Monitoramento de plantas em casas de vegetação para assimilação de dados
Resumo
Mais dados têm sido gerados em aplicações agrícolas por novas fontes e com grande potencial de uso. Obtidos no campo ou em fazendas verticais, eles podem ser usados, por exemplo, em gêmeos digitais, que visam conectar observações a um modelo do sistema. Essa conexão pode ocorrer por assimilação de dados e em ambientes protegidos, em que as plantas podem ser monitoradas mais intensamente, mais dados estariam disponíveis. Neste trabalho, realizamos assimilação em um modelo de tomateiro usando dados coletados por câmeras e por células de carga, observando que essas fontes fornecem boas estimativas de biomassa da parte aérea e que a técnica melhora as estimativas obtidas pelo modelo Tomgro Reduzido sem calibração.
Palavras-chave:
Filtro Kalman, Modelos de Crescimento Vegetal, Cultivo Protegido
Referências
Bojacá, C. R., Gil, R. and Cooman, A. (mar 2009). Use of geostatistical and crop growth modelling to assess the variability of greenhouse tomato yield caused by spatial temperature variations. Computers and Electronics in Agriculture, v. 65, n. 2, p. 219–227. DOI: https://doi.org/10.1016/j.compag.2008.10.001
Chen, W.-T., Yeh, Y.-H. F., Liu, T.-Y. and Lin, T.-T. (31 mar 2016). An Automated and Continuous Plant Weight Measurement System for Plant Factory. Frontiers in Plant Science, v. 7, n. MAR2016, p. 392. DOI: https://doi.org/10.3389/fpls.2016.00392
Chen, Y., Zhang, Z. and Tao, F. (1 nov 2018). Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data. European Journal of Agronomy, v. 101, p. 163–173. DOI: https://doi.org/10.1016/j.eja.2018.09.006
De Graaf, R., De Gelder, A. and Blok, C. (2004). Advanced weighing equipment for water, crop growth and climate control management. Acta Horticulturae, v. 664, p. 163–167. DOI: https://doi.org/10.17660/ActaHortic.2004.664.17
De Koning, A. N. M. and Bakker, J. C. (mar 1992). In situ plant weight measurement of tomato with an electronic force gauge. Acta Horticulturae, n. 304, p. 183–186. DOI: https://doi.org/10.17660/ActaHortic.1992.304.20
Dorigo, W. A., Zurita-Milla, R., De Wit, A. J. W., et al. (may 2007). A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, v. 9, n. 2, p. 165–193. DOI: https://doi.org/10.1016/j.jag.2006.05.003
Gong, L., Yu, M., Jiang, S., Cutsuridis, V. and Pearson, S. (1 jul 2021). Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN. Sensors, v. 21, n. 13, p. 4537. DOI: https://doi.org/10.3390/s21134537
Helmer, T., Ehret, D. L. and Bittman, S. (2005). CropAssist, an automated system for direct measurement of greenhouse tomato growth and water use. Computers and Electronics in Agriculture, v. 48, n. 3, p. 198–215. DOI: https://doi.org/10.1016/j.compag.2005.04.005
Hemming, S., De Zwart, F. De, Elings, A., Petropoulou, A. and Righini, I. (11 nov 2020). Cherry Tomato Production in Intelligent Greenhouses—Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality. Sensors, v. 20, n. 22, p. 6430. DOI: https://doi.org/10.3390/s20226430
Hemming, S., De Zwart, F., Elings, A., Righini, I. and Petropoulou, A. (16 apr 2019). Remote Control of Greenhouse Vegetable Production with Artificial Intelligence Greenhouse Climate, Irrigation, and Crop Production. Sensors, v. 19, n. 8, p. 1807. DOI: https://doi.org/10.3390/s19081807
Hu, S., Shi, L., Huang, K., et al. (15 feb 2019). Improvement of sugarcane crop simulation by SWAP-WOFOST model via data assimilation. Field Crops Research, v. 232, p. 49–61. DOI: https://doi.org/10.1016/j.fcr.2018.12.009
