A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses

dc.creatorCama-Pinto, Dora
dc.creatorDamas, Miguel
dc.creatorHolgado-Terriza, Juan Antonio
dc.creatorArrabal Campos, Francisco Manuel
dc.creatorMartínez Lao, Juan Antonio
dc.creatorCama-Pinto, Alejandro
dc.creatorManzano-Agugliaro, Francisco
dc.date2023-01-23T14:58:44Z
dc.date2023-01-23T14:58:44Z
dc.date2023-01-13
dc.date.accessioned2023-10-03T19:25:03Z
dc.date.available2023-10-03T19:25:03Z
dc.identifierCama-Pinto, D.; Damas, M.; Holgado-Terriza, J.A.; Arrabal-Campos, F.M.; Martínez-Lao, J.A.; Cama-Pinto, A.; Manzano-Agugliaro, F. A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses. Agronomy 2023, 13, 244. https:// doi.org/10.3390/agronomy13010244
dc.identifierhttps://hdl.handle.net/11323/9792
dc.identifier10.3390/agronomy13010244
dc.identifier2073-4395
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9170019
dc.descriptionThe production of crops in greenhouses will ensure the demand for food for the world’s population in the coming decades. Precision agriculture is an important tool for this purpose, supported among other things, by the technology of wireless sensor networks (WSN) in the monitoring of agronomic parameters. Therefore, prior planning of the deployment of WSN nodes is relevant because their coverage decreases when the radio waves are attenuated by the foliage of the plantation. In that sense, the method proposed in this study applies Deep Learning to develop an empirical model of radio wave attenuation when it crosses vegetation that includes height and distance between the transceivers of the WSN nodes. The model quality is expressed via the parameters cross-validation, R2 of 0.966, while its generalized error is 0.920 verifying the reliability of the empirical model.
dc.format16 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherMDPI AG
dc.publisherSwitzerland
dc.relationAgronomy
dc.relation1. Bartkowiak, A.M. Energy-saving and low-emission livestock buildings in the concept of a smart farming. J. Water Land Dev. 2021, 51, 272–278. [CrossRef]
dc.relation2. Sagheer, A.; Mohammed, M.; Riad, K.; Alhajhoj, M. A Cloud-Based IoT Platform for Precision Control of Soilless Greenhouse Cultivation. Sensors 2021, 21, 223. [CrossRef] [PubMed]
dc.relation3. Akhigbe, B.; Munir, K.; Akinade, O.; Akanbi, L.; Oyedele, L. IoT Technologies for Livestock Management: A Review of Present Status, Opportunities, and Future Trends. Big Data Cogn. Comput. 2021, 5, 10. [CrossRef]
dc.relation4. Mahbub, M. A smart farming concept based on smart embedded electronics, internet of things and wireless sensor network. Internet Things 2020, 9, 100161. [CrossRef]
dc.relation5. Andrianto, H.; Suhardi; Faizal, A. Development of smart greenhouse system for hydroponic agriculture. In Proceedings of the 2020 International Conference on Information Technology Systems and Innovation, Padang, Indonesia, 19–23 October 2020; pp. 335–340. [CrossRef]
dc.relation6. Latino, M.E.; Menegoli, M.; Corallo, A. Agriculture Digitalization: A Global Examination Based on Bibliometric Analysis. IEEE Trans. Eng. Manag. 2022, 1–16. [CrossRef]
dc.relation7. Ullah, M.; Narayanan, A.; Wolff, A.; Nardelli, P.H.J. Unified Framework to Select an IoT Platform for Industrial Energy Management Systems. In Proceedings of the 2021 44th International Convention on Information, Communication and Electronic Technology, Opatija, Croatia, 27 September–1 October 2021; pp. 950–955. [CrossRef]
dc.relation8. Dastjerdi, A.V.; Buyya, R. Fog Computing: Helping the Internet of Things Realize Its Potential. Computer 2016, 49, 112–116. [CrossRef]
dc.relation9. Pflanzner, T.; Hovari, M.; Vass, I.; Kertesz, A. Designing an IoT-Cloud Gateway for the Internet of Living Things. Commun. Comput. Inf. Sci. 2020, 1218, 23–41. [CrossRef]
dc.relation10. Sorri, K.; Mustafee, N.; Seppänen, M. Revisiting IoT definitions: A framework towards comprehensive use. Technol. Forecast. Soc. Chang. 2022, 179, 121623. [CrossRef]
