Irrigation in the age of agriculture 4.0: management, monitoring and precision

Alexsandro Oliveira da Silva, Bruna Aires da Silva, Claudinei Fonseca Souza, Benito Moreira de Azevedo, Luís Henrique Bassoi, Denise Vieira Vasconcelos, Guilherme Vieira do Bonfim, Juan Manzano Juarez, Adão Felipe dos Santos, Franciele Morlin Carneiro

Resumo


Technological evolution is essential to make irrigated agriculture more efficient in the use of water. Thus, this review article aims to contextualize irrigation in the age of agriculture 4.0 in order to address how these new technologies are impacting the rational use of water. With regard to the automation of irrigated systems, irrigation efficiency with moisture sensors, applications using smartphone, controllers and fertilizer injectors, as well as how their operation can promote irrigation, was addressed. Regarding irrigation management, the use of remote sensing as an option to determine crop evapotranspiration was contextualized, listing the types of spectral bands and sensors used to collect images (orbital, aerial and terrestrial), in the monitoring of crop water status. The importance of data collection in the delineations of management zones for precision irrigation and what possible advances can still be achieved with regard to obtaining and analyzing data were also discussed. Finally, it is concluded that, despite the high efficiency of automated irrigation systems, information of soil, climate and plant attributes obtained through the range of data provided by sensors will be responsible for mitigating the global impacts caused by irrigated agriculture in the near future, since this information can enhance irrigation, with maximum efficiency, thus reducing water consumption by agriculture.

Palavras-chave


Precision agriculture; Internet of things; Remote sensing; Management zones

Texto completo:

PDF

Referências


ABIOYE, E. A. et al. A review on monitoring and advanced control strategies for precision irrigation. Computers and Electronics in Agriculture, v. 173, p. 1-22, 2020.

ADEYEMI, O. et al. Dynamic neural network modelling of soil moisture content for predictive irrigation scheduling. Sensors, v. 18, n. 10, p. 1-22, 2018.

ALLEN, R. et al. Satellite-based ET estimation in agriculture using SEBAL and METRIC. Hydrological Processes, v. 25, n. 26, p. 4011-4027, 2011.

ALLEN, R. G.; TASUMI, M.; TREZZA, R. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) - Model. Journal of Irrigation and Drainage Engineering, v. 133, n. 4, p. 380-394, 2007.

ANDERSON, M. C. et al. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sensing of Environment, v.122, p. 50-65, 2012.

AQEEL-UR-REHMAN.; SHAIKH, Z. A. Smart agriculture. In: ZUBAIRI, J. A. Application of modern high performance networks. Sharjah: Bentham Science Publishers Ltd., 2009. cap. 6, p. 120-129.

ARSLAN, S., COLVIN, T. S. Grain yield mapping: yield sensing, yield reconstruction, and errors. Precision Agriculture, v. 3, n. 2, 135-154, 2002.

ARVANITIS, K. G.; SYMEONAKI, E. G. Agriculture 4.0: the role of innovative smart technologies towards sustainable farm management. The Open Agriculture Journal, v. 14, n. 1, p. 130-135, 2020.

ASOKARAJA, N. Advances in fertigation for micro irrigation: India. In: GOYAL, M. R. (ed.). Water and fertigation management in microirrigation. Oakville, Canada: Apple Academic Press, 2015. p. 145-174. E-book. Available at: https://doi.org/10.1201/b18800. Accessed on: Oct 19, 2020.

BASTIAANSSEN, W. G. M. et al. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, v. 212-213, p. 198-212, 1998.

BELLO, Z.A.; TFWALA, C. M.; RENSBURG, L. D. V. Evaluation of newly developed capacitance probes for continuous soil water measurement. Geoderma, v. 345, p. 104-113, 2019.

BERÇA, A. S.; MENDONÇA, T. G.; SOUZA, C. F. Influence of organic mulching on drip irrigation management of cabbage cultivation. Revista Ambiente e Água, v. 14, p. 1-11, 2019.

BERNI, J. A. J. et al. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing, v. 47, n. 3, p. 722-738, 2009.

BHATTI, S. et al. Site-specific irrigation management in a sub-humid climate using a spatial evapotranspiration model with satellite and airborne imagery, Agricultural Water Management, v. 230, 2020.

