{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T20:00:41Z","timestamp":1775073641972,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T00:00:00Z","timestamp":1747094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["CN00000022"],"award-info":[{"award-number":["CN00000022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/fi17050214","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T09:26:48Z","timestamp":1747128408000},"page":"214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2300-0257","authenticated-orcid":false,"given":"Cristian","family":"Bua","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Pisa, Via G. Caruso, 16, 56122 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5572-3623","authenticated-orcid":false,"given":"Francesco","family":"Fiorini","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Pisa, Via G. Caruso, 16, 56122 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1706-4994","authenticated-orcid":false,"given":"Michele","family":"Pagano","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Pisa, Via G. Caruso, 16, 56122 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3007-1400","authenticated-orcid":false,"given":"Davide","family":"Adami","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, CNIT\u2014University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1408-7528","authenticated-orcid":false,"given":"Stefano","family":"Giordano","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Pisa, Via G. Caruso, 16, 56122 Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21219","DOI":"10.1109\/ACCESS.2022.3152544","article-title":"IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends","volume":"10","author":"Qazi","year":"2022","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/JRFID.2020.2984391","article-title":"Internet of Things Empowered Smart Greenhouse Farming","volume":"4","author":"Rayhana","year":"2020","journal-title":"IEEE J. Radio Freq. Identif."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105587","DOI":"10.1109\/ACCESS.2020.3000175","article-title":"Smart Farming Becomes Even Smarter with Deep Learning\u2014A Bibliographical Analysis","volume":"8","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5393","DOI":"10.1109\/ACCESS.2023.3236663","article-title":"Short-Term Load Forecasting and Associated Weather Variables Prediction Using ResNet-LSTM Based Deep Learning","volume":"11","author":"Chen","year":"2023","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108261","DOI":"10.1016\/j.compag.2023.108261","article-title":"Multistep ahead prediction of temperature and humidity in solar greenhouse based on FAM-LSTM model","volume":"213","author":"Yang","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1002\/int.22620","article-title":"A long short-term memory-based model for greenhouse climate prediction","volume":"37","author":"Liu","year":"2022","journal-title":"Int. J. Intell. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kalyani, Y., and Collier, R. (2021). A Systematic Survey on the Role of Cloud, Fog, and Edge Computing Combination in Smart Agriculture. Sensors, 21.","DOI":"10.3390\/s21175922"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100867","DOI":"10.1109\/ACCESS.2022.3207200","article-title":"Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML)","volume":"10","author":"Zaidi","year":"2022","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Falanji, R., Heusse, M., and Duda, A. (2022). Range and capacity of lora 2.4 ghz. Proceedings of the International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, Springer.","DOI":"10.1007\/978-3-031-34776-4_21"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s11235-019-00557-9","article-title":"A survey on low-power wide area networks for IoT applications","volume":"71","author":"Bembe","year":"2019","journal-title":"Telecommun. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mukherji, A., and Sadu, S. (2016, January 23\u201325). ZigBee performance analysis. Proceedings of the 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India.","DOI":"10.1109\/WiSPNET.2016.7566148"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bua, C., Adami, D., and Giordano, S. (2024). GymHydro: An Innovative Modular Small-Scale Smart Agriculture System for Hydroponic Greenhouses. Electronics, 13.","DOI":"10.3390\/electronics13071366"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100350","DOI":"10.1016\/j.atech.2023.100350","article-title":"Integrating explainable artificial intelligence and blockchain to smart agriculture: Research prospects for decision making and improved security","volume":"6","author":"Chen","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"eaay7120","DOI":"10.1126\/scirobotics.aay7120","article-title":"XAI\u2014Explainable artificial intelligence","volume":"4","author":"Gunning","year":"2019","journal-title":"Sci. Robot."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Balasubramaniam, P., and Babu, N.R. (2024). Cascaded-ANFIS and Its Successful Real-World Applications. Fuzzy Logic Controllers and Applications, IntechOpen. Chapter 3.","DOI":"10.5772\/intechopen.1004663"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rathnayake, N., Miyazaki, A., Dang, T.L., and Hoshino, Y. (2023). Age Classification of Rice Seeds in Japan Using Gradient-Boosting and ANFIS Algorithms. Sensors, 23.","DOI":"10.3390\/s23052828"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., and Sun, J. (2017, January 22\u201329). Channel Pruning for Accelerating Very Deep Neural Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yan, M., Zhao, M., Xu, Z., Zhang, Q., Wang, G., and Su, Z. (2019, January 27\u201328). VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00323"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, S., Lin, L., Zhang, Z., and Gen, M. (2019, January 20\u201322). Evolutionary NetArchitecture Search for Deep Neural Networks Pruning. Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, ACAI \u201919, New York, NY, USA.","DOI":"10.1145\/3377713.3377739"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yao, S., Zhao, Y., Zhang, A., Su, L., and Abdelzaher, T. (2017, January 5\u20137). DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework. Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems, New York, NY, USA. SenSys \u201917.","DOI":"10.1145\/3131672.3131675"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Parashar, A., Rhu, M., Mukkara, A., Puglielli, A., Venkatesan, R., Khailany, B., Emer, J., Keckler, S.W., and Dally, W.J. (2017). SCNN: An Accelerator for Compressed-Sparse Convolutional Neural Networks. arXiv.","DOI":"10.1145\/3079856.3080254"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3287036","article-title":"Performance Characterization of Deep Learning Models for Breathing-Based Authentication on Resource-Constrained Devices","volume":"2","author":"Chauhan","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_23","unstructured":"Han, S., Mao, H., and Dally, W.J. (2016). Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., and Kalenichenko, D. (2017). Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. arXiv.","DOI":"10.1109\/CVPR.2018.00286"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"48046","DOI":"10.1109\/ACCESS.2023.3276438","article-title":"A Study on the Application of TensorFlow Compression Techniques to Human Activity Recognition","volume":"11","author":"Contoli","year":"2023","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Umuroglu, Y., Fraser, N.J., Gambardella, G., Blott, M., Leong, P., Jahre, M., and Vissers, K. (2017, January 22\u201324). FINN: A Framework for Fast, Scalable Binarized Neural Network Inference. Proceedings of the 2017 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA \u201917, New York, NY, USA.","DOI":"10.1145\/3020078.3021744"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Eshratifar, A.E., and Pedram, M. (2020, January 18\u201321). Runtime Deep Model Multiplexing for Reduced Latency and Energy Consumption Inference. Proceedings of the 2020 IEEE 38th International Conference on Computer Design (ICCD), Hartford, CT, USA.","DOI":"10.1109\/ICCD50377.2020.00053"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1145\/3131895","article-title":"Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations","volume":"1","author":"Georgiev","year":"2017","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Abbasi, S., Hajabdollahi, M., Karimi, N., and Samavi, S. (2019). Modeling Teacher-Student Techniques in Deep Neural Networks for Knowledge Distillation. arXiv.","DOI":"10.1109\/MVIP49855.2020.9116923"},{"key":"ref_30","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the Knowledge in a Neural Network. arXiv."},{"key":"ref_31","unstructured":"Mishra, R., Gupta, H.P., and Dutta, T. (2020). A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bua, C., Fiorini, F., Adami, D., Giordano, S., and Pagano, M. (2024, January 29\u201331). Enhancing Climate Prediction in Smart Greenhouse: Synergy of Neural Network and Granular Computing to Reduce Computational Complexity. Proceedings of the 2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Padua, Italy.","DOI":"10.1109\/MetroAgriFor63043.2024.10948813"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"181721","DOI":"10.1109\/ACCESS.2019.2958962","article-title":"A Survey on Deep Learning Empowered IoT Applications","volume":"7","author":"Ma","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1090\/qam\/10666","article-title":"A method for the solution of certain non\u2014Linear problems in least squares","volume":"2","author":"Levenberg","year":"1944","journal-title":"Q. Appl. Math."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.neunet.2021.01.026","article-title":"A survey on modern trainable activation functions","volume":"138","author":"Apicella","year":"2021","journal-title":"Neural Netw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.neucom.2022.06.111","article-title":"Activation functions in deep learning: A comprehensive survey and benchmark","volume":"503","author":"Dubey","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, Y., Zhao, L., Liang, R., and Wang, P. (2022). Comparative Evaluation of the Performance of ZigBee and LoRa Wireless Networks in Building Environment. Electronics, 11.","DOI":"10.3390\/electronics11213560"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s11235-020-00658-w","article-title":"Performance analysis of LoRa in the 2.4 GHz ISM band: Coexistence issues with Wi-Fi","volume":"74","author":"Polak","year":"2020","journal-title":"Telecommun. Syst."},{"key":"ref_39","unstructured":"Longfei, S., and Bodhi, P. (2023). Range and Capacity of LoRa 2.4 GHz. Mobile and Ubiquitous Systems, Proceedings of the Computing, Networking and Services, Melbourne, VIC, Australia, 14\u201317 November 2023, Springer Nature."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1109\/COMST.2024.3372630","article-title":"Securing the IoT Application Layer from an MQTT Protocol Perspective: Challenges and Research Prospects","volume":"26","author":"Lakshminarayana","year":"2024","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bua, C., Borgianni, L., Adami, D., and Giordano, S. (2024, January 22\u201324). Empowering Remote Agriculture: Wearable Glove Control for Smart Hydroponic Greenhouses. Proceedings of the 2024 IEEE 25th International Conference on High Performance Switching and Routing (HPSR), Pisa, Italy.","DOI":"10.1109\/HPSR62440.2024.10635961"},{"key":"ref_42","unstructured":"(2024, November 11). Vantage PRO 2 Weather Station Specifications. Available online: https:\/\/www.meteoproject.it\/davis-vantage-pro2.php."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Pedrycz, W. (2020). An Introduction to Computing with Fuzzy Sets, Springer. Intelligent Systems Reference Library.","DOI":"10.1007\/978-3-030-52800-3"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1515\/intag-2017-0005","article-title":"Review of optimum temperature, humidity, and vapour pressure deficit for microclimate evaluation and control in greenhouse cultivation of tomato: A review","volume":"32","author":"Shamshiri","year":"2018","journal-title":"Int. Agrophysics"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/5\/214\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:31:54Z","timestamp":1760031114000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/5\/214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,13]]},"references-count":44,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["fi17050214"],"URL":"https:\/\/doi.org\/10.3390\/fi17050214","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,13]]}}}