dc.relation11. Vanitha, V.; Raj, C.S.; Kumar, K.M.; Sebastian, J. Design and Development of an Effective Smart Garbage System using the Internet of Things. J. Physics Conf. Ser. 2021, 2040, 012033. [CrossRef]
dc.relation12. Arrubla-Hoyos, W.; Ojeda-Beltrán, A.; Solano-Barliza, A.; Rambauth-Ibarra, G.; Barrios-Ulloa, A.; Cama-Pinto, D.; ArrabalCampos, F.M.; Martínez-Lao, J.A.; Cama-Pinto, A.; Manzano-Agugliaro, F. Precision Agriculture and Sensor Systems Applications in Colombia through 5G Networks. Sensors 2022, 22, 7295. [CrossRef]
dc.relation13. Barrios-Ulloa, A.; Ariza-Colpas, P.P.; Sánchez-Moreno, H.; Quintero-Linero, A.P.; De la Hoz-Franco, E. Modeling Radio Wave Propagation for Wireless Sensor Networks in Vegetated Environments: A Systematic Literature Review. Sensors 2022, 22, 5285. [CrossRef] [PubMed]
dc.relation14. Sishodia, R.P.; Ray, R.L.; Singh, S.K. Applications of Remote Sensing in Precision Agriculture: A Review. Remote. Sens. 2020, 12, 3136. [CrossRef]
dc.relation15. Suresh, P.; Daniel, J.V.; Parthasarathy, V.; Aswathy, R.H. A state of the art review on the Internet of Things (IoT) history, technology and fields of deployment. In Proceedings of the 2014 International Conference on Science Engineering and Management Research, Chennai, India, 27–29 November 2014. [CrossRef]
dc.relation16. Mahajan, H.B.; Badarla, A. Cross-Layer Protocol for WSN-Assisted IoT Smart Farming Applications Using Nature Inspired Algorithm. Wirel. Pers. Commun. 2021, 121, 3125–3149. [CrossRef]
dc.relation17. Raheemah, A.; Sabri, N.; Salim, M.S.; Ehkan, P.; Kamaruddin, R.; Ahmad, R.B.; Jaafar, M.N.; Aljunid, S.A.; Chemat, M.H. Influences of parts of tree on propagation path losses for wsn deployment in greenhouse environments. J. Theor. Appl. Inf. Technol. 2015, 81, 552–557.
dc.relation18. Li, P.; Peng, Y.; Wang, J. Propagation characteristics of 2.4 GHz radio wave in greenhouse of green peppers. Nongye Jixie Xuebao/Trans. Chin. Soc. Agric. Mach. 2014, 45, 251–255. [CrossRef]
dc.relation19. Raheemah, A.; Sabri, N.; Salim, M.; Ehkan, P.; Ahmad, R.B. New empirical path loss model for wireless sensor networks in mango greenhouses. Comput. Electron. Agric. 2016, 127, 553–560. [CrossRef]
dc.relation20. Vougioukas, S.; Anastassiu, H.; Regen, C.; Zude, M. Influence of foliage on radio path losses (PLs) for wireless sensor network (WSN) planning in orchards. Biosyst. Eng. 2013, 114, 454–465. [CrossRef]
dc.relation21. Cama-Pinto, D.; Damas, M.; Holgado-Terriza, J.A.; Gómez-Mula, F.; Cama-Pinto, A. Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse. Int. J. Environ. Res. Public Health 2019, 16, 1744. [CrossRef]
dc.relation22. Cama-Pinto, D.; Damas, M.; Holgado-Terriza, J.A.; Arrabal-Campos, F.M.; Gómez-Mula, F.; Martínez-Lao, J.A.M.; Cama-Pinto, A. Empirical Model of Radio Wave Propagation in the Presence of Vegetation inside Greenhouses Using Regularized Regressions. Sensors 2020, 20, 6621. [CrossRef]
dc.relation23. França, R.P.; Monteiro, A.C.B.; Arthur, R.; Iano, Y. An Overview of Internet of Things Technology Applied on Precision Agriculture Concept. Precis. Agric. Technol. Food Secur. Sustain. 2020, 47–70. [CrossRef]
dc.relation24. Boursianis, A.D.; Papadopoulou, M.S.; Diamantoulakis, P.; Liopa-Tsakalidi, A.; Barouchas, P.; Salahas, G.; Karagiannidis, G.; Wan, S.; Goudos, S.K. Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet Things 2022, 18, 100187. [CrossRef]
dc.relation25. Sott, M.K.; Nascimento, L.D.S.; Foguesatto, C.R.; Furstenau, L.B.; Faccin, K.; Zawislak, P.A.; Mellado, B.; Kong, J.D.; Bragazzi, N.L. A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure. Sensors 2021, 21, 7889. [CrossRef] [PubMed]
dc.relation26. Khandelwal, D.D.; Singhal, M. Developing a low-cost weather monitoring system for data-sparse regions of the Himalayas. Weather 2021, 76, 60–64. [CrossRef]
dc.relation27. Montoya, F.G.; Gomez, J.; Manzano-Agugliaro, F.; Cama, A.; García-Cruz, A.; De La Cruz, J.L. 6LoWSoft: A software suite for the design of outdoor environmental measurements. J. Food Agric. Environ. 2013, 11, 2584–2586.