CARRIJO, O. A. et al. Tendências e desafios da fertirrigação no Brasil. In: FOLEGATTI, M. V.; CASARINI, E.; BLANCO, F. F.; BRASIL, R. P. C. do; RESENDE, R. S. (org.). Fertirrigação: citrus, flores e hortaliças. Guaiúba, RS: Agropecuária, 2001. v. 2p. 155-206.

CHAUHDARY, J. N. et al. Impact assessment of precision agriculture and optimization of fertigation for corn growth. Pakistan Journal of Agricultural Sciences, v. 57, n. 4, p. 993-1001, 2020.

CHEN, L. et al. Data-driven calibration of soil moisture sensor considering impacts of temperature: a case study on FDR sensors. Sensors, v. 19 n. 20, 4381, 2019.

CHEN, Y. et al. Detecting irrigation extent, frequency, and timing in a heterogeneous arid agricultural region using MODIS time series, Landsat imagery, and ancillary data. Remote Sensing of Environment, v. 204, p. 197-211, 2018.

COHEN, Y. et al. Crop water status estimation using thermography: Multi-year model development using groundbased thermal images. Precision Agriculture, v. 16, p. 311-329, 2015.

CÓRDOBA, M. et al. Subfield management class delineation using cluster analysis from spatial principal components of soil variables. Computer and Electronic in Agriculture, v. 97, p. 6-14, 2013.

DOMÍNGUEZ-NIÑO, J. M. et al. Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors. Agricultural Water Management, v. 228, p. 1-11, 2020.

ENCUESTA SOBRE SUPERFICIES Y RENDIMIENTOS DE CULTIVOS - ESYRCE. Análisis de los Regadíos Españoles. Madrid: Ministerio de Agricultura, Pesca y Alimentación, 2019. 166p. Available at: https://www.mapa.gob.es/es/estadistica/temas/estadisticas-agrarias/agricultura/esyrce/. Accessed on: Oct 19, 2020.

FOOD AND AGRICULTURE ORGANIZATION – FAO. The state of the world’s land and water resources for food and agriculture (SOLAW) - Managing systems at risk. Rome: Food and Agriculture Organization of the United Nations, Rome and Earthscan, London. 2011. 285p.

FRENCH, A. N.; HUNSAKER, D. J.; THORP, K. R. Remote sensing of evapotranspiration over cotton using the TSEB and METRIC energy balance models. Remote Sensing of Environment, v. 158, p. 281-294, 2015.

FUCHS, M. Infrared measurement of canopy temperature and detection of plant water-stress. Theoretical and Applied Climatology, v. 42, n. 4, p. 253-261, 1990.

GAO, B. NDWI - a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, v. 58, n. 3, p. 257-266, 1996.

GARCIA, A. D. C. Las comunidades de regantes de España y su federación nacional. Ed. Ampliada. Madrid: FENACORE, 2018. 102p.

GAVA, R.; SILVA, E. E.; BAIO, F. H. R. Calibração de sensor eletrônico de umidade em diferentes texturas de solo. Brazilian Journal of Biosystems Engineering, v. 10, n. 2, p. 154-162, 2016.

GENERALITAT VALENCIANA. Guía metodológica para la compatibilización de la fertiirrigación comunitaria con la agricultura ecológica y el policultivo y para su empleo hacia una producción ecológica comunitaria. Valencia: Servicios de regadíos, 2020. 51p. Available at: < http://www.agroambient.gva.es/documents/163214705/171110019/GUIA+METODOLOGICA+FERTIRRIGACION+DEFINITIVA.pdf/64597fcf-8ee4-4973-9d49-75cf79d888ce> Accessed on: Oct 19, 2020.

GOLDEN SOFTWARE. Surfer 12®: powerful contouring, gridding & 3D surface mapping. Colorado, USA: Golden Software Inc., 2017. 124p.

GONZÁLEZ, A. R. T. et al. Development of crop coefficients using remote sensing-based vegetation index and growing degree days. In Proceedings of the ASABE Annual International Meeting, Orlando, Fla, USA, July 2016.

