Intelligent monitoring and control of farmland based on edge-cloud collaboration and digital twin for digital energy management: investment benefit analysis
Abstract The current farmland energy management and monitoring system still has problems, such as poor real-time data collection, low energy utilization efficiency, and insufficient intelligent decision-making. Focusing on digital energy management, this paper proposes a data collection and analysis...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | Sustainable Energy Research |
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| Online Access: | https://doi.org/10.1186/s40807-025-00179-7 |
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| author | Zheng Liu |
| author_facet | Zheng Liu |
| author_sort | Zheng Liu |
| collection | DOAJ |
| description | Abstract The current farmland energy management and monitoring system still has problems, such as poor real-time data collection, low energy utilization efficiency, and insufficient intelligent decision-making. Focusing on digital energy management, this paper proposes a data collection and analysis based on edge computing and cloud collaboration architecture to improve the accuracy and real-time performance of farmland environmental monitoring. In terms of intelligent control, deep reinforcement learning is used to optimize irrigation decision-making, and adaptive algorithms are combined to improve the flexibility of agricultural equipment scheduling. Regarding energy management, a digital twin model of the photovoltaic energy storage system is constructed to achieve accurate prediction and optimization of energy flow. Edge-cloud collaborative architecture for real-time data collection/analysis, reducing network latency by 40% compared to traditional cloud-only models; deep reinforcement learning (DRL)-driven irrigation optimization, achieving 51% crop yield increase and 18% water efficiency improvement; digital twin modeling of photovoltaic-energy storage systems, enhancing energy flow prediction accuracy to 98.2% and reducing energy waste by 9.5%; game theory-based resource allocation to balance energy supply–demand, improving system economic benefits by 15%. The system stability reached 96.24%, and the maintenance cost was reduced by 21.0%. The utilization rate of irrigation water increased from 76.9% to 43.0% by 1.8 times, reaching 77.4%. |
| format | Article |
| id | doaj-art-0f8f1dde00bb46b2acf112202e50ccb0 |
| institution | Kabale University |
| issn | 2731-9237 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Sustainable Energy Research |
| spelling | doaj-art-0f8f1dde00bb46b2acf112202e50ccb02025-08-20T03:46:17ZengSpringerOpenSustainable Energy Research2731-92372025-07-0112111310.1186/s40807-025-00179-7Intelligent monitoring and control of farmland based on edge-cloud collaboration and digital twin for digital energy management: investment benefit analysisZheng Liu0Department of Business, Huanghe S & T UniversityAbstract The current farmland energy management and monitoring system still has problems, such as poor real-time data collection, low energy utilization efficiency, and insufficient intelligent decision-making. Focusing on digital energy management, this paper proposes a data collection and analysis based on edge computing and cloud collaboration architecture to improve the accuracy and real-time performance of farmland environmental monitoring. In terms of intelligent control, deep reinforcement learning is used to optimize irrigation decision-making, and adaptive algorithms are combined to improve the flexibility of agricultural equipment scheduling. Regarding energy management, a digital twin model of the photovoltaic energy storage system is constructed to achieve accurate prediction and optimization of energy flow. Edge-cloud collaborative architecture for real-time data collection/analysis, reducing network latency by 40% compared to traditional cloud-only models; deep reinforcement learning (DRL)-driven irrigation optimization, achieving 51% crop yield increase and 18% water efficiency improvement; digital twin modeling of photovoltaic-energy storage systems, enhancing energy flow prediction accuracy to 98.2% and reducing energy waste by 9.5%; game theory-based resource allocation to balance energy supply–demand, improving system economic benefits by 15%. The system stability reached 96.24%, and the maintenance cost was reduced by 21.0%. The utilization rate of irrigation water increased from 76.9% to 43.0% by 1.8 times, reaching 77.4%.https://doi.org/10.1186/s40807-025-00179-7Digital energy managementIntelligent monitoring of farmlandDeep reinforcement learningInvestment benefit analysis |
| spellingShingle | Zheng Liu Intelligent monitoring and control of farmland based on edge-cloud collaboration and digital twin for digital energy management: investment benefit analysis Sustainable Energy Research Digital energy management Intelligent monitoring of farmland Deep reinforcement learning Investment benefit analysis |
| title | Intelligent monitoring and control of farmland based on edge-cloud collaboration and digital twin for digital energy management: investment benefit analysis |
| title_full | Intelligent monitoring and control of farmland based on edge-cloud collaboration and digital twin for digital energy management: investment benefit analysis |
| title_fullStr | Intelligent monitoring and control of farmland based on edge-cloud collaboration and digital twin for digital energy management: investment benefit analysis |
| title_full_unstemmed | Intelligent monitoring and control of farmland based on edge-cloud collaboration and digital twin for digital energy management: investment benefit analysis |
| title_short | Intelligent monitoring and control of farmland based on edge-cloud collaboration and digital twin for digital energy management: investment benefit analysis |
| title_sort | intelligent monitoring and control of farmland based on edge cloud collaboration and digital twin for digital energy management investment benefit analysis |
| topic | Digital energy management Intelligent monitoring of farmland Deep reinforcement learning Investment benefit analysis |
| url | https://doi.org/10.1186/s40807-025-00179-7 |
| work_keys_str_mv | AT zhengliu intelligentmonitoringandcontroloffarmlandbasedonedgecloudcollaborationanddigitaltwinfordigitalenergymanagementinvestmentbenefitanalysis |