Skip to main navigation menu Skip to main content Skip to site footer

Vol. 18 No. 2 (2024)
Horticultural Excellence: Innovations in Quality, Technology, and Sustainability

Fruits section

Precision agriculture in avocado production: Mapping the landscape of scientific and technological developments

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

Published 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

Abstract

The integration of cutting-edge precision agriculture technologies into avocado production is a promising strategy to boost productivity and profitability in this thriving industry. While previous reviews have explored the application of emerging technologies in avocado cultivation, there is a gap in the analysis of patent production. This research aims to bridge that gap by identifying trends in both scientific and technological innovations related to precision agriculture in avocado. Through a bibliometric analysis using data from Scopus and Lens.org, this study reveals that scientific production is primarily concentrated in industrialized countries, with limited research output from major avocado-producing nations. The focus of research has been on remote sensing and image processing techniques. In terms of technological development, innovations in agricultural data capture, collection, and processing, as well as components for agricultural machinery, have been the most prevalent. Market-available technologies are designed to predict crop yields and assess the impact of abiotic factors such as temperature, humidity, and precipitation. By adopting these precision agriculture tools, avocado farmers can make data-driven decisions to optimize resource use, improve crop health, and ultimately enhance overall farm performance.

Keywords

Persea americana Mill., Digital agriculture, Technological surveillance, Agriculture 4.0, agricultural productivity

XML PDF

References

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. DTN. 2023a. Agriculture. In: https://www.dtn.com/agriculture/; consulted: April, 2024.
  18. 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.
  19. 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
  20. 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
  21. 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
  22. FAO. 2022. Major tropical fruits: preliminary results 2021. Rome.
  23. FAOSTAT. 2022. Production quantity. Crops and livestock products. In: https://www.fao.org/faostat/en/#data/QCL; consulted: April, 2024.
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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.
  45. 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
  46. 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
  47. 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
  48. 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
  49. Scimago. 2023. Scimago Journal & Country Rank. Scimago Journal & Country Rank. https://www.scimagojr.com/; consulted: October, 2023.
  50. 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
  51. The Climate Corporation. 2023. Digital farming’s leading software platform.In: https://climate.com/; consulted: October, 2023.
  52. 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
  53. 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
  54. 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
  55. 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
  56. USPTO. 2023. USPTO trademark & patent filings: stout industrial technology. In: https://uspto.report/company/Stout-Industrial-Technology-Inc; consulted: October, 2023.
  57. 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
  58. 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
  59. 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
  60. 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.
  61. 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
  62. 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
  63. 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

Downloads

Download data is not yet available.

Similar Articles

1 2 3 4 > >> 

You may also start an advanced similarity search for this article.