Ir al menú de navegación principal Ir al contenido principal Ir al pie de página del sitio

Vol. 18 Núm. 2 (2024)
En imprenta / In press

Sección de frutales

Agricultura de precisión en la producción de aguacate: mapeando el panorama de los desarrollos científicos y tecnológicos

https://doi.org/10.17584/rcch.2024v18i2.17428

Publicado 2024-05-01

  • Juan P. Taramuel-Taramuel
    • ,
  • Claudia Nelcy Jiménez-Hernández
    • ,
  • Dursun Barrios

Juan P. Taramuel-Taramuel
Universidad Nacional de Colombia, sede Bogotá, Facultad de Ciencias Económicas, Doctorado en Administración, Bogota (Colombia)

Claudia Nelcy Jiménez-Hernández
Universidad Nacional de Colombia, sede Bogotá, Facultad de Ciencias Agrarias, Departamento de Desarrollo Rural y Agroalimentario, Bogota (Colombia)

Dursun Barrios
Universidad Nacional de Colombia, sede Bogotá, Facultad de Ciencias Agrarias, Grupo de investigación Biogénesis, Bogota (Colombia)

Analysis of keywords and their correlations about precision agriculture on avocado production from 2012 to 2024. Photo: J.P. Taramuel-Taramuel

Resumen

La integración de tecnologías de agricultura de precisión de vanguardia en la producción de aguacate es una estrategia prometedora para impulsar la productividad y la rentabilidad en esta próspera industria. Si bien revisiones anteriores han explorado la aplicación de tecnologías emergentes en el cultivo de aguacate, existe un vacío en el análisis de la producción de patentes. Esta investigación tiene como objetivo cerrar esa brecha identificando tendencias en innovaciones científicas y tecnológicas relacionadas con la agricultura de precisión en el aguacate. A través de un análisis bibliométrico utilizando datos de Scopus y Lens.org, este estudio revela que la producción científica se concentra principalmente en los países industrializados, con una producción de investigación limitada de las principales naciones productoras de aguacate. La investigación se ha centrado en las técnicas de teledetección y procesamiento de imágenes. En términos de desarrollo tecnológico, las innovaciones en captura, recolección y procesamiento de datos agrícolas, así como en componentes para maquinaria agrícola, han sido las más predominantes. Las tecnologías disponibles en el mercado están diseñadas para predecir el rendimiento de los cultivos y evaluar el impacto de factores abióticos como la temperatura, la humedad y las precipitaciones. Al adoptar estas herramientas de agricultura de precisión, los productores de aguacate pueden tomar decisiones basadas en datos para optimizar el uso de recursos, mejorar la salud de los cultivos y, en última instancia, mejorar el rendimiento general de la finca.

Palabras clave

Persea americana Mill., Agricultura digital, Vigilancia tecnológica, Agricultura 4.0, Productividad agrícola

PDF (English)

