Innovative technologies for the management of fruit trees, emphasis on deciduous fruits, avocado and topicals
DOI:
https://doi.org/10.5377/elhigo.v14i1.17977Keywords:
Agricultural crops, Precision farming, Artificial intelligence, Crop Monitoring, Intelligent Irrigation Systems, Nanotechnology, Geographic Information SystemsAbstract
The present study focuses on analyzing innovative technologies applied to crop management in modern agriculture, with an emphasis on fruit trees. The main objective was to understand how these technologies enhance the efficiency, sustainability, and productivity of fruit crop cultivation, aimed at meeting increasing demand and promoting sustainable agricultural practices. The research is structured in three phases. In the first phase, databases documenting the evolution of conventional agricultural practices are reviewed. The second phase focuses on the scientific and technical literature of the last decade, highlighting recent advances in agricultural technologies. The third phase focused on classifying ongoing research and development, providing a forward-looking view of emerging trends. Multidisciplinary analysis criteria were used that include efficiency in the use of resources, environmental sustainability, crop management, post-harvest management and the capacity for large-scale implementation. Innovative technologies identified include precision agriculture, the use of remote sensors, artificial intelligence applied to crop monitoring, and the implementation of crop control systems. The results highlight that innovative technologies in crop management can improve the efficiency of agricultural production by up to 30%, reduce dependence on pesticides, fertilizers and minimize negative environmental impact. In conclusion, the strategic adoption of these technologies can transform agriculture, providing key solutions for global food security and long-term sustainability.
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References
Azam Ansari, M. (2023). Nanotechnology in Food and Plant Science: Challenges and Future Prospects. Plants, 1-34. doi:https://doi.org/10.3390/plants12132565
Baker , B. P., Green, T. A., y Loker, A. J. (2020). Biological control and integrated pest management in organic and conventional systems. Biological Control, 1, 1-9. doi:https://doi.org/10.1016/j.biocontrol.2019.104095
Bwambale , E., Abagale, F. K., y Anornu, G. K. (2023). Data-driven model predictive control for precision irrigation management. Smart Agricultural Technology, 3, 1-12. doi:https://doi.org/10.1016/j.atech.2022.100074
Chakraborty, S. K., Rahul Potdar, S. A., Singh Chandel, N., Jat, D., Dubey, K., y Shelake, P. (2023). AI-enabled farm-friendly automatic machine for washing, image-based sorting, and weight grading of citrus fruits: Design optimization, performance evaluation, and ergonomic assessment. Journal of Field Robotics. doi:https://doi.org/10.1002/rob.22193
Chanchí-Golondrino, G. E., Ospina-Alarcón, M. A., y Saba, M. (2022). IoT System for Monitoring of Climatological Variables in Urban Agriculture Crops. Revista Científica, 44(2), 257-271. doi:https://doi.org/10.14483/23448350.18470
De Armas Costa, R. J., Martín Gómez, P. F., y Rangel Díaz , J. E. (2022). Gulupa fruit (Passiflora edulis Sims), its export potential, matrix and ripening signature: a review. Ciencia y agricultura, 19(1), 15-27. doi:https://doi.org/10.19053/01228420.v19.n1.2022.13822
De la Vega, J. C., Cañarejo, M. A., y Pinto, N. S. (2017). Avances en Tecnología de Atmósferas Controladas y sus Aplicaciones en la Industria De la Vega. Información Tecnológica, 75-86. doi:http://dx.doi.org/10.4067/S0718-0764201700030000