GRECCO, K. L.; BIZARI, D. R.; SOUZA, C. F. Avaliação do modelo Hydrus-2D na distribuição do soluto no gotejamento subsuperficial. Irriga, v.1, p.113-125, 2016.

HABOUDANE, D. et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, v. 81, n. 2-3, p. 416-426, 2002.

HAGHVERDI, A. et al. Perspectives on delineating management zones for variable rate irrigation. Computers and Electronics in Agriculture, v. 117, p. 154-167, 2015.

HOOK, W. R.; LIVINGSTON, N. J. Errors in converting time domain reflectometry measurements of propagation velocity to estimates of soil water content. Soil Science Society of America Journal, v. 60, n. 1, p. 35-41, 1995.

IHUOMA, S. O.; MADRAMOOTOO, C. A. Recent advances in crop water stress detection. Computers and Electronics in Agriculture, v. 141, p. 267-275, 2017. https://doi.org/10.1016/j.compag.2017.07.026

INCROCCI, L.; MASSA, D.; PARDOSSI, A. New trends in the fertigation management of irrigated vegetable crops. Horticulturae, v. 3, n. 2, p. 1-20, 2017.

INTERNATIONAL SOCIETY OF PRECISION AGRICULTURE. Precision Agriculture Definition - Language Modal. Available at . Accessed on: Jul 19, 2020.

JIANG, Q.; FU, Q.; WANG, Z. Delineating site-specific irrigation management zones. Irrigation and Drainage, v. 60, p. 464-472, 2011.

JIMÉNEZ-BELLO, M. A. et al. Analysis, assessment, and improvement of fertilizer distribution in pressure irrigation systems. Irrigation Science, v. 29, p.45-53, 2011.

JONES, H.; SIRAULT, X. Scaling of Thermal Images at Different Spatial Resolution: The Mixed Pixel Problem. Agronomy, v. 4, p. 380–396, 2014.

KAMBLE, B.; KILIC, A.; HUBBARD, K. Estimating crop coefficients using remote sensing-based vegetation index. Remote Sensing, v. 5, n. 4, p.1588-1602, 2013.

KAMIENSKI, C.; VISOLI, M. SWAMP: uma plataforma para irrigação de precisão baseada na internet das coisas. Fonte, v. 15, n. 20, p. 76-84, 2018.

KASSING, R. C.; SCHUTTER, B. D.; ABRAHAM, E. Optimal control for precision irrigation of a large-scale plantation. Water Resources Research, p. 1-28, 2020.

KHANAL, S.; FULTON, J.; SHEARER, S. An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture, v. 139, p. 22-32, 2017.

LEMOS FILHO, L. C. A., BASSOI, L. H., FARIA, M. A. Variabilidade espacial e estabilidade temporal do armazenamento de água em solo arenoso cultivado com videiras irrigadas. Irriga, v. 1, p. 319-340, 2016.

LEMOS FILHO, L. C. A.; BASSOI, L. H.; FARIA, M. A. Modelagem espacial da água em solo arenoso com cultivo irrigado no Semiárido. Water Resources and Irrigation Management, v. 4, p. 15-24, 2015.

LOPES, M.S.; REYNOLDS, M. P. Partitioning of assimilates to deeper roots is associated with cooler canopies and increased yield under drought in wheat. Functional Plant Biology, v. 37, n. 2, p. 147-156, 2010.

MENDONÇA, T. et al. Deficit irrigation of subsurface drip-irrigated grape tomato. Engenharia Agrícola, v. 40, n. 4, p. 453-461, 2020.

MESSINA, G.; MODICA, G. Applications of UAV thermal imagery in precision agriculture: state of art and future research outlook. Remote Sensing, v.12, n.9, 1-26, 2020.

MOKHTARI, A. et al. Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2. ISPRS Journal of Photogrammetry and Remote Sensing, v. 154, p. 231-245, 2019.

NADLER, A. et al. Stress induced water content variations in mango by Time Domain Reflectometry. Soil Science Society of America Journal, v. 70, p. 510-520, 2006.

NARAIN-FORD, D. M. et al. Natural purification through soils: risks and opportunities of sewage effluent reuse in sub-surface irrigation. In: VOOGT, P. de; BERGMAN, H. (ed.). Reviews of environmental contamination and toxicology. Cham: Springer International Publishing, 2020. p. 1-33. E-book. Available at: https://doi.org/10.1007/398_2020_49. Accessed on: Oct 19, 2020.