Huang, J., Gómez-Dans, J. L., Huang, H., et al. (15 oct 2019). Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology, v. 276–277, p. 107609. DOI: https://doi.org/10.1016/j.agrformet.2019.06.008
Huang, J., Sedano, F., Huang, Y., et al. (2016). Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agricultural and Forest Meteorology, v. 216, p. 188–202. DOI: https://doi.org/10.1016/j.agrformet.2015.10.013
Jin, X., Kumar, L., Li, Z., et al. (jan 2018). A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, v. 92, n. November 2017, p. 141–152. DOI: https://doi.org/10.1016/j.eja.2017.11.002
Jones, J. W., Kenig, A. and Vallejos, C. E. (1999). Reduced state-variable tomato growth model. Transactions of the ASAE, v. 42, n. 1, p. 255–265. DOI: https://doi.org/10.13031/2013.13203
Keating, B. A. and Thorburn, P. J. (22 oct 2018). Modelling crops and cropping systems—Evolving purpose, practice and prospects. European Journal of Agronomy, v. 100, n. April, p. 163–176. DOI: https://doi.org/10.1016/j.eja.2018.04.007
Lee, J. W. and Son, J. E. (18 oct 2019). Nondestructive and Continuous Fresh Weight Measurements of Bell Peppers Grown in Soilless Culture Systems. Agronomy, v. 9, n. 10, p. 652. DOI: https://doi.org/10.3390/agronomy9100652
Linker, R. and Ioslovich, I. (2017). Assimilation of canopy cover and biomass measurements in the crop model AquaCrop. Biosystems Engineering, v. 162, p. 57–66. DOI: https://doi.org/10.1016/j.biosystemseng.2017.08.003
Lu, Y., Chibarabada, T. P., Ziliani, M. G., et al. (2021). Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model. Agricultural Water Management, v. 252, n. April, p. 106884. DOI: https://doi.org/10.1016/j.agwat.2021.106884
Marcelis, L. F. M., Van den Boogaard, R. and Meinen, E. (2000). Control of Crop Growth and Nutrient Supply by the Combined Use of Crop Models and Plant Sensors. IFAC Proceedings Volumes, v. 33, n. 19, p. 161–166. DOI: https://doi.org/10.1016/S1474-6670(17)40906-2
Pellenq, J. and Boulet, G. (may 2004). A methodology to test the pertinence of remotesensing data assimilation into vegetation models for water and energy exchange at the land surface. Agronomie, v. 24, n. 4, p. 197–204. DOI: https://doi.org/10.1051/agro:2004017
Ruíz-García, A., López-Cruz, I. L., Ramírez-Arias, A. and Rico-Garcia, E. (may 2014). Modeling uncertainty of greenhouse crop lettuce growth model using Kalman Filtering. Acta Horticulturae, v. 1037, n. 1037, p. 361–368. DOI: https://doi.org/10.17660/ActaHortic.2014.1037.44
Torres-Monsivais, J. C., López-Cruz, I. L., Ruíz-García, A., Ramírez-Arias, J. A. and Peña-Moreno, R. D. (2017). Data assimilation to improve states estimation of a dynamic greenhouse tomatoes crop growth model. Acta Horticulturae, n. 1170, p. 433–440. DOI: https://doi.org/10.17660/actahortic.2017.1170.53
Vazifedoust, M., Van Dam, J. C., Bastiaanssen, W. G. M. and Feddes, R. A. (2009). Assimilation of satellite data into agrohydrological models to improve crop yield forecasts. International Journal of Remote Sensing, v. 30, n. 10, p. 2523–2545. DOI: https://doi.org/10.1080/01431160802552769
Verdouw, C., Tekinerdogan, B., Beulens, A. and Wolfert, S. (1 apr 2021). Digital twins in smart farming. Agricultural Systems, v. 189, n. January, p. 103046. DOI: https://doi.org/10.1016/j.agsy.2020.103046