Referencias

  • Abdulridha, J., Y. Ampatzidis, R. Ehsani, and A.I. de Castro. 2018. Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado. Comput. Electron. Agric. 155, 203-211. Doi: https://doi.org/10.1016/j.compag.2018.10.016
  • Abdulridha, J., R. Ehsani, A. Abd-Elrahman, and Y. Ampatzidis. 2019. A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses. Comput. Electron. Agric. 156, 549-557. Doi: https://doi.org/10.1016/j.compag.2018.12.018
  • Abdulsalam, M., Z. Chekakta, N. Aouf, and M. Hogan. 2023. Fruity: a multi-modal dataset for fruit recognition and 6D-Pose estimation in precision agriculture. pp. 144-149. In: 31st Mediterranean Conference on Control and Automation. Limassol, Cyprus Doi: https://doi.org/10.1109/MED59994.2023.10185851
  • Antle, J.L. and S.T. Snyder. 2023. Smart sprayer for precision agriculture (EP 4223119 A1). Stout Industrial Tech Inc. https://lens.org/064-570-967-646-298
  • Aria, M. and C. Cuccurullo. 2017. Bibliometrix: an R-tool for comprehensive science mapping analysis. J. Informetr. 11(4), 959-975. Doi: https://doi.org/10.1016/j.joi.2017.08.007
  • Arias, J.S., A. Hurtado-Salazar, and N. Ceballos-Aguirre. 2021. Current overview of Hass avocado in Colombia. Challenges and opportunities: a review. Ciênc. Rural 51(8), e20200903. Doi: https://doi.org/10.1590/0103-8478cr20200903
  • Balafoutis, A., B. Beck, S. Fountas, J. Vangeyte, T. van der Wal, I. Soto, M. Gómez-Barbero, A. Barnes, and V. Eory. 2017. Precision agriculture technologies positively contributing to GHG emissions mitigation, farm productivity and economics. Sustainability 9(8), 1339. Doi: https://doi.org/10.3390/su9081339
  • Builes, S. and M. Duque. 2020. Socio-economic and technological typology of avocado cv. Hass farms from Antioquia (Colombia). Ciênc. Rural 50(7), e20190188. Doi: https://doi.org/10.1590/0103-8478cr20190188
  • Cáceres-Zambrano, J., C.N. Jiménez-Hernández, and D. Barrios. 2022a. Tendencias en investigación y desarrollo tecnológico en la cadena productiva de aguacate (Persea americana L.). Rev. EIA 19(38), 3826. Doi: https://doi.org/10.24050/reia.v19i38.1573
  • Cáceres-Zambrano, J., J.G. Ramírez-Gil, and D. Barrios. 2022b. Validating technologies and evaluating the technological level in avocado production systems: a value chain approach. Agronomy 12(12), 3130. Doi: https://doi.org/10.3390/agronomy12123130
  • Cisternas, I., I. Velásquez, A. Caro, and A. Rodríguez. 2020. Systematic literature review of implementations of precision agriculture. Comput. Electron. Agric. 176, 105626. Doi: https://doi.org/10.1016/j.compag.2020.105626
  • Cohen, H.L. 2018. Computer-implemented methods, computer readable medium and systems for a precision agriculture platform with a satellite data model (US 2018/0330487 A1). Cohen Harris Lee. https://lens.org/108-133-195-289-903
  • Da Silveira, F., F.H. Lermen, and F.G. Amaral. 2021. An overview of agriculture 4.0 development: systematic review of descriptions, technologies, barriers, advantages, and disadvantages. Comput. Electron. Agric. 189, 106405. Doi: https://doi.org/10.1016/j.compag.2021.106405
  • De Castro, A.I., R. Ehsani, R. Ploetz, J.H. Crane, and J. Abdulridha. 2015. Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado. Remote Sens. Environ. 171, 33-44. Doi: https://doi.org/10.1016/j.rse.2015.09.011
  • Díaz, R. 2021. Mercado mundial de aguacate: 60 años del liderazgo de México y su impacto en la próxima década. Anáhuac J. 21(2), 12-49. Doi: https://doi.org/10.36105/theanahuacjour.2021v21n2.01
  • Donadon, G., L.G. Bot, and B. Miolo. 2023. Sowing element for precision agricultural seeders and seeder including element of this kind (Patent US 202318183915 A). Maschio Gaspardo Spa. https://lens.org/106-345-708-638-710
  • DTN. 2023a. Agriculture. In: https://www.dtn.com/agriculture/; consulted: April, 2024.
