data to identify key trends in soil conditions. Our system employs smart soil sensors to measure macronutrient values up to a depth of 80 cm. These sensors will transmit data wirelessly. Laboratory research involved a two-month evaluation of the system’s performance across three distinct soil types collected from diverse geographical locations. Analysis of the three soil types yielded a model accuracy estimate of 0.01. A strong positive linear correlation (0.92) between moisture and macronutrients has been observed in two out of the three soil types. The results, particularly related to soil moisture, were averaged over the testing period. While precipitation values were not directly integrated into the modeling framework, they were calculated in l/m<sup>2</sup> to ensure accurate real-time estimates. The need for such advanced monitoring systems is critical for optimizing key soil macronutrients and enabling spatiotemporal mapping. This information is essential for developing effective strategies to mitigate soil aridification and prevent desertification. |
| format | Article |
| id | doaj-art-a9c9ef3865994494bcac5a9918cb2be8 |
| institution | Kabale University |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-a9c9ef3865994494bcac5a9918cb2be82025-01-24T13:16:16ZengMDPI AGAgriEngineering2624-74022025-01-01711810.3390/agriengineering7010018Autonomous, Multisensory Soil Monitoring SystemValentina-Daniela Băjenaru0Simona-Elena Istrițeanu1Paul-Nicolae Ancuța2Intelligent Thermo-Technical Measurements Department, National Institute of Research and Development in Mechatronics and Measurement Technique, 021631 Bucharest, RomaniaEnvironmental Engineering and Renewable Energy Systems Department, National Institute of Research and Development in Mechatronics and Measurement Technique, 021631 Bucharest, RomaniaComplex Automation and Control Systems Department, National Institute of Research and Development in Mechatronics and Measurement Technique, 021631 Bucharest, RomaniaThe research investigates the advantages of real-time soil quality monitoring for various land management applications. We emphasize the crucial role of soil modeling and mapping by visualizing and understanding aridity trends across different regions. The primary objective is to develop an innovative soil monitoring system utilizing Internet of Things (IoT) technology. This system, equipped with intelligent sensors, will operate autonomously, collecting real-time data to identify key trends in soil conditions. Our system employs smart soil sensors to measure macronutrient values up to a depth of 80 cm. These sensors will transmit data wirelessly. Laboratory research involved a two-month evaluation of the system’s performance across three distinct soil types collected from diverse geographical locations. Analysis of the three soil types yielded a model accuracy estimate of 0.01. A strong positive linear correlation (0.92) between moisture and macronutrients has been observed in two out of the three soil types. The results, particularly related to soil moisture, were averaged over the testing period. While precipitation values were not directly integrated into the modeling framework, they were calculated in l/m<sup>2</sup> to ensure accurate real-time estimates. The need for such advanced monitoring systems is critical for optimizing key soil macronutrients and enabling spatiotemporal mapping. This information is essential for developing effective strategies to mitigate soil aridification and prevent desertification.https://www.mdpi.com/2624-7402/7/1/18smart sensorsmechatronicsdesertification |
| spellingShingle | Valentina-Daniela Băjenaru Simona-Elena Istrițeanu Paul-Nicolae Ancuța Autonomous, Multisensory Soil Monitoring System AgriEngineering smart sensors mechatronics desertification |
| title | Autonomous, Multisensory Soil Monitoring System |
| title_full | Autonomous, Multisensory Soil Monitoring System |
| title_fullStr | Autonomous, Multisensory Soil Monitoring System |
| title_full_unstemmed | Autonomous, Multisensory Soil Monitoring System |
| title_short | Autonomous, Multisensory Soil Monitoring System |
| title_sort | autonomous multisensory soil monitoring system |
| topic | smart sensors mechatronics desertification |
| url | https://www.mdpi.com/2624-7402/7/1/18 |
| work_keys_str_mv | AT valentinadanielabajenaru autonomousmultisensorysoilmonitoringsystem AT simonaelenaistriteanu autonomousmultisensorysoilmonitoringsystem AT paulnicolaeancuta autonomousmultisensorysoilmonitoringsystem |