Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach
Maintaining optimal microclimatic conditions within greenhouses represents a significant challenge in modern agricultural contexts, where prediction systems play a crucial role in regulating temperature and humidity, thereby enabling timely interventions to prevent plant diseases or adverse growth c...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/1999-5903/17/5/214 |
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| author | Cristian Bua Francesco Fiorini Michele Pagano Davide Adami Stefano Giordano |
| author_facet | Cristian Bua Francesco Fiorini Michele Pagano Davide Adami Stefano Giordano |
| author_sort | Cristian Bua |
| collection | DOAJ |
| description | Maintaining optimal microclimatic conditions within greenhouses represents a significant challenge in modern agricultural contexts, where prediction systems play a crucial role in regulating temperature and humidity, thereby enabling timely interventions to prevent plant diseases or adverse growth conditions. In this work, we propose a novel approach which integrates a cascaded Feed-Forward Neural Network (FFNN) with the Granular Computing paradigm to achieve accurate microclimate forecasting and reduced computational complexity. The experimental results demonstrate that the accuracy of our approach is the same as that of the FFNN-based approach but the complexity is reduced, making this solution particularly well suited for deployment on edge devices with limited computational capabilities. Our innovative approach has been validated using a real-world dataset collected from four greenhouses and integrated into a distributed network architecture. This setup supports the execution of predictive models both on sensors deployed within the greenhouse and at the network edge, where more computationally intensive models can be utilized to enhance decision-making accuracy. |
| format | Article |
| id | doaj-art-295e7ac6f1aa49afa3a0fc021c6a1d6c |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-295e7ac6f1aa49afa3a0fc021c6a1d6c2025-08-20T03:47:57ZengMDPI AGFuture Internet1999-59032025-05-0117521410.3390/fi17050214Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural ApproachCristian Bua0Francesco Fiorini1Michele Pagano2Davide Adami3Stefano Giordano4Department of Information Engineering, University of Pisa, Via G. Caruso, 16, 56122 Pisa, ItalyDepartment of Information Engineering, University of Pisa, Via G. Caruso, 16, 56122 Pisa, ItalyDepartment of Information Engineering, University of Pisa, Via G. Caruso, 16, 56122 Pisa, ItalyDepartment of Information Engineering, CNIT—University of Pisa, Via G. Caruso 16, 56122 Pisa, ItalyDepartment of Information Engineering, University of Pisa, Via G. Caruso, 16, 56122 Pisa, ItalyMaintaining optimal microclimatic conditions within greenhouses represents a significant challenge in modern agricultural contexts, where prediction systems play a crucial role in regulating temperature and humidity, thereby enabling timely interventions to prevent plant diseases or adverse growth conditions. In this work, we propose a novel approach which integrates a cascaded Feed-Forward Neural Network (FFNN) with the Granular Computing paradigm to achieve accurate microclimate forecasting and reduced computational complexity. The experimental results demonstrate that the accuracy of our approach is the same as that of the FFNN-based approach but the complexity is reduced, making this solution particularly well suited for deployment on edge devices with limited computational capabilities. Our innovative approach has been validated using a real-world dataset collected from four greenhouses and integrated into a distributed network architecture. This setup supports the execution of predictive models both on sensors deployed within the greenhouse and at the network edge, where more computationally intensive models can be utilized to enhance decision-making accuracy.https://www.mdpi.com/1999-5903/17/5/214neural networkfuzzy setgranular computingmicroclimate classificationcomplexity reductionsmart agriculture |
| spellingShingle | Cristian Bua Francesco Fiorini Michele Pagano Davide Adami Stefano Giordano Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach Future Internet neural network fuzzy set granular computing microclimate classification complexity reduction smart agriculture |
| title | Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach |
| title_full | Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach |
| title_fullStr | Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach |
| title_full_unstemmed | Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach |
| title_short | Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach |
| title_sort | low complexity microclimate classification in smart greenhouses a fuzzy neural approach |
| topic | neural network fuzzy set granular computing microclimate classification complexity reduction smart agriculture |
| url | https://www.mdpi.com/1999-5903/17/5/214 |
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