Georgiev, G., Beloev, I., Hristov, G., y Zahariev, P. (2022). LoRa Network-Based System for Remote Monitoring of Agricultural Crops. 2022 30th National Conference with International Participation (TELECOM), 1-4. doi:https://doi.org/10.3390/s22186743
Hassan, W., Manzoor, T., Jaleel, H., y Muhammad, A. (2021). Demand-based water allocation in irrigation systems using mechanism design: A case study from Pakistan. Agricultural Water Management, 256(1). doi:https://doi.org/10.1016/j.agwat.2021.107075
huidaagtech.com. (2023). Intelligent Irrigation Solutions. Recuperado el 10 de 08 de 2023, de https://www.huidaagtech.com/intelligent-irrigation-solutions.html
ICA. (2011). Manejo de problemas fitosanitarios del cultivo de Gulupa. Bogotá: Produmedios. Obtenido de https://repository.agrosavia.co/bitstream/handle/20.500.12324/2262/44992_60739.pdf?sequence=1yisAllowed=y
Intagri. (14 de 08 de 2023). Atmósferas Controladas y Modificadas en Postcosecha. Obtenido de https://www.intagri.com/articulos/poscosecha-comercializacion/atmosferas-controladas-y-modificadas-en-postcosecha
Jain, R. K. (2023). Experimental performance of smart IoT-enabled drip irrigation system using and controlled through web-based applications. Smart Agricultural Technology, 4, 1-20. doi:https://doi.org/10.1016/j.atech.2023.100215
Korotcenkov, G., Simonenko, N., Simonenko, E., Sysoev, V., y Brinzari, V. (2023). Paper-Based Humidity Sensors as Promising Flexible Devices, State of the Art, Part 2: Humidity-Sensor Performances. Nanomaterials, 13(1381), 1-63. doi:https://doi.org/10.3390/nano13081381
Kritchenkov, A. S., Egorov, A. R., Volkova, O. V., Artemjev, A., Kurliuk, A. V., Le, T. A., . . . Khrustalev, V. (2021). Novel biopolymer-based nanocomposite food coatings that exhibit active and smart properties due to a single type of nanoparticles. Food Chemistry, 1-9. doi:https://doi.org/10.1016/j.foodchem.2020.128676
Ligiang, Z., Shouvi, Y., Leibo, L., Zhen, Z., y Shaojun, W. (2011). A Crop Monitoring System Based on Wireless Sensor Network. Procedia Environmental Sciences, 558-565. doi:https://doi.org/10.1016/j.proenv.2011.12.088
Liu, Q., Gu, X., Chen, X., Mumtaz, F., Liu, Y., Wang, C., . . . Zhan, Y. (2022). Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization. Sensors, 22(1611), 1-21. doi:https://doi.org/10.3390/s22041611
Lu, Y., Huang, Y., y Lu, R. (2017). Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review. applied Sciences, 7(189), 1-36. doi:https://doi.org/10.3390/app7020189
Mali, S. C., Raj, S., y Trived, R. (2020). Nanotechnology a novel approach to enhance crop productivity. Biochemistry and Biophysics Reports, 1-4. doi:https://doi.org/10.1016/j.bbrep.2020.100821
Maqsood, U., Abbas, A., Rehman, S., Asghar, A., Kanwal, B., y Saud Shoukat, R. (2022). Comparative Analysis of Fruits and Vegetables Quality Using AI Assisted Technologies: A Review. Foundation University Journal of, 1-25. doi:https://doi.org/10.33897/fujeas.v3i2.688
Miranda, M., Ribeiro , M. D., Spricigo, P. C., Pilon, L., Mitsuyuki, M. C., Correa, D. S., y Ferreira, M. D. (2022). Carnauba wax nanoemulsion applied as an edible coating on fresh tomato for postharvest quality evaluation. Heliyon, 21-9. doi:https://doi.org/10.1016/j.heliyon.2022.e09803
Oon, A., Ahmad, A., Sah, S. M., Abdul Maulud, K. N., Syafiq Yahya, M., Lechner, A. M., y Azhar, B. (2023). The conservation of biodiverse continuous forests and patches may provide services that support oil palm yield: Evidence from satellite crop monitoring. Cleaner Production Letters, 4, 1-10. doi:https://doi.org/10.1016/j.clpl.2023.100036
Mi, D., Thao, V., Thi, T., Thi, K., y Thuy, H. (2024). A review of preservation approaches for extending avocado fruit shelf-life. Journal of Agriculture and Food Research, Volume 16, 2666-1543. Doi: https://doi.org/10.1016/j.jafr.2024.101102.