NASCIMENTO, P. S. et al. Zonas homogêneas de atributos do solo para o manejo de irrigação em pomar de videira. Revista Brasileira de Ciência do Solo, v. 38, p. 1101-1113, 2014.

O’SHAUGHNESSY, S. A. et al. Identifying advantages and disadvantages of variable rate irrigation - an updated review. Applied Engineering in Agriculture, v. 35 n. 6, p. 837-852, 2019.

OLDONI, H. et al. Delineamento de zonas de manejo de irrigação em vinhedo com base na granulometria do solo. In: Congresso Brasileiro de Agricultura de Precisão, 2018, Curitiba. Construção dos dados na era da digitalização agrícola. Curitiba: AsBraAP, 2018. p. 52-58.

OLDONI, H.; BASSOI, L. H. Delineation of irrigation management zones in a Quartzipsamment of the Brazilian semiarid region. Pesquisa Agropecuária Brasileira, v. 51, p. 1283-1294, 2016.

OLIVERA-GUERRA, L.; MERLIN, O.; ER-RAKI, S. Irrigation retrieval from Landsat optical/thermal data integrated into a crop water balance model: A case study over winter wheat fields in a semi-arid region. Remote Sensing of Environment, v. 239, p.1-18, 2020.

ORTEGA-REIG, M. et al Institutional and management implications of drip irrigation introduction in collective irrigation systems in Spain. Agriculture Water Management, v. 187, 164-172, 2017.

PAVÃO, G. C. et al. Avaliação da técnica da TDR no monitoramento de solução xilemática em cana-de-açúcar. Irriga, v. 1, n. 1, p. 144-150, 2014.

PAVÃO, G. C.; SIMIONE, J. R.; SOUZA, C. F. Construção de sondas TDR e avaliação em diferentes softwares de aplicação técnica. Engenharia na Agricultura, v. 25, n. 3, p. 283-289, 2017.

QUEBRAJO, L. et al. Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet. Biosystems Engineering, v. 165, p. 77-87, 2018.

RAMOS, F. T. et al. Acurácia e calibração de uma sonda de capacitância em um Neossolo Quartzarênico cultivado com caju. Bioscience Journal, v. 30, n. 6, 2014.

REYES-GONZÁLEZ, A. et al. Comparative analysis of METRIC model and atmometer methods for estimating actual evapotranspiration. International Journal of Agronomy, v. 2017, p.1-17, 2017.

REYES-GONZÁLEZ, A. et al. Estimation of crop evapotranspiration using satellite remote sensing-based vegetation index. Advances in Meteorology, v. 2018, p. 1-13, 2018.

ROBINSON, D. A. et al. A Review of advances in dielectric and electrical conductivity measurement in soils using Time Domain Reflectometry. Vadose Zone Journal, v. 2, n. 4, p. 444-475, 2003.

ROSSMAN, L. A., EPANET 2 users manual. U.S. Environmental Protection Agency, Cincinnati, Ohio. 2000. 200p.

ROUDIER, P. et al. Management zone delineation using a modified watershed algorithm. Precision Agriculture, v. 9, p. 233-250, 2008.

ROUJEAN, J. L.; BREON, F.M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, v. 51, n. 3, p. 375-384, 1995.

ROUSE, J.W. et al. Monitoring vegetation systems in the great plains with ERTS. In: FRADEN, S. C.; MARCANTI, E.P.; BECKER, M. A. (Eds.), Third ERTS-1 Symposium, 10-14 Dec. 1973, NASA SP-351, Washington D.C. NASA, p. 309-317, 1974.

SANTAELLA, L. et al. Desvendando a Internet das Coisas. Revista GEMInIS, v. 4, n. 2, p. 19-32, 2013.

SANTOS, C. et al. Integrating satellite-based evapotranspiration with simulation models for irrigation management at the scheme. Irrigation Science, v. 26, p. 277-288, 2008.

SCHEPERS, A. R. et al. Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agronomy Journal, v. 96 n. 1, 195-203, 2004.