dc.relation28. Salim, C.; Mitton, N. K-predictions based data reduction approach in WSN for smart agriculture. Computing 2021, 103, 509–532. [CrossRef]
dc.relation29. Wu, H.; Miao, Y.; Li, F.; Zhu, L. Empirical Modeling and Evaluation of Multi-Path Radio Channels on Wheat Farmland Based on Communication Quality. Trans. ASABE 2016, 59, 759–767. [CrossRef]
dc.relation30. Maiolo, L.; Polese, D. Advances in sensing technologies for smart monitoring in precise agriculture. In Proceedings of the 10th International Conference on Sensor Networks, Online, 9–10 February 2021; pp. 151–158.
dc.relation31. Sathish, C.; Srinivasan, K. An artificial bee colony algorithm for efficient optimized data aggregation to agricultural IoT devices application. J. Appl. Sci. Eng. 2021, 24, 927–936. [CrossRef]
dc.relation32. Hsiao, S.-J.; Sung, W.-T. A Study on Using a Wireless Sensor Network to Design a Plant Monitoring System. Intell. Autom. Soft Comput. 2021, 27, 359–377. [CrossRef]
dc.relation33. Xuanrong, P.; Tingdong, Y.; Yuesheng, W. Research and design of precision irrigation system based on artificial neural network. In Proceedings of the 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China, 9–11 June 2018; pp. 3865–3870.
dc.relation34. Wang, J.; Peng, Y.; Li, P. Propagation Characteristics of Radio Wave in Plastic Greenhouse. In IFIP Advances in Information and Communication Technology; Springer: Cham, Switzerland, 2016; pp. 208–215. [CrossRef]
dc.relation35. Huang, C.-N.; Chan, C.-T. A ZigBee-Based Location-Aware Fall Detection System for Improving Elderly Telecare. Int. J. Environ. Res. Public Health 2014, 11, 4233. [CrossRef]
dc.relation36. Rao, Y.; Jiang, Z.-H.; Lazarovitch, N. Investigating signal propagation and strength distribution characteristics of wireless sensor networks in date palm orchards. Comput. Electron. Agric. 2016, 124, 107–120. [CrossRef]
dc.relation37. Shaw, J.A. Radiometry and the Friis transmission equation. Am. J. Phys. 2012, 81, 33–37. [CrossRef]
dc.relation38. Friis, H. A Note on a Simple Transmission Formula. Proc. IRE 1946, 34, 254–256. [CrossRef]
dc.relation39. Biggelaar, A.J.V.D.; Geluk, S.J.; Jamroz, B.F.; Williams, D.F.; Smolders, A.B.; Johannsen, U.; Bronckers, L.A. Accurate Gain Measurement Technique for Limited Antenna Separations. IEEE Trans. Antennas Propag. 2021, 69, 6772–6782. [CrossRef]
dc.relation40. Kim, Y.; Boo, S.; Kim, G.; Kim, N.; Lee, B. Wireless Power Transfer Efficiency Formula Applicable in Near and Far Fields. J. Electromagn. Eng. Sci. 2019, 19, 239–244. [CrossRef]
dc.relation41. Bensky, A. (Ed.) Chapter 2—Radio Propagation, Short-range Wireless Communication, 3rd ed.; Newnes: Oxford, UK, 2019; pp. 11–41. ISBN 9780128154052. [CrossRef]
dc.relation42. Bowrothu, R.; Yoon, Y.K. Low Loss Cu/Co Metaconductor Based Array Antenna in KaBand for 5G Applications. In Proceedings of the 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, Toronto, ON, Canada, 5–10 July 2020; pp. 35–36. [CrossRef]
dc.relation43. Mahesh, G.; Balachander, D.; Rao, T.R. RF Propagation Measurements in Agricultural Fields for Wireless Sensor Communications. In Proceedings of the IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT), Nagercoil, India, 20–21 March 2013; pp. 808–812. [CrossRef]
dc.relation44. Rao, T.R.; Balachander, D.; Tiwari, N. UHF short-range pathloss measurements in forest & plantation environments for wireless sensor networks. In Proceedings of the IEEE International Conference on Communication Systems (ICCS), Singapore, 21–23 November 2012; pp. 194–198. [CrossRef]
dc.relation45. Frenzel, L. Millimeter Waves Will Expand the Wireless Future (2013). Electronic Design (6). Available online: https://www.electronicdesign.com/home/whitepaper/21802894/millimeter-waves-will-expand-the-wireless-future-pdf-download (accessed on 30 October 2022).
