Publicado:
2025-11-13Edição:
v. 21 n. 2 (2024)Seção:
Actualidad TecnológicaAgricultura 4.0 En Anacardo: Barreras Y Estrategias De Adopción
Agriculture 4.0 In Cashew: Barriers And Adoption Strategies
Palavras-chave:
Anarcado, agricultura 4.0, control de plagas, inteligencia artificial, adopción de tecnología, barreras socio-técnicas (es).Palavras-chave:
Cashew, agriculture 4.0, pest control, artificial intelligence, technology adoption, socio-technical barriers (en).Downloads
Resumo (es)
El anacardo (Anacardium occidentale L.) es un cultivo tropical estratégico cuya productividad se ve afectada por plagas y enfermedades. Las tecnologías de Agricultura 4.0, como inteligencia artificial, drones y sensores, ofrecen alto potencial para el manejo fitosanitario de precisión; sin embargo, su implementación en agroecosistemas tropicales enfrenta barreras socio-técnicas. Mediante un análisis de literatura y un estudio de caso en Vichada, Colombia, se validó la eficacia de estas herramientas, destacando ventajas como el monitoreo temprano y la aplicación localizada de insumos. No obstante, se identificaron limitantes claves: escasez de datos locales, altos costos y falta de capacitación. El estudio concluye que el futuro de la agricultura inteligente en anacardo requiere integrar innovación tecnológica con modelos de negocio inclusivos, (ej. MaaS), y consorcios de datos público-privados. Se propone una hoja de ruta para alinear el potencial de la Agricultura 4.0 con las condiciones socioeconómicas y ambientales del campo.
Resumo (en)
Cashew (Anacardium occidentale L.) is a key tropical crop whose productivity is threatened by pests and diseases. Although Agriculture 4.0 technologies offer enormous technical potential for precision management, this article argues that their real viability in complex tropical agroecosystems critically depends on overcoming socio-technical barriers that the literature often underestimates. Through a literature analysis and a practical case study in Vichada, Colombia, the operational potential of these tools is validated, but the scarcity of local data, high costs, and lack of training are identified as the main bottlenecks. It is concluded that the future of smart agriculture in cashew requires an integrated approach that combines technological in novation with inclusive business models (e.g., Machinery-as-a-Service) and data consortia. This work proposes a roadmap to align technological potential with field reality.
Referências
K. Peiris and W. Wimalaratana, “The cashew crucible: Progress, problems and pros pects of the sri lankan cashew journey,” International Journal of Research and Innovation in Social Science, 2024.
G. M. Rwegasira, M. M. Mwatawala, R. G. Rwegasira, A. N. Rashidi, N. Wilson, and W. J. George, “Economic rationale of using african weaver ants, oecophylla lon ginoda latreille (hymenoptera: Formicidae) for sustainable management of cashew pests in tanzania,” in Sustainable Agriculture Reviews, 2020.
“Documento técnico de priorización productiva para el departamento del vichada,” tech. rep., Gobernación de Vichada, OCAD, Colombia, 2020. Consultado para diagnostico agroproductivo del cultivo de Marañón.
G. K. Mahapatro, “Helopeltis management by chemicals in cashew: A critical concern,” Indian Journal of Entomology, 2008.
J. Anikwe, F. Okelana, H. Otuonye, L. A. Hammed, and O. Aliyu, “The integrated management of an emerging insect pest of cashew: A case study of the cashew root and stemborer, plocaederus ferrugineus in ibadan, nigeria,” Journal of Agriculture, Forestry and the Social Sciences, 2009.
F. Monteiro, M. M. Romeiras, J. Barnabe, S. Catarino, D. Batista, and M. Sebastiana, “Disease-causing agents in cashew: A review in a tropical cash crop,” Agronomy, 2022.
M. K. Rajagopal and M. Balamurugan, “Artificial intelligence-based drone for early disease detection and precision pesticide management in cashew farming,” Conference Proceedings, 2023.
S. R. Nayak, R. B. Hegde, A. S. Rao, and H. K. Sachidananda, “Revitalizing agriculture with the potential of cashew nutshell liquid: a comprehensive exploration and synergy with ai,” Deleted Journal, 2024.