SHIRATSUCHI, L. S. et al. Sensoriamento remoto: conceitos básicos e aplicações na agricultura de precisão. In: BERNARDI, A. C. et al. Agricultura de Precisão: Resultados de um novo olhar. Brasília: Embrapa, 2014. cap. 4, p.58-73.

SMITH, R.; BAILLIE, J. Defining precision irrigation: a new approach to irrigation management. In: IRRIGATION AND DRAINAGE CONFERENCE, 1., 2009, Swan Hill, Australia. Anais... Irrigation Australia Irrigation and Drainage Conference: Irrigation Today - Meeting the Challenge, 2009, p. 1-6.

SOUZA, C. F. et al. Calibração da Reflectometria no Domínio do Tempo (TDR) para a estimativa da concentração da solução no solo. Engenharia Agrícola, v. 26, n. 1, p. 282-291, 2006a.

SOUZA, C. F. et al. Calibração de sonda FDR e TDR para a estimativa da umidade em dois tipos de solo. Irriga, v. 18, n. 4, p. 597-606, 2013.

SOUZA, C. F. et al. Monitoramento do teor de água no solo em tempo real com as técnicas de TDR e FDR. Irriga, v. 1, n. 1, p. 26-42, 2016a.

SOUZA, C. F. et al. Sonda de TDR para a estimativa de umidade em bagaço de Cana-de-açúcar. Engenharia Agrícola, v. 36, n. 1, p. 24-35, 2016b.

SOUZA, C. F. et al. Sondas de TDR para a estimativa da umidade e da condutividade elétrica do solo. Irriga, v. 11, n. 1, p. 12-25, 2006b.

SOUZA, C. F.; CONCHESQUI, M. E. S.; SILVA, M. B. Semiautomatic irrigation management in tomato. Engenharia Agrícola, v. 39, n. esp, p. 118-125, 2019.

SOUZA, C. F.; MATSURA, E. E. Distribuição da água no solo para o dimensionamento da irrigação por gotejamento. Revista Brasileira de Engenharia Agrícola e Ambiental, v. 8, n. 1, p. 7-15, 2004.

TARJUELO, J. M. et al. Efficient water and energy use in irrigation modernization: Lessons from Spanish case studies Agricultural Water Management, v.162, p. 67-77, 2015.

TOPP, G. C.; DAVIS, J. L.; ANNAN, A. P. Electromagnetic determination of soil-water content: Measurement in coaxial transmission lines. Water Resources Research, v. 16, n. 3, p. 574-582, 1980.

VANINO, S. et al. Estimation of evapotranspiration and crop coefficients of ten done vineyards using multi-sensor remote sensing data in a Mediterranean environment. Remote Sensing, v. 7, n. 11, p.14708-14730, 2015.

VELLIDIS, G. et al. A Dynamic variable rate irrigation control system. In: PROCEEDINGS OF THE 13th INTERNATIONAL CONFERENCE ON PRECISION AGRICULTURE 13., 2016, Saint Louis, Missouri, USA. Anais...Missouri:The International Society of Precision Agriculture, 2016. p.1-9.

VILLAGRA, P. et al. Estimation of water requirements and Kc values of “Thompson Seedless” table grapes grown in the overhead trellis system, using the Eddy covariance method. Chilean Journal of Agricultural Research, v. 74, n. 2, p. 213-218, 2014.

VIRNODKAR, S. S. et al. Remote sensing and machine learning for crop water stress determination in various crops: a critical review. Precision Agriculture, v. 21, p. 1121-1155, 2020.

ZARCO-TEJADA, P. J. et al. Relationships between net photosynthesis and steady-state chlorophyll fluorescence retrieved from air-borne hyperspectral imagery. Remote Sensing of Environment, v. 136, p.24-258, 2013.

ZHANG, X. C, et al. Simulation of Time-Domain Reflectometry. Applied Mechanics and Materials v. 864, p. 206-211, 2017.




Revista Ciência Agronômica ISSN 1806-6690 (online) 0045-6888 (impresso), Site: www.ccarevista.ufc.br, e-mail: ccarev@ufc.br - Fone: (85) 3366.9702 - Expediente: 2ª a 6ª feira - de 7 às 17h.