Technologies Driving the Shift to Smart Farming: A Review
<p>As today’s agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment, and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgradin...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | |
| منشور في: |
2022
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513562728202240 |
|---|---|
| author | Nabila ElBeheiry (16904583) |
| author2 | Robert S. Balog (16904586) |
| author2_role | author |
| author_facet | Nabila ElBeheiry (16904583) Robert S. Balog (16904586) |
| author_role | author |
| dc.creator.none.fl_str_mv | Nabila ElBeheiry (16904583) Robert S. Balog (16904586) |
| dc.date.none.fl_str_mv | 2022-11-29T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/jsen.2022.3225183 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Technologies_Driving_the_Shift_to_Smart_Farming_A_Review/24056277 |
| 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 Agriculture, land and farm management Engineering Control engineering, mechatronics and robotics Environmental sciences Climate change impacts and adaptation Information and computing sciences Distributed computing and systems software Machine learning Sensors Sensor phenomena and characterization Temperature sensors Wireless sensor networks Smart agriculture Climate change Capacitive sensors Actuators Automation Data analysis Deep learning (DL) Internet of Things (IoT) Irrigation systems Lowpower wide area network (LPWAN) Machine learning (ML) Microcontrollers Remote monitoring Robotics Smart farming (SF) Wireless sensor networks (WSNs) |
| dc.title.none.fl_str_mv | Technologies Driving the Shift to Smart Farming: A Review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>As today’s agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment, and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading farming practices by shifting toward smart farming (SF)—utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify suitable technologies and best practices for a particular application. This article provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers’ contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, this article discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.</p><h2>Other Information</h2><p>Published in: IEEE Sensors Journal<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/jsen.2022.3225183" target="_blank">https://dx.doi.org/10.1109/jsen.2022.3225183</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0e07eb874770e4e3cdcfbda7a656aaea |
| identifier_str_mv | 10.1109/jsen.2022.3225183 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056277 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Technologies Driving the Shift to Smart Farming: A ReviewNabila ElBeheiry (16904583)Robert S. Balog (16904586)Agricultural, veterinary and food sciencesAgriculture, land and farm managementEngineeringControl engineering, mechatronics and roboticsEnvironmental sciencesClimate change impacts and adaptationInformation and computing sciencesDistributed computing and systems softwareMachine learningSensorsSensor phenomena and characterizationTemperature sensorsWireless sensor networksSmart agricultureClimate changeCapacitive sensorsActuatorsAutomationData analysisDeep learning (DL)Internet of Things (IoT)Irrigation systemsLowpower wide area network (LPWAN)Machine learning (ML)MicrocontrollersRemote monitoringRoboticsSmart farming (SF)Wireless sensor networks (WSNs)<p>As today’s agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment, and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading farming practices by shifting toward smart farming (SF)—utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify suitable technologies and best practices for a particular application. This article provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers’ contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, this article discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.</p><h2>Other Information</h2><p>Published in: IEEE Sensors Journal<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/jsen.2022.3225183" target="_blank">https://dx.doi.org/10.1109/jsen.2022.3225183</a></p>2022-11-29T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/jsen.2022.3225183https://figshare.com/articles/journal_contribution/Technologies_Driving_the_Shift_to_Smart_Farming_A_Review/24056277CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240562772022-11-29T00:00:00Z |
| spellingShingle | Technologies Driving the Shift to Smart Farming: A Review Nabila ElBeheiry (16904583) Agricultural, veterinary and food sciences Agriculture, land and farm management Engineering Control engineering, mechatronics and robotics Environmental sciences Climate change impacts and adaptation Information and computing sciences Distributed computing and systems software Machine learning Sensors Sensor phenomena and characterization Temperature sensors Wireless sensor networks Smart agriculture Climate change Capacitive sensors Actuators Automation Data analysis Deep learning (DL) Internet of Things (IoT) Irrigation systems Lowpower wide area network (LPWAN) Machine learning (ML) Microcontrollers Remote monitoring Robotics Smart farming (SF) Wireless sensor networks (WSNs) |
| status_str | publishedVersion |
| title | Technologies Driving the Shift to Smart Farming: A Review |
| title_full | Technologies Driving the Shift to Smart Farming: A Review |
| title_fullStr | Technologies Driving the Shift to Smart Farming: A Review |
| title_full_unstemmed | Technologies Driving the Shift to Smart Farming: A Review |
| title_short | Technologies Driving the Shift to Smart Farming: A Review |
| title_sort | Technologies Driving the Shift to Smart Farming: A Review |
| topic | Agricultural, veterinary and food sciences Agriculture, land and farm management Engineering Control engineering, mechatronics and robotics Environmental sciences Climate change impacts and adaptation Information and computing sciences Distributed computing and systems software Machine learning Sensors Sensor phenomena and characterization Temperature sensors Wireless sensor networks Smart agriculture Climate change Capacitive sensors Actuators Automation Data analysis Deep learning (DL) Internet of Things (IoT) Irrigation systems Lowpower wide area network (LPWAN) Machine learning (ML) Microcontrollers Remote monitoring Robotics Smart farming (SF) Wireless sensor networks (WSNs) |