dc.relation46. Agrawal, S.K.; Garg, P. Calculation of channel capacity and rician factor in the presence of vegetation in higher altitude platforms communication systems. In Proceedings of the 15th International Conference on Advanced Computing and Communications (ADCOM), Guwahati, India, 18–21 December 2007; pp. 243–248.
dc.relation47. Sabri, N.; Mohammed, S.S.; Fouad, S.; Syed, A.A.; Al-Dhief, F.T.; Raheemah, A. Investigation of Empirical Wave Propagation Models in Precision Agriculture. MATEC Web Conf. 2018, 150, 06020. [CrossRef]
dc.relation48. Lytaev, M.S.; Vladyko, A.G. Split-step Padé Approximations of the Helmholtz Equation for Radio Coverage Prediction over Irregular Terrain. In Proceedings of the 2018 Advances in Wireless and Optical Communications (RTUWO), Riga, Latvia, 15–16 November 2018; pp. 179–184.
dc.relation49. Liu, H.-X.; Yan, H.-L.; Jia, N.; Tang, S.; Cong, D.; Yang, B.; Li, Z.; Zhang, Y.; Esling, C.; Zhao, X.; et al. Machine-learning-assisted discovery of empirical rule for inherent brittleness of full Heusler alloys. J. Mater. Sci. Technol. 2022, 131, 1–13. [CrossRef]
dc.relation50. Figueroa, J.D.O.; Reeves, J.S.; McPherron, S.P.; Tennie, C. A proof of concept for machine learning-based virtual knapping using neural networks. Sci. Rep. 2021, 11, 19966. [CrossRef]
dc.relation51. Wang, F.; Zhang, Z.; Wang, G.; Wang, Z.; Li, M.; Liang, W.; Gao, J.; Wang, W.; Chen, D.; Feng, Y.; et al. Machine learning and theoretical analysis release the non-linear relationship among ozone, secondary organic aerosol and volatile organic compounds. J. Environ. Sci. 2022, 114, 75–84. [CrossRef]
dc.relation52. Matsuo, Y.; LeCun, Y.; Sahani, M.; Precup, D.; Silver, D.; Sugiyama, M.; Uchibe, E.; Morimoto, J. Deep learning, reinforcement learning, and world models. Neural Netw. 2022, 152, 267–275. [CrossRef]
dc.relation53. Sharma, S.; Mandal, P.K. A Comprehensive Report on Machine Learning-based Early Detection of Alzheimer’s Disease using Multi-modal Neuroimaging Data. ACM Comput. Surv. 2023, 55, 3492865. [CrossRef]
dc.relation54. Srinivasan, P.; Jagatheeswari, P. Machine Learning Controller for DFIG Based Wind Conversion System. Intell. Autom. Soft Comput. 2023, 35, 381–397. [CrossRef]
dc.relation55. Agliari, E.; Alemanno, F.; Barra, A.; De Marzo, G. The emergence of a concept in shallow neural networks. Neural Netw. 2022, 148, 232–253. [CrossRef] [PubMed]
dc.relation56. Mishra, C.; Gupta, D.L. Deep Machine Learning and Neural Networks: An Overview. IAES Int. J. Artif. Intell. (IJ-AI) 2017, 6, 66–73. [CrossRef]
dc.relation57. Muthukumaran, S.; Geetha, P.; Ramaraj, E. Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model. Intell. Autom. Soft Comput. 2023, 35, 215–230. [CrossRef]
dc.relation58. Li, X.; He, J.; Huang, Y.; Li, J.; Liu, X.; Dai, J. Predicting the factors influencing construction enterprises’ adoption of green development behaviors using artificial neural network. Humanit. Soc. Sci. Commun. 2022, 9, 1–12. [CrossRef]
dc.relation59. Mrabti, N.N.; Mrabti, H.N.; Khalil, Z.; Bouyahya, A.; Mohammed, E.-R.; Dguigui, K.; Doudach, L.; Zengin, G.; Elhallaoui, M. Molecular Docking and QSAR Studies for Modeling the Inhibitory Activity of Pyrazole-benzimidazolone Hybrids as Novel Inhibitors of Human 4-hydroxyphenylpyruvate dioxygenase Against Type I Tyrosinemia Disease. Biointerface Res. Appl. Chem. 2022, 13, 38. [CrossRef]
dc.relation60. Dai, D. An Introduction of CNN: Models and Training on Neural Network Models. In Proceedings of the 2021 International Conference on Big Data, Artificial Intelligence and Risk Management, Shanghai, China, 5–7 November 2021; pp. 135–138. [CrossRef]
dc.relation61. Wickramanayake, S.; Hsu, W.; Lee, M.L. Comprehensible Convolutional Neural Networks via Guided Concept Learning. In Proceedings of the International Joint Conference on Neural Networks, Shenzhen, China, 18–22 July 2021. [CrossRef]
dc.relation62. Vaz, J.M.; Balaji, S. Convolutional neural networks (CNNs): Concepts and applications in pharmacogenomics. Mol. Divers. 2021, 25, 1569–1584. [CrossRef]
dc.relation63. Sergeev, D. Classification of human actions using task fMRI images (2019) CEUR Workshop Proceedings; CEUR Workshop Proceedings: Tenerife, Spain, 2019; Volume 2523, pp. 395–403.