  • DTN. 2023b. ClearAg Viewer: change how you view your critical ag data. In: https://www.dtn.com/wp-content/uploads/2023/03/ss_clearagviewer.pdf; consulted: April, 2024.
  • Duarte, P.F., M.A. Chaves, C.D. Borges, and C.R.B. Mendonça. 2016. Avocado: characteristics, health benefits and uses. Ciênc. Rural 46(4), 747-754. Doi: https://doi.org/10.1590/0103-8478cr20141516
  • EL Amraoui, K., M. Lghoul, A. Ezzaki, L. Masmoudi, M. Hadri, H. Elbelrhiti, and A.A. Simo. 2022. Avo-AirDB: an avocado UAV database for agricultural image segmentation and classification. Data Brief 45, 108738. Doi: https://doi.org/10.1016/j.dib.2022.108738
  • Erazo-Mesa, E., A. Echeverri-Sánchez, and J.G. Ramírez-Gil. 2022. Advances in Hass avocado irrigation scheduling under digital agriculture approach. Rev. Colomb. Cienc. Hortic. 16(1), e13456. Doi: https://doi.org/10.17584/rcch.2022v16i1.13456
  • FAO. 2022. Major tropical fruits: preliminary results 2021. Rome.
  • FAOSTAT. 2022. Production quantity. Crops and livestock products. In: https://www.fao.org/faostat/en/#data/QCL; consulted: April, 2024.
  • Gokcebag, S., H.S. Gecim, K. Aydogdu, G. Duztas, and H. Arslan. 2023. Precision agriculture kit (Patent TR 2021021521 A). Cankaya Univ. https://lens.org/083-142-523-856-356
  • Grant, D., S.T. Snyder, and J.L. Antle. 2023. Machine vision plant tracking system for precision agriculture (Patent US 202217672647 A). Stout Industrial Tech Inc. https://lens.org/081-466-413-442-10X
  • Jeong, Y.-J., K.-I. Kim, C.-D. Lim, B.-K. Kim, Y.-B. Kim, J.-A. Shin, D.-K. Woo, D.-W. Ryoo, Y.-J. Lim, and S.-J. Ha. 2023. Apparatus and method for providing wide-area precision agriculture service based on collaboration between heterogeneous drones (Patent US 2023/0165182 A1). Electronics & Telecommunications Res Inst. https://lens.org/153-128-257-259-001
  • Jezewski, W. 2023. Hybrid system for monitoring and managing of crops, especially in agriculture (Patent EP 22178233 A). Promet Plast S C Elzbieta Jezewska Andrzej Jezewski. https://lens.org/032-288-688-260-288
  • Lacerda, R.T.O., L. Ensslin, and S.R. Ensslin. 2012. Uma análise bibliométrica da literatura sobre estratégia e avaliação de desempenho. Gest. Prod. 19(1), 59-78. Doi: https://doi.org/10.1590/S0104-530X2012000100005
  • Lee, C.-L., R. Strong, and K.E. Dooley. 2021. Analyzing precision agriculture adoption across the globe: a systematic review of scholarship from 1999-2020. Sustainabiblity 13(18), 10295. Doi: https://doi.org/10.3390/su131810295
  • Marconi, L., D. Mengoli, A. Sala, and R. Tazzari. 2023. Lifting system for a farm vehicle for precision agriculture and relative farm vehicle (Patent IT 202100023096 A). Univ Bologna Alma Mater Studiorum. https://lens.org/040-362-664-457-951
  • Mehmood, A., M. Ahmad, and Q.M. Ilyas. 2023. On precision agriculture: enhanced automated fruit disease identification and classification using a new ensemble classification method. Agriculture 13(2), 500. Doi: https://doi.org/10.3390/agriculture13020500
  • Mejiá-Cabrera, H.I., J. Flores, J. Sigueñas, V.A. Tuesta-Monteza, and M.G. Forero. 2020. Identification of Lasiodiplodia Theobromae in avocado trees through image processing and machine learning. 115102F. Proc. SPIE 11510, Applications of Digital Image Processing XLIII. Vol. 11510. Doi: https://doi.org/10.1117/12.2567322
  • Moreno-Bernal, P., P. Arizmendi-Peralta, J.A. Hernández-Aguilar, J. del Carmen Peralta-Abarca, and J.G. Velásquez-Aguilar. 2023. IoT platform for monitoring nutritional and weather conditions of avocado production. pp. 95-109. In: Nesmachnow, S., and L. Hernández Callejo (eds) Smart cities. ICSC-CITIES 2022.Communications in Computer and Information Science. Vol. 1706. Springer, Cham, Switzerland. Doi: https://doi.org/10.1007/978-3-031-28454-0_7