S. K. Swarnkar, Y. K. Rathore, and V. K. Swarnkar, Machine learning models for early detection of pest infestation in crops. 2024.
M. Mittal, V. Gupta, M. Aamash, and T. K. Upadhyay, “Machine learning for pest detection and infestation prediction: A comprehensive review,” Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, 2024.
T. Ilakya, B. Lakshmi, S. Jeyanthi, U. Esakkiammal, T. Abinaya, and C. F. T. Cenate, “Towards resilient crops: Disease detection and pest control strategies,” in IEEE Conference on PECTS, 2024.
D. Kapetas, P. Christakakis, S. Faliagka, Katsoulas, and E. M. Pechlivani, “Ai-driven insect detection, real-time monitoring, and population forecasting in greenhouses,” AgriEngineering, 2025.
K. Dasari, S. A. Yadav, L. Kansal, J. Adilakshmi, G. Kaliyaperumal, and A. Albawi, “Fusion of hyperspectral imaging and convolutional neural networks for early detection of crop diseases in precision agriculture,” 2024.
F. Ali, A. Razzaq, W. Tariq, A. Hameed, A. Rehman, K. Razzaq, S. Sarfraz, N. A. Rajput, H. E. M. Zaki, M. S. Shahid, and G. Ondrasek, “Spectral intelligence: Ai-driven hyperspectral imaging for agricultural and ecosystem applications,” Agronomy, 2024.
N. P. Vidhya and R. Priya, “Automated diagnosis of the severity of tmb infestation in cashew plants using yolov5,” Journal of Image and Graphics, 2024.
A. Upadhyay, N. S. Chandel, K. P. Singh, S. K. Chakraborty, B. M. Nandede, M. Ku mar, A. Subeesh, K. Upendar, A. Salem, and A. Elbeltagi, “Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture,” Artificial Intelligence Review, 2025.
M. K. Rajagopal, B. Murugan, P. Arumugam, C. Rajurkar, R. Anand, and S. Kumar, “Intelligent drone design for precision cashew farming,” 2024.
E. Singh, A. Pratap, U. Mehta, and S. Azid, “Smart agriculture drone for crop spraying using image-processing and machine learning techniques: Experimental validation,” Iot, 2024.
R. Guebsi, S. Mami, and K. Chokmani, “Drones in precision agriculture: A comprehensive review of applications, technologies, and challenges,” Drones, 2024.
A. N. Barve, M. R. Lajurkar, S. B. Kharbade, A. Bagde, S. J. Waghmare, R. Karande, and S. K. Sathe, “Advancing precision agriculture: The role of uavs and drones in sustainable farming,” Asian Research Journal of Agriculture, 2024.
S. Rishikesavan, P. Kannan, S. Pazhanivelan, R. Kumaraperumal, N. Sritharan, D. Muthumanickam, M. M. R. A. Firnass, V. Baskaran, and V. S. Teja, “Prospects and challenges of drone technology in sustainable agriculture,” Plant Science Today, 2024.
R. Karmakar, T. Paul, and A. Mandal, “A prototype modeling of a smart agriculture monitoring system using iot based drones for spraying pesticides,” Research Square Preprint, 2024.
L. Aiswarya, Siddharam, G. Rajesh, V. Gaddikeri, M. S. Jatav, J. Rajput, and K. Asha, “Smart irrigation management through unmanned aerial vehicles (uavs),” in Smart Agricultural Technologies, 2024.
H. Mahasneh, “Drones in agriculture : Real-world applications and impactful case studies,” Journal of Natural Science Reports, 2024.
J. P. A. R. d. Cunha, L. d. L. Lopes, C. O. R. Alves, and C. B. d. Alvarenga, “Spray deposition and losses to soil from a remotely piloted aircraft and airblast sprayer on coffee,” AgriEngineering, 2024.
O. Kholodyuk and O. Tokarchuk, “The efficiency of using agras drones for spraying, their design, technical and technological features,” Tehnıka, Energetika, Transport APK, 2022.
M. E. Mignoni, E. S. Monteiro, C. Zagonel, and R. Kunst, “Artificial intelligence and its tools in pest control for agricultural production : a review,” Recima21, 2024.
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