dc.relation64. Berhich, A.; Belouadha, F.-Z.; Kabbaj, M.I. A location-dependent earthquake prediction using recurrent neural network algorithms. Soil Dyn. Earthq. Eng. 2022, 161, 107389. [CrossRef]
dc.relation65. Escamilla-Rivera, C.; Carvajal, M.; Zamora, C.; Hendry, M. Neural networks and standard cosmography with newly calibrated high redshift GRB observations. J. Cosmol. Astropart. Phys. 2022, 2022, 52. [CrossRef]
dc.relation66. Farris, A.B.; Vizcarra, J.; Amgad, M.; Cooper, L.A.D.; Gutman, D.; Hogan, J. Artificial intelligence and algorithmic computational pathology: An introduction with renal allograft examples. Histopathology 2021, 78, 791–804. [CrossRef]
dc.relation67. Kavlakoglu, E. AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference? IBM Blog. 2020. Available online: https://www.ibm.com/cloud/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks (accessed on 28 July 2022).
dc.relation68. Bengio, Y.; Lecun, Y.; Hinton, G. Deep learning for AI. Commun. ACM 2021, 64, 58–65. [CrossRef]
dc.relation69. Franchini, G.; Ruggiero, V.; Porta, F.; Zanni, L. Neural architecture search via standard machine learning methodologies. Math. Eng. 2023, 5, 1–21. [CrossRef]
dc.relation70. LeCun, Y. 1.1 Deep Learning Hardware: Past. Present, and Future Digest of Technical Papers. In Proceedings of the IEEE International Solid-State Circuits Conference, San Francisco, CA, USA, 17–21 February 2019; pp. 12–19. [CrossRef]
dc.relation71. Ranzato, M.; Hinton, G.; LeCun, Y. Guest Editorial: Deep Learning. Int. J. Comput. Vis. 2015, 113, 1–2. [CrossRef]
dc.relation72. Ito, S.; Hayashi, T. Radio Propagation Estimation in a Long-Range Environment using a Deep Neural Network. In Proceedings of the 15th European Conference on Antennas and Propagation, Düsseldorf, Germany, 22–26 March 2021. [CrossRef]
dc.relation73. Moraitis, N.; Tsipi, L.; Vouyioukas, D.; Gkioni, A.; Louvros, S. On the Assessment of Ensemble Models for Propagation Loss Forecasts in Rural Environments. IEEE Wirel. Commun. Lett. 2022, 11, 1097–1101. [CrossRef]
dc.relation74. Bogdándy, B.; Tóth, Z. Inversion of artificial neural networks for WiFi RSSI propagation modeling. CEUR Workshop Proc. 2021, 2874, 67–76.