Poenaru, V., Badea, A., Cimpeanu, S. M., y Irimescu, A. (2015). Multi-temporal multi-spectral and radar remote sensing for agricultural monitoring in the Braila Plain. Agriculture and Agricultural Science Procedia, 6, 506-516. doi:https://doi.org/10.1016/j.aaspro.2015.08.134
Rai, A., Kumari, K., y Vashi, P. (2022). Umbrella review on chilling injuries: Post-harvest issue, cause, and treatment in tomato. Scientia Horticulturae, 1-16. doi:https://doi.org/10.1016/j.scienta.2021.110710
Singh, P., y Saikia, S. (2016). Arduino-based smart irrigation using water flow sensor, soil moisture sensor, temperature sensor and ESP8266 WiFi module. 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 1-4. doi:https://doi.org/10.1109/R10-HTC.2016.7906792
Ungureanu, C., Tihan, G., Zgâr, R., y Pandelea, G. (2023). Bio-Coatings for Preservation of Fresh Fruits and Vegetables. Coatings, 1-34. doi:https://doi.org/10.3390/coatings13081420
Veeramanikandasamy, T., Sambath, K., Rajendran, K., y Sangeetha, D. (2014). Remote Monitoring and Closed Loop Control System for Social Modernization in Agricultural System Using GSM and Zigbee Technology. International Conference on Advances in Electrical Engineering (ICAEE), 1-4. doi:https://doi.org/10.1109/ICAEE.2014.6838438
Veys, C., Chatziavgerinos, F., AlSuwaidi, A., Hibbert, J., Hansen, M., Bernotas, G., . . . Grieve, B. (2019). Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape. Plant Methods, 15(4), 1-12. doi:https://doi.org/10.1186/s13007-019-0389-9
Wieme, J., Mollazade, K., Malounas, I., Zude-Sasse, M., Zhao, M., Gowen, A., . . . Van Beek , J. (2022). Application of hyperspectral imaging systems and artificial intelligence for quality assessment of fruit, vegetables and mushrooms: A review. Biosystems Engineering, 156-176. doi:https://doi.org/10.1016/j.biosystemseng.2022.07.013
Xu, J., Gu, B., y Tian, G. (2022). Review of agricultural IoT technology. Artificial Intelligence in Agriculture, 10-22. doi:https://doi.org/10.1016/j.aiia.2022.01.001
Yamini, B., Pradeep, G., Kalaiyarasi, D., Jayaprakash, M., Janani, G., y Uthayakumar, G. S. (2024). Theoretical study and analysis of advanced wireless sensor network techniques in Internet of Things (IoT). Measurement: Sensors, 33, 101098. Doi : https://doi.org/10.1016/j.measen.2024.101098
Zhang, Z., y Zhu, L. (2023). A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications. Drones, 7(398), 42. doi:https://doi.org/10.1016/j.aiia.2022.01.001
Zhou, X. X., Li, Y. Y., Luo, Y. K., Sun, Y. W., Su, Y. J., Tan, C. W., y Liu, Y. J. (2022). Research on remote sensing classification of fruit trees based on Sentinel-2 multi-temporal imageries. Scientific Reports, 12(1), 11549. doi: https://doi.org/10.1016/j.scienta.2023.112333
Zhou, Z., Zahid, U., Majeed, Y., Nisha, Mustafa, S., Sajjad, M., . . . Fu, L. (2023). Advancement in artificial intelligence for on-farm fruit sorting and transportation. Frontiers in Plant Science. doi: https://doi.org/10.3389/fpls.2023.1082860
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