AIoT in Agriculture: Safeguarding Crops from Pest and Disease Threats
A significant proportion of the world’s agricultural production is lost to pests and diseases. To mitigate this problem, an AIoT system for the early detection of pest and disease risks in crops is proposed. It presents a system based on low-power and low-cost sensor nodes that collect environmental...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/24/9733 |
| _version_ | 1827573574463913984 |
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| author | Pedro Blanco-Carmona Lucía Baeza-Moreno Eduardo Hidalgo-Fort Rubén Martín-Clemente Ramón González-Carvajal Fernando Muñoz-Chavero |
| author_facet | Pedro Blanco-Carmona Lucía Baeza-Moreno Eduardo Hidalgo-Fort Rubén Martín-Clemente Ramón González-Carvajal Fernando Muñoz-Chavero |
| author_sort | Pedro Blanco-Carmona |
| collection | DOAJ |
| description | A significant proportion of the world’s agricultural production is lost to pests and diseases. To mitigate this problem, an AIoT system for the early detection of pest and disease risks in crops is proposed. It presents a system based on low-power and low-cost sensor nodes that collect environmental data and transmit it once a day to a server via a NB-IoT network. In addition, the sensor nodes use individual, retrainable and updatable machine learning algorithms to assess the risk level in the crop every 30 min. If a risk is detected, environmental data and the risk level are immediately sent. Additionally, the system enables two types of notification: email and flashing LED, providing online and offline risk notifications. As a result, the system was deployed in a real-world environment and the power consumption of the sensor nodes was characterized, validating their longevity and the correct functioning of the risk detection algorithms. This allows the farmer to know the status of their crop and to take early action to address these threats. |
| first_indexed | 2024-03-08T20:23:13Z |
| format | Article |
| id | doaj.art-972dc0ecca234599bbf6477d4fa3d806 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T20:23:13Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-972dc0ecca234599bbf6477d4fa3d8062023-12-22T14:40:26ZengMDPI AGSensors1424-82202023-12-012324973310.3390/s23249733AIoT in Agriculture: Safeguarding Crops from Pest and Disease ThreatsPedro Blanco-Carmona0Lucía Baeza-Moreno1Eduardo Hidalgo-Fort2Rubén Martín-Clemente3Ramón González-Carvajal4Fernando Muñoz-Chavero5Department of Electronic Engineering, University of Seville, 41092 Seville, SpainDepartment of Electronic Engineering, University of Seville, 41092 Seville, SpainDepartment of Electronic Engineering, University of Seville, 41092 Seville, SpainDepartment of Signal Processing and Communications, University of Seville, 41092 Seville, SpainDepartment of Electronic Engineering, University of Seville, 41092 Seville, SpainDepartment of Electronic Engineering, University of Seville, 41092 Seville, SpainA significant proportion of the world’s agricultural production is lost to pests and diseases. To mitigate this problem, an AIoT system for the early detection of pest and disease risks in crops is proposed. It presents a system based on low-power and low-cost sensor nodes that collect environmental data and transmit it once a day to a server via a NB-IoT network. In addition, the sensor nodes use individual, retrainable and updatable machine learning algorithms to assess the risk level in the crop every 30 min. If a risk is detected, environmental data and the risk level are immediately sent. Additionally, the system enables two types of notification: email and flashing LED, providing online and offline risk notifications. As a result, the system was deployed in a real-world environment and the power consumption of the sensor nodes was characterized, validating their longevity and the correct functioning of the risk detection algorithms. This allows the farmer to know the status of their crop and to take early action to address these threats.https://www.mdpi.com/1424-8220/23/24/9733Internet of Things (IoT)Wireless Sensor Network (WSN)NB-IoTsmart agricultureArtificial Intelligence (AI) |
| spellingShingle | Pedro Blanco-Carmona Lucía Baeza-Moreno Eduardo Hidalgo-Fort Rubén Martín-Clemente Ramón González-Carvajal Fernando Muñoz-Chavero AIoT in Agriculture: Safeguarding Crops from Pest and Disease Threats Sensors Internet of Things (IoT) Wireless Sensor Network (WSN) NB-IoT smart agriculture Artificial Intelligence (AI) |
| title | AIoT in Agriculture: Safeguarding Crops from Pest and Disease Threats |
| title_full | AIoT in Agriculture: Safeguarding Crops from Pest and Disease Threats |
| title_fullStr | AIoT in Agriculture: Safeguarding Crops from Pest and Disease Threats |
| title_full_unstemmed | AIoT in Agriculture: Safeguarding Crops from Pest and Disease Threats |
| title_short | AIoT in Agriculture: Safeguarding Crops from Pest and Disease Threats |
| title_sort | aiot in agriculture safeguarding crops from pest and disease threats |
| topic | Internet of Things (IoT) Wireless Sensor Network (WSN) NB-IoT smart agriculture Artificial Intelligence (AI) |
| url | https://www.mdpi.com/1424-8220/23/24/9733 |
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