dc.relation75. Seretis, A.; Zhang, X.; Zeng, K.; Sarris, C.D. Artificial neural network models for radiowave propagation in tunnels. IET Microw. Antennas Propag. 2020, 14, 1198–1208. [CrossRef]
dc.relation76. Castillo-Díaz, F.J.; Marín-Guirao, J.I.; Belmonte-Ureña, L.J.; Tello-Marquina, J.C. Effect of Repeated Plant Debris Reutilization as Organic Amendment on Greenhouse Soil Fertility. Int. J. Environ. Res. Public Health 2021, 18, 11544. [CrossRef] [PubMed]
dc.relation77. Caparrós-Martínez, J.L.; Rueda-Lópe, N.; Milán-García, J.; Valenciano, J.D.P. Public policies for sustainability and water security: The case of Almeria (Spain). Glob. Ecol. Conserv. 2020, 23, e01037. [CrossRef]
dc.relation78. Massa, D.; Magán, J.J.; Montesano, F.F.; Tzortzakis, N. Minimizing water and nutrient losses from soilless cropping in southern Europe. Agric. Water Manag. 2020, 241, 106395. [CrossRef]
dc.relation79. Pinna-Hernández, M.; Fernández, F.; Segura, J.; López, J. Solar Drying of Greenhouse Crop Residues for Energy Valorization: Modeling and Determination of Optimal Conditions. Agronomy 2020, 10, 2001. [CrossRef]
dc.relation80. Camacho-Arévalo, R.; García-Delgado, C.; Mayans, B.; Antón-Herrero, R.; Cuevas, J.; Segura, M.; Eymar, E. Sulfonamides in Tomato from Commercial Greenhouses Irrigated with Reclaimed Wastewater: Uptake, Translocation and Food Safety. Agronomy 2021, 11, 1016. [CrossRef]
dc.relation81. Martin-Gorriz, B.; Maestre-Valero, J.; Gallego-Elvira, B.; Marín-Membrive, P.; Terrero, P.; Martínez-Alvarez, V. Recycling drainage effluents using reverse osmosis powered by photovoltaic solar energy in hydroponic tomato production: Environmental footprint analysis. J. Environ. Manag. 2021, 297, 113326. [CrossRef] [PubMed]
dc.relation82. Aznar-Sánchez, J.A.; Velasco-Muñoz, J.F.; García-Arca, D.; López-Felices, B. Identification of Opportunities for Applying the Circular Economy to Intensive Agriculture in Almería (South-East Spain). Agronomy 2020, 10, 1499. [CrossRef]
dc.relation83. Duque-Acevedo, M.; Belmonte-Ureña, L.J.; Toresano-Sánchez, F.; Camacho-Ferre, F. Biodegradable Raffia as a Sustainable and Cost-Effective Alternative to Improve the Management of Agricultural Waste Biomass. Agronomy 2020, 10, 1261. [CrossRef]
dc.relation84. Manríquez-Altamirano, A.; Sierra-Pérez, J.; Muñoz, P.; Gabarrell, X. Analysis of urban agriculture solid waste in the frame of circular economy: Case study of tomato crop in integrated rooftop greenhouse. Sci. Total Environ. 2020, 734, 139375. [CrossRef]
dc.relation85. Téllez, M.; Cabello, T.; Gámez, M.; Burguillo, F.; Rodríguez, E. Comparative study of two predatory mites Amblyseius swirskii Athias-Henriot and Transeius montdorensis (Schicha) by predator-prey models for improving biological control of greenhouse cucumber. Ecol. Model. 2020, 431, 109197. [CrossRef]
dc.relation86. Honoré, M.N.; Belmonte-Ureña, L.J.; Navarro-Velasco, A.; Camacho-Ferre, F. Profit Analysis of Papaya Crops under Greenhouses as an Alternative to Traditional Intensive Horticulture in Southeast Spain. Int. J. Environ. Res. Public Health 2019, 16, 2908. [CrossRef]
dc.relation87. Martínez-Valderrama, J.; Guirado, E.; Maestre, F.T. Discarded food and resource depletion. Nat. Food 2020, 1, 660–662. [CrossRef]
dc.relation88. Cama-Pinto, D.; Holgado-Terriza, J.A.; Damas-Hermoso, M.; Gómez-Mula, F.; Cama-Pinto, A. Radio Wave Attenuation Measurement System Based on RSSI for Precision Agriculture: Application to Tomato Greenhouses. Inventions 2021, 6, 66. [CrossRef]