  • Moreno-Ortega, G., C. Pliego, D. Sarmiento, A. Barceló, and E. Martínez-Ferri. 2019. Yield and fruit quality of avocado trees under different regimes of water supply in the subtropical coast of Spain. Agric. Water Manag. 221, 192-201. Doi: https://doi.org/10.1016/j.agwat.2019.05.001
  • Ouzzani, M., H. Hammady, Z. Fedorowicz, and A. Elmagarmid. 2016. Rayyan-a web and mobile app for systematic reviews. Syst. Rev. 5(1), 210. Doi: https://doi.org/10.1186/s13643-016-0384-4
  • Page, M.J., J.E. McKenzie, P.M. Bossuyt, I. Boutron, T.C. Hoffmann, C.D. Mulrow, L. Shamseer, J.M. Tetzlaff, E.A. Akl, S.E. Brennan, R. Chou, J. Glanville, J.M. Grimshaw, A. Hróbjartsson, M.M. Lalu, T. Li, E.W. Loder, E. Mayo-Wilson, S. McDonald, L.A. McGuinness, L.A. Stewart, J. Thomas, A.C. Tricco, V.A. Welch, P. Whiting, and D. Moher. 2021. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst. Rev. 10(1), 89. Doi: https://doi.org/10.1186/s13643-021-01626-4
  • Pérez-Bueno, M.L., M. Pineda, C. Vida, D. Fernández-Ortuño, J.A. Torés, A. de Vicente, F.M. Cazorla, and M. Barón. 2019. Detection of white root rot in avocado trees by remote sensing. Plant Dis. 103(6), 1119-1125. Doi: https://doi.org/10.1094/PDIS-10-18-1778-RE
  • Ramírez-Gil, J.G., G.O. Giraldo, and J.G. Morales. 2018. Design of electronic devices for monitoring climatic variables and development of an early warning system for the avocado wilt complex disease. Comput. Electron. Agric. 153, 134-143. Doi: https://doi.org/10.1016/j.compag.2018.08.002
  • Ramirez-Guerrero, T., M.I. Hernández-Pérez, M.S. Tabares, and E. Villanueva. 2023. Characterization of variables for modeling agroclimatic and phytosanitary events in agricultural crops using deep learning models. J. Phys. Conf. Ser. 2515(1), 012009. Doi: https://doi.org/10.1088/1742-6596/2515/1/012009
  • Rivera, G., R. Porras, R. Florencia, and J.P. Sánchez-Solís. 2023. LiDAR applications in precision agriculture for cultivating crops: a review of recent advances. Comput. Electron. Agric. 207, 107737. Doi: https://doi.org/10.1016/j.compag.2023.107737
  • Robson, A., M.M. Rahman, and J. Muir. 2017. Using worldview satellite imagery to map yield in avocado (Persea americana): a case study in Bundaberg, Australia. Remote Sens. 9(12), 1223. Doi: https://doi.org/10.3390/rs9121223
  • Rogovska, N., D.A. Laird, C.-P. Chiou, and L.J. Bond. 2019. Development of field mobile soil nitrate sensor technology to facilitate precision fertilizer management. Precision Agric. 20(1), 40-55. Doi: https://doi.org/10.1007/s11119-018-9579-0
  • Roy, S.K. and D. De. 2022. Genetic algorithm based Internet of precision agricultural things (IopaT) for agriculture 4.0. Internet Things 18, 100201. Doi: https://doi.org/10.1016/j.iot.2020.100201
  • Ruthes, S. and C.L. Silva. 2015. O uso de estudos prospectivos na análise de políticas públicas: uma análise bibliométrica. pp. 1-19. In: ALTEC - Congresso Latino-Iberoamericano de Gestão Da Tecnologia. Porto Alegre, Brazil.
  • Saiz-Rubio, V. and F. Rovira-Más. 2020. From smart farming towards agriculture 5.0: a review on crop data management. Agronomy 10(2), 207. Doi: https://doi.org/10.3390/agronomy10020207
  • Salgadoe, A.S.A., A.J. Robson, D.W. Lamb, E.K. Dann, and C. Searle. 2018. Quantifying the severity of phytophthora root rot disease in avocado trees using image analysis. Remote Sens. 10(2), 226. Doi: https://doi.org/10.3390/rs10020226
  • Santos, S.D., A.R. Pereira-Moro, and L. Ensslin. 2015. State of the art of ergonomic costs as criterion for evaluating and improving organizational performance in industry. DYNA 82(191), 163-170. Doi: https://doi.org/10.15446/dyna.v82n191.43733