Yu, D., Zha, Y., Shi, L., et al. (2020). Improvement of sugarcane yield estimation by assimilating UAV-derived plant height observations. European Journal of Agronomy, v. 121, n. February, p. 126159. DOI: https://doi.org/10.1016/j.eja.2020.126159
Zhao, Y., Chen, S. and Shen, S. (dec 2013). Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation. Ecological Modelling, v. 270, p. 30–42. DOI: https://doi.org/10.1016/j.ecolmodel.2013.08.016Get
Chen, W.-T., Yeh, Y.-H. F., Liu, T.-Y. and Lin, T.-T. (31 mar 2016). An Automated and Continuous Plant Weight Measurement System for Plant Factory. Frontiers in Plant Science, v. 7, n. MAR2016, p. 392. DOI: https://doi.org/10.3389/fpls.2016.00392
Chen, Y., Zhang, Z. and Tao, F. (1 nov 2018). Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data. European Journal of Agronomy, v. 101, p. 163–173. DOI: https://doi.org/10.1016/j.eja.2018.09.006
De Graaf, R., De Gelder, A. and Blok, C. (2004). Advanced weighing equipment for water, crop growth and climate control management. Acta Horticulturae, v. 664, p. 163–167. DOI: https://doi.org/10.17660/ActaHortic.2004.664.17
De Koning, A. N. M. and Bakker, J. C. (mar 1992). In situ plant weight measurement of tomato with an electronic force gauge. Acta Horticulturae, n. 304, p. 183–186. DOI: https://doi.org/10.17660/ActaHortic.1992.304.20
Dorigo, W. A., Zurita-Milla, R., De Wit, A. J. W., et al. (may 2007). A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, v. 9, n. 2, p. 165–193. DOI: https://doi.org/10.1016/j.jag.2006.05.003
Gong, L., Yu, M., Jiang, S., Cutsuridis, V. and Pearson, S. (1 jul 2021). Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN. Sensors, v. 21, n. 13, p. 4537. DOI: https://doi.org/10.3390/s21134537
Helmer, T., Ehret, D. L. and Bittman, S. (2005). CropAssist, an automated system for direct measurement of greenhouse tomato growth and water use. Computers and Electronics in Agriculture, v. 48, n. 3, p. 198–215. DOI: https://doi.org/10.1016/j.compag.2005.04.005
Hemming, S., De Zwart, F. De, Elings, A., Petropoulou, A. and Righini, I. (11 nov 2020). Cherry Tomato Production in Intelligent Greenhouses—Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality. Sensors, v. 20, n. 22, p. 6430. DOI: https://doi.org/10.3390/s20226430
Hemming, S., De Zwart, F., Elings, A., Righini, I. and Petropoulou, A. (16 apr 2019). Remote Control of Greenhouse Vegetable Production with Artificial Intelligence Greenhouse Climate, Irrigation, and Crop Production. Sensors, v. 19, n. 8, p. 1807. DOI: https://doi.org/10.3390/s19081807
Hu, S., Shi, L., Huang, K., et al. (15 feb 2019). Improvement of sugarcane crop simulation by SWAP-WOFOST model via data assimilation. Field Crops Research, v. 232, p. 49–61. DOI: https://doi.org/10.1016/j.fcr.2018.12.009
Huang, J., Gómez-Dans, J. L., Huang, H., et al. (15 oct 2019). Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology, v. 276–277, p. 107609. DOI: https://doi.org/10.1016/j.agrformet.2019.06.008
Huang, J., Sedano, F., Huang, Y., et al. (2016). Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agricultural and Forest Meteorology, v. 216, p. 188–202. DOI: https://doi.org/10.1016/j.agrformet.2015.10.013
Jin, X., Kumar, L., Li, Z., et al. (jan 2018). A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, v. 92, n. November 2017, p. 141–152. DOI: https://doi.org/10.1016/j.eja.2017.11.002