dc.relation89. ¸Sen, Z.; ¸Si¸sman, E.; Kızılöz, B. A new innovative method for model efficiency performance. Water Supply 2022, 22, 589–601. [CrossRef]
dc.relation90. Lyu, Z.; Yu, Y.; Samali, B.; Rashidi, M.; Mohammadi, M.; Nguyen, T.N.; Nguyen, A. Back-Propagation Neural Network Optimized by K-Fold Cross-Validation for Prediction of Torsional Strength of Reinforced Concrete Beam. Materials 2022, 15, 1477. [CrossRef]
dc.relation91. Song, X.; Li, K.; Dai, K.; Wang, X.; Du, H.; Zhao, H. A random-forest-assisted artificial-neural-network method for analysis of steel using laser-induced breakdown spectroscopy. Optik 2022, 249, 168214. [CrossRef]
dc.relation92. Short, S.M.; Cogdill, R.P.; Anderson, C.A. Determination of figures of merit for near-infrared and raman spectrometry by net analyte signal analysis for a 4-component solid dosage system. AAPS PharmSciTech 2007, 8, 109–119. [CrossRef]
dc.relation93. Peng, Y.; Knadel, M.; Gislum, R.; Schelde, K.; Thomsen, A.; Greve, M.H. Quantification of SOC and Clay Content Using Visible Near-Infrared Reflectance–Mid-Infrared Reflectance Spectroscopy with Jack-Knifing Partial Least Squares Regression. Soil Sci. 2014, 179, 325–332. [CrossRef]
dc.relation94. Yoon, S.; Choi, J.; Moon, S.-J.; Choi, J. Determination and Quantification of Heavy Metals in Sediments through Laser-Induced Breakdown Spectroscopy and Partial Least Squares Regression. Appl. Sci. 2021, 11, 7154. [CrossRef]
dc.relation95. Yap, X.Y.; Chia, K.S.; Tee, K.S. A Portable Gas Pressure Control and Data Acquisition System using Regression Models. Int. J. Electr. Eng. Inform. 2021, 13, 242–251. [CrossRef]
dc.relation96. Obisesan, K.A.; Neri, S.; Bugnicourt, E.; Campos, I.; Rodriguez-Turienzo, L. Determination and Quantification of the Distribution of CN-NL Nanoparticles Encapsulating Glycyrrhetic Acid on Novel Textile Surfaces with Hyperspectral Imaging. J. Funct. Biomater. 2020, 11, 32. [CrossRef]
dc.relation97. Chang, W.; Ji, X.; Wang, L.; Liu, H.; Zhang, Y.; Chen, B.; Zhou, S. A Machine-Learning Method of Predicting Vital Capacity Plateau Value for Ventilatory Pump Failure Based on Data Mining. Healthcare 2021, 9, 1306. [CrossRef] [PubMed]
dc.relation98. Faroughi, S.A.; Roriz, A.I.; Fernandes, C. A Meta-Model to Predict the Drag Coefficient of a Particle Translating in Viscoelastic Fluids: A Machine Learning Approach. Polymers 2022, 14, 430. [CrossRef] [PubMed]
dc.relation99. Aziz, A.; Driouich, A.; Bellil, A.; Ben Ali, M.; Mabtouti, S.E.; Felaous, K.; Achab, M.; El Bouari, A. Optimization of new eco-material synthesis obtained by phosphoric acid attack of natural Moroccan pozzolan using Box-Behnken Design. Ceram. Int. 2021, 47, 33028–33038. [CrossRef]
dc.relation100. Cesari, M.; Stefani, A.; Mitterling, T.; Frauscher, B.; Schönwald, S.V.; Högl, B. Sleep modelled as a continuous and dynamic process predicts healthy ageing better than traditional sleep scoring. Sleep Med. 2021, 77, 136–146. [CrossRef]
dc.relation101. Qadir, Z.; Khan, S.I.; Khalaji, E.; Munawar, H.S.; Al-Turjman, F.; Mahmud, M.P.; Kouzani, A.Z.; Le, K. Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids. Energy Rep. 2021, 7, 8465–8475. [CrossRef]
dc.relation102. Sallehhudin, W.; Diab, A. Using Machine Learning to Predict the Fuel Peak Cladding Temperature for a Large Break Loss of Coolant Accident. Front. Energy Res. 2021, 9, 755638. [CrossRef]
dc.relation103. Ahmadi, K.; Alavi, S.J.; Kouchaksaraei, M.T.; Aertsen, W. Non-linear height-diameter models for oriental beech (Fagus ori-entalis Lipsky) in the Hyrcanian forests, Iran. Biotechnol. Agron. Soc. Environ. 2013, 17, 431–440.
