A Deep Learning-Based Sensor Modeling for Smart Irrigation System
<p dir="ltr">The use of Internet of things (IoT)-based physical sensors to perceive the environment is a prevalent and global approach. However, one major problem is the reliability of physical sensors’ nodes, which creates difficulty in a real-time system to identify whether the phy...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2022
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513506114535424 |
|---|---|
| author | Maira Sami (19547806) |
| author2 | Saad Qasim Khan (19547809) Muhammad Khurram (3123207) Muhammad Umar Farooq (9727687) Rukhshanda Anjum (19547812) Saddam Aziz (19547815) Rizwan Qureshi (15279193) Ferhat Sadak (16904730) |
| author2_role | author author author author author author author |
| author_facet | Maira Sami (19547806) Saad Qasim Khan (19547809) Muhammad Khurram (3123207) Muhammad Umar Farooq (9727687) Rukhshanda Anjum (19547812) Saddam Aziz (19547815) Rizwan Qureshi (15279193) Ferhat Sadak (16904730) |
| author_role | author |
| dc.creator.none.fl_str_mv | Maira Sami (19547806) Saad Qasim Khan (19547809) Muhammad Khurram (3123207) Muhammad Umar Farooq (9727687) Rukhshanda Anjum (19547812) Saddam Aziz (19547815) Rizwan Qureshi (15279193) Ferhat Sadak (16904730) |
| dc.date.none.fl_str_mv | 2022-01-16T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/agronomy12010212 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Deep_Learning-Based_Sensor_Modeling_for_Smart_Irrigation_System/26947000 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Agricultural biotechnology Information and computing sciences Data management and data science neural networks artificial intelligence sensor reliability agritech precision agriculture Recurrent Neural Networks sensor modeling |
| dc.title.none.fl_str_mv | A Deep Learning-Based Sensor Modeling for Smart Irrigation System |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The use of Internet of things (IoT)-based physical sensors to perceive the environment is a prevalent and global approach. However, one major problem is the reliability of physical sensors’ nodes, which creates difficulty in a real-time system to identify whether the physical sensor is transmitting correct values or malfunctioning due to external disturbances affecting the system, such as noise. In this paper, the use of Long Short-Term Memory (LSTM)-based neural networks is proposed as an alternate approach to address this problem. The proposed solution is tested for a smart irrigation system, where a physical sensor is replaced by a neural sensor. The Smart Irrigation System (SIS) contains several physical sensors, which transmit temperature, humidity, and soil moisture data to calculate the transpiration in a particular field. The real-world values are taken from an agriculture field, located in a field of lemons near the Ghadap Sindh province of Pakistan. The LM35 sensor is used for temperature, DHT-22 for humidity, and we designed a customized sensor in our lab for the acquisition of moisture values. The results of the experiment show that the proposed deep learning-based neural sensor predicts the real-time values with high accuracy, especially the temperature values. The humidity and moisture values are also in an acceptable range. Our results highlight the possibility of using a neural network, referred to as a neural sensor here, to complement the functioning of a physical sensor deployed in an agriculture field in order to make smart irrigation systems more reliable.</p><h2>Other Information</h2><p dir="ltr">Published in: Agronomy<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/agronomy12010212" target="_blank">https://dx.doi.org/10.3390/agronomy12010212</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e269c7a50bc272bb9e9cf2b2356e5462 |
| identifier_str_mv | 10.3390/agronomy12010212 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26947000 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Deep Learning-Based Sensor Modeling for Smart Irrigation SystemMaira Sami (19547806)Saad Qasim Khan (19547809)Muhammad Khurram (3123207)Muhammad Umar Farooq (9727687)Rukhshanda Anjum (19547812)Saddam Aziz (19547815)Rizwan Qureshi (15279193)Ferhat Sadak (16904730)Agricultural, veterinary and food sciencesAgricultural biotechnologyInformation and computing sciencesData management and data scienceneural networksartificial intelligencesensor reliabilityagritechprecision agricultureRecurrent Neural Networkssensor modeling<p dir="ltr">The use of Internet of things (IoT)-based physical sensors to perceive the environment is a prevalent and global approach. However, one major problem is the reliability of physical sensors’ nodes, which creates difficulty in a real-time system to identify whether the physical sensor is transmitting correct values or malfunctioning due to external disturbances affecting the system, such as noise. In this paper, the use of Long Short-Term Memory (LSTM)-based neural networks is proposed as an alternate approach to address this problem. The proposed solution is tested for a smart irrigation system, where a physical sensor is replaced by a neural sensor. The Smart Irrigation System (SIS) contains several physical sensors, which transmit temperature, humidity, and soil moisture data to calculate the transpiration in a particular field. The real-world values are taken from an agriculture field, located in a field of lemons near the Ghadap Sindh province of Pakistan. The LM35 sensor is used for temperature, DHT-22 for humidity, and we designed a customized sensor in our lab for the acquisition of moisture values. The results of the experiment show that the proposed deep learning-based neural sensor predicts the real-time values with high accuracy, especially the temperature values. The humidity and moisture values are also in an acceptable range. Our results highlight the possibility of using a neural network, referred to as a neural sensor here, to complement the functioning of a physical sensor deployed in an agriculture field in order to make smart irrigation systems more reliable.</p><h2>Other Information</h2><p dir="ltr">Published in: Agronomy<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/agronomy12010212" target="_blank">https://dx.doi.org/10.3390/agronomy12010212</a></p>2022-01-16T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/agronomy12010212https://figshare.com/articles/journal_contribution/A_Deep_Learning-Based_Sensor_Modeling_for_Smart_Irrigation_System/26947000CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/269470002022-01-16T09:00:00Z |
| spellingShingle | A Deep Learning-Based Sensor Modeling for Smart Irrigation System Maira Sami (19547806) Agricultural, veterinary and food sciences Agricultural biotechnology Information and computing sciences Data management and data science neural networks artificial intelligence sensor reliability agritech precision agriculture Recurrent Neural Networks sensor modeling |
| status_str | publishedVersion |
| title | A Deep Learning-Based Sensor Modeling for Smart Irrigation System |
| title_full | A Deep Learning-Based Sensor Modeling for Smart Irrigation System |
| title_fullStr | A Deep Learning-Based Sensor Modeling for Smart Irrigation System |
| title_full_unstemmed | A Deep Learning-Based Sensor Modeling for Smart Irrigation System |
| title_short | A Deep Learning-Based Sensor Modeling for Smart Irrigation System |
| title_sort | A Deep Learning-Based Sensor Modeling for Smart Irrigation System |
| topic | Agricultural, veterinary and food sciences Agricultural biotechnology Information and computing sciences Data management and data science neural networks artificial intelligence sensor reliability agritech precision agriculture Recurrent Neural Networks sensor modeling |