Jones, J. W., Kenig, A. and Vallejos, C. E. (1999). Reduced state-variable tomato growth model. Transactions of the ASAE, v. 42, n. 1, p. 255–265. DOI: https://doi.org/10.13031/2013.13203
Keating, B. A. and Thorburn, P. J. (22 oct 2018). Modelling crops and cropping systems—Evolving purpose, practice and prospects. European Journal of Agronomy, v. 100, n. April, p. 163–176. DOI: https://doi.org/10.1016/j.eja.2018.04.007
Lee, J. W. and Son, J. E. (18 oct 2019). Nondestructive and Continuous Fresh Weight Measurements of Bell Peppers Grown in Soilless Culture Systems. Agronomy, v. 9, n. 10, p. 652. DOI: https://doi.org/10.3390/agronomy9100652
Linker, R. and Ioslovich, I. (2017). Assimilation of canopy cover and biomass measurements in the crop model AquaCrop. Biosystems Engineering, v. 162, p. 57–66. DOI: https://doi.org/10.1016/j.biosystemseng.2017.08.003
Lu, Y., Chibarabada, T. P., Ziliani, M. G., et al. (2021). Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model. Agricultural Water Management, v. 252, n. April, p. 106884. DOI: https://doi.org/10.1016/j.agwat.2021.106884
Marcelis, L. F. M., Van den Boogaard, R. and Meinen, E. (2000). Control of Crop Growth and Nutrient Supply by the Combined Use of Crop Models and Plant Sensors. IFAC Proceedings Volumes, v. 33, n. 19, p. 161–166. DOI: https://doi.org/10.1016/S1474-6670(17)40906-2
Pellenq, J. and Boulet, G. (may 2004). A methodology to test the pertinence of remotesensing data assimilation into vegetation models for water and energy exchange at the land surface. Agronomie, v. 24, n. 4, p. 197–204. DOI: https://doi.org/10.1051/agro:2004017
Ruíz-García, A., López-Cruz, I. L., Ramírez-Arias, A. and Rico-Garcia, E. (may 2014). Modeling uncertainty of greenhouse crop lettuce growth model using Kalman Filtering. Acta Horticulturae, v. 1037, n. 1037, p. 361–368. DOI: https://doi.org/10.17660/ActaHortic.2014.1037.44
Torres-Monsivais, J. C., López-Cruz, I. L., Ruíz-García, A., Ramírez-Arias, J. A. and Peña-Moreno, R. D. (2017). Data assimilation to improve states estimation of a dynamic greenhouse tomatoes crop growth model. Acta Horticulturae, n. 1170, p. 433–440. DOI: https://doi.org/10.17660/actahortic.2017.1170.53
Vazifedoust, M., Van Dam, J. C., Bastiaanssen, W. G. M. and Feddes, R. A. (2009). Assimilation of satellite data into agrohydrological models to improve crop yield forecasts. International Journal of Remote Sensing, v. 30, n. 10, p. 2523–2545. DOI: https://doi.org/10.1080/01431160802552769
Verdouw, C., Tekinerdogan, B., Beulens, A. and Wolfert, S. (1 apr 2021). Digital twins in smart farming. Agricultural Systems, v. 189, n. January, p. 103046. DOI: https://doi.org/10.1016/j.agsy.2020.103046
Yu, D., Zha, Y., Shi, L., et al. (2020). Improvement of sugarcane yield estimation by assimilating UAV-derived plant height observations. European Journal of Agronomy, v. 121, n. February, p. 126159. DOI: https://doi.org/10.1016/j.eja.2020.126159
Zhao, Y., Chen, S. and Shen, S. (dec 2013). Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation. Ecological Modelling, v. 270, p. 30–42. DOI: https://doi.org/10.1016/j.ecolmodel.2013.08.016Get
Publicado
10/11/2021
Como Citar
OLIVEIRA, Monique Pires Gravina de; RODRIGUES, Luiz Henrique Antunes.
Monitoramento de plantas em casas de vegetação para assimilação de dados. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA (SBIAGRO), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 215-224.
ISSN 2177-9724.
DOI: https://doi.org/10.5753/sbiagro.2021.18393.