dc.relation104. Oussama, C.; Abdellah, E.A.; Youssef, E.O.; Mohammed, B.; Abdelkrim, O. In silico Prediction of Novel SARS-CoV 3CL pro Inhibitors: A Combination of 3D-QSAR, Molecular Docking, ADMET Prediction, and Molecular Dynamics Simulation. Biointerface Res. Appl. Chem. 2022, 12, 5100–5115. [CrossRef]
dc.relation105. Gramatica, P. On the Development and Validation of QSAR Models. Methods Mol. Biol. 2013, 930, 499–526. [CrossRef] [PubMed]
dc.relation106. Gohain, P.B.; Jansson, M. Scale-Invariant and consistent Bayesian information criterion for order selection in linear regression models. Signal Process. 2022, 196, 108499. [CrossRef]
dc.relation107. Banteng, L.; Yang, H.; Chen, Q.; Wang, Z. Research on the subtractive clustering algorithm for mobile ad hoc network based on the Akaike information criterion. Int. J. Distrib. Sens. Netw. 2019, 15, 1550147719877612. [CrossRef]
dc.relation108. Banks, H.T.; Joyner, M.L. Adaption of Akaike information criterion under least squares frameworks for comparison of stochastic models. Q. Appl. Math. 2019, 77, 831–859. [CrossRef]
dc.relation109. Tozzi, R.; Masci, F.; Pezzopane, M. A stress test to evaluate the usefulness of Akaike information criterion in short-term earthquake prediction. Sci. Rep. 2020, 10, 21153. [CrossRef]
dc.relation110. Benedet, A.L.; Brum, W.S.; Hansson, O.; Karikari, T.K.; Zimmer, E.R.; Zetterberg, H.; Blennow, K.; Ashton, N.J. Alzheimer’s Disease Neuroimaging Initiative the accuracy and robustness of plasma biomarker models for amyloid PET positivity. Alzheimer’s Res. Ther. 2022, 14, 26. [CrossRef]
dc.relation111. Bhattacharya, S.; Biswas, A.; Nandi, S.; Patra, S.K. Exhaustive model selection in b→s`` decays: Pitting cross-validation against the Akaike information criterion. Phys. Rev. D 2020, 101, 055025. [CrossRef]
dc.relation112. Pham, H. A New Criterion for Model Selection. Mathematics 2019, 7, 1215. [CrossRef]
dc.relation113. Tran, D.A.; Tsujimura, M.; Ha, N.T.; Nguyen, V.T.; van Binh, D.; Dang, T.D.; Doan, Q.-V.; Bui, D.T.; Ngoc, T.A.; Phu, L.V.; et al. Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam. Ecol. Indic. 2021, 127, 107790. [CrossRef]
dc.relation114. Saez, L.M.A.; Campos, F.M.A.; Callejón-Ferre, J.; Medina, M.D.S.; Fernández, I. Use of multivariate NMR analysis in the content prediction of hemicellulose, cellulose and lignin in greenhouse crop residues. Phytochemistry 2019, 158, 110–119. [CrossRef]
dc.relation115. Ezuma, M.; Anjinappa, C.K.; Funderburk, M.; Guvenc, I. Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies. IEEE Trans. Aerosp. Electron. Syst. 2022, 58, 27–46. [CrossRef]
dc.relation116. Ikbal, N.A.M.; Halim, S.A.; Ali, N. Estimating Weibull Parameters Using Maximum Likelihood Estimation and Ordinary Least Squares: Simulation Study and Application on Meteorological Data. Math. Stat. 2022, 10, 269–292. [CrossRef]
dc.relation117. Xu, Q.; Li, R.; Liu, Y.; Luo, C.; Xu, A.; Xue, F.; Xu, Q.; Li, X. Forecasting the Incidence of Mumps in Zibo City Based on a SARIMA Model. Int. J. Environ. Res. Public Health 2017, 14, 925. [CrossRef]
dc.relation118. Tikhonov, A.N. On the solution of ill-posed problems and the method of regularization. In Doklady Akademii Nauk; Russian Academy of Sciences: Moscow, Russia, 1963; pp. 501–504. [CrossRef]
dc.relation119. Calvetti, D.; Morigi, S.; Reichel, L.; Sgallari, F. Tikhonov regularization and the L-curve for large discrete ill-posed problems. J. Comput. Appl. Math. 2000, 123, 423–446. [CrossRef]
dc.relation120. Rasmussen, C.E.; Williams, C.K.I. Gaussian Processes for Machine Learning; the MIT Press: Cambridge, MA, USA, 2006; ISBN 026218253X.
dc.relation121. Wolfe, P. Convergence Conditions for Ascent Methods. SIAM Rev. 1969, 11, 226–235. [CrossRef]
dc.relation122. Wolfe, P. Convergence Conditions for Ascent Methods. II: Some Corrections. SIAM Rev. 1971, 13, 185–188. [CrossRef]
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dc.rightsAtribución 4.0 Internacional (CC BY 4.0)
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dc.sourcehttps://www.mdpi.com/2073-4395/13/1/244
dc.subjectDeep learning
dc.subjectNeural network
dc.subjectPrecision agriculture
dc.subjectPropagation model
dc.subjectWireless sensor networks
dc.titleA deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses
dc.typeArtículo de revista
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