  • Schwartz, M., Y. Maldonado, L. Luchsinger, L.A. Lizana, and W. Kern. 2018. Competitive Peruvian and Chilean avocado export profile. Acta Hortic. 1194, 1079-1084. Doi: https://doi.org/10.17660/ActaHortic.2018.1194.154
  • Scimago. 2023. Scimago Journal & Country Rank. Scimago Journal & Country Rank. https://www.scimagojr.com/; consulted: October, 2023.
  • Staples, T.E., R.C. Miller, M. Hoosein, and V.M. Finn. 2019. System and method for prescribing fertilizer application rates for spatial distribution of a product (Patent US 201616081327 A; US 201662303856 P; CA 2016050323 W). Nutrien Ag Solutions Canada Inc. https://lens.org/125-080-646-990-003
  • The Climate Corporation. 2023. Digital farming’s leading software platform.In: https://climate.com/; consulted: October, 2023.
  • Torres-Madronero, M.C., T. Rondón, R. Franco, M. Casamitjana, and J. Trochez. 2023. Spectral characterization of avocado Persea Americana Mill. Cv. Hass using spectrometry and imagery from the visible to near-infrared range. TecnoLógicas 26(56), e2567. Doi: https://doi.org/10.22430/22565337.2567
  • Tran, V.L., T.N.C. Doan, F. Ferrero, T.L Huy, and N. Le-Thanh. 2023. The novel combination of nano vector network analyzer and machine learning for fruit identification and ripeness grading. Sensors 23(2), 952. Doi: https://doi.org/10.3390/s23020952
  • Tu, Y.-H., K. Johansen, S. Phinn, and A. Robson. 2019. Measuring canopy structure and condition using multi-spectral UAS imagery in a horticultural environment. Remote Sens. 11(3), 269. Doi. https://doi.org/10.3390/rs11030269
  • Tu, Y.-H., S. Phinn, K. Johansen, and A. Robson. 2018. Assessing radiometric correction approaches for multi-spectral UAS imagery for horticultural applications. Remote Sens. 10(11), 1684. Doi: https://doi.org/10.3390/rs10111684
  • USPTO. 2023. USPTO trademark & patent filings: stout industrial technology. In: https://uspto.report/company/Stout-Industrial-Technology-Inc; consulted: October, 2023.
  • Van Eck, N.J., and L. Waltman. 2014. Visualizing bibliometric networks. pp. 285-320. In: Ding, Y., R. Rousseau, and D. Wolfram (eds.). Measuring scholarly impact: methods and practice. Springer, Cham, Switzerland. Doi: https://doi.org/10.1007/978-3-319-10377-8_13
  • Vasconez, J.P., J. Delpiano, S. Vougioukas, and F. Auat Cheein. 2020. Comparison of convolutional neural networks in fruit detection and counting: a comprehensive evaluation. Comput. Electron. Agric. 173, 105348. Doi: https://doi.org/10.1016/j.compag.2020.105348
  • Veneros, J., L. García, E. Morales, V. Gómez, M. Torres, and F. López-Morales. 2020. Aplicación de sensores remotos para el análisis de cobertura vegetal y cuerpos de agua. Idesia 38(4), 99-107. Doi: https://doi.org/10.4067/S0718-34292020000400099
  • White, R.D. 2015. Iteris greenlights ways to better account for its revenue. In: Los Angeles Times, https://www.latimes.com/business/la-fi-stock-spotlight-iteris-20150504-story.html; consulted: October, 2023.
  • Yábar-Gamarra, D., D. Rosales-Gurmendi, R. Manzanares-Grados, K.I. Curasi-Anchayhua, J.S. Bulnes, and R.R. Clemente. 2023. Design of a radio frequency robot for the analysis of farmlands of avocado crops in Peru. 601. In: Proc. 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023). Doi: https://doi.org/10.18687/LACCEI2023.1.1.601
  • Zhang, N., M. Wang, and N. Wang. 2002. Precision agriculture-a worldwide overview. Comput. Electron. Agric. 36(2-3), 113-132. Doi: https://doi.org/10.1016/S0168-1699(02)00096-0
  • Zude-Sasse, M., S. Fountas, T.A. Gemtos, and N. Abu-Khalaf. 2016. Applications of precision agriculture in horticultural crops. Eur. J. Hortic. Sci. 81(2), 78-90. Doi: https://doi.org/10.17660/eJHS.2016/81.2.2

Descargas

Los datos de descargas todavía no están disponibles.

Artículos similares

1 2 > >> 

También puede Iniciar una búsqueda de similitud avanzada para este artículo.