Vol. 36 No. NE-1 (2026): XXVIII Congreso Internacional en Ciencias Agrícolas
Artículos de investigación

Automated quantification of cabbage plants grown under plastic mulches using multitemporal orthomosaics

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  • Samuel Uriel Samaniego Gamez,
  • Fidel Núñez Ramírez,
  • MOISES GILBERTO YAÑEZ JUAREZ,
  • María Alejandra Payán Arzapalo,
  • Raúl Enrique Valle Gough,
  • Blancka Yesenia Samaniego Gamez
MOISES GILBERTO YAÑEZ JUAREZ FACULTAD DE AGRONOMÍA- UNIVERSIDAD AUTÓNOMA DE SINALOA

Published 2026-09-23

Copyright (c) 2026 Samuel Uriel Samaniego Gamez, Fidel Núñez Ramírez, Moisés Gilberto Yáñez Juarez, María Alejandra Payán Arzapalo, Raúl Enrique Valle Gough, Blancka Yesenia Samaniego Gamez

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Samaniego Gamez, S. U., Núñez Ramírez, F., YAÑEZ JUAREZ, M. G., Payán Arzapalo, M. A., Valle Gough, R. E., & Samaniego Gamez, B. Y. (2026). Automated quantification of cabbage plants grown under plastic mulches using multitemporal orthomosaics. Acta Universitaria, 36(NE-1), 1-11. https://doi.org/10.15174/au.2026.4929

Abstract

Knowing the number of plants during the establishment of cabbage (Brassica oleracea L. var. capitata) crops allows a better management. Therefore, an experiment was performed to automatically quantify cabbage plants grown under plastic mulch using multitemporal orthomosaics. Plants were identified using artificial intelligence (AI) applied to multitemporal orthomosaics acquired by a multispectral Phantom 4 RTK unmanned aerial vehicle (UAV) at 12, 22, 32, and 47 days after transplanting (DAT). AI-derived counts were compared with manual population counts using the estimation accuracy metric (Ps) and variance analysis (ANOVA). Plastic mulch color did not significantly affect Ps; however, Ps differed across DAT, with the highest coefficient of determination observed at 32 DAT (R² = 0.90). Flights are recommended at 32 DAT to quantify plants.

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References

  1. Adilov, M. M., Rustamov, B. A., Amanova, M. E., & Rustamov, A. S. (2021). Planting dates and seedling age of red cabbage during the spring season in Uzbekistan. IOP Conference Series: Earth and Environmental Science, 939(1), 012035. https://doi.org/10.1088/1755-1315/939/1/012035
  2. Agremo. (s. f.a). Agremo Analyses - Plant Count & Health Monitoring. https://www.agremo.com/documentation/agremo-analyses/
  3. Agremo. (s. f.b). Crop Monitoring & Field Analytics Platform. https://www.agremo.com/
  4. Agremo. (s. f.c). Field Trials Comprehensive User Guide. https://www.agremo.com/documentation/field-trials-comprehensive-user-guide/
  5. Alcaras, E., Parente, C., & Vallario, A. (2020). The importance of the coordinate transformation process in using heterogeneous data in coastal and marine geographic information system. Journal of Marine Science and Engineering, 8(9), 708. https://doi.org/10.3390/jmse8090708
  6. Awasthi, R., Bhandari, K., & Nayyar, H. (2015). Temperature stress and redox homeostasis in agricultural crops. Frontiers in Environmental Science, 3. https://doi.org/10.3389/fenvs.2015.00011
  7. Aziz, A., Arkeman, Y., Kusuma, W. A., Kurniawan, F., Prabowo, G. S., Wirawan, A., Pandjaitan, L., Wardana, T. K., Firmansyah, Y., & Trisasongko, B. H. (2023). Identification of holes in plastic mulch based on UAV multispectral image using template matching algorithm. AIP Conference Proceedings, 2941(1). https://doi.org/10.1063/5.0181351
  8. Banerjee, B. P., Sharma, V., Spangenberg, G., & Kant, S. (2021). Machine learning regression analysis for estimation of crop emergence using multispectral UAV imagery. Remote Sensing, 13(15), 2918. https://doi.org/10.3390/rs13152918
  9. Camargo-Bravo, A., & García-Cueto, R. O. (2012). Evaluación de dos modelos de reducción de escala en la generación de escenarios de cambio climático en el valle de Mexicali en México. Información Tecnológica, 23(3), 11-20. https://doi.org/10.4067/S0718-07642012000300003
  10. Che, Y., Wang, Q., Zhou, L., Wang, X., Li, B., & Ma, Y. (2022). The effect of growth stage and plant counting accuracy of maize inbred lines on LAI and biomass prediction. Precision Agriculture, 23, 2159-2185. https://doi.org/10.1007/s11119-022-09915-1
  11. DJI. (2019). P4 Multispectral. https://www.dji.com/mx/p4-multispectral
  12. Dobosz, B., Gozdowski, D., Koronczok, J., Žukovskis, J., & Wójcik-Gront, E. (2023). Evaluation of maize crop damage using UAV-Based RGB and multispectral imagery. Agriculture, 13(8), 1627. https://doi.org/10.3390/agriculture13081627
  13. Du, X., Huang, D., Dai, L., & Du, X. (2024). Recognition of plastic film in terrain-fragmented areas based on drone visible light images. Agriculture, 14(5), 736. https://doi.org/10.3390/agriculture14050736
  14. Escobosa-García, I., Vázquez-Medina, M., Samaniego-Gámez, B., Valle-Gough, R., Vázquez-Angulo, J., & Núñez-Ramírez, F. (2022). Effect of mulching on cabbage grown in the Mexicali Valley. Revista Mexicana de Ciencias Agrícolas, (28), 197-206. https://doi.org/10.29312/remexca.v13i28.3275
  15. Feng, A., Zhou, J., Vories, E., & Sudduth, K. A. (2020). Evaluation of cotton emergence using UAV-Based Narrow-Band spectral imagery with customized image alignment and stitching algorithms. Remote Sensing, 12(11), 1764. https://doi.org/10.3390/rs12111764
  16. García-Martínez, H., Flores-Magdaleno, H., Khalil-Gardezi, A., Ascencio-Hernández, R., Tijerina-Chávez, L., Vázquez-Peña, M., & Mancilla-Villa, O. R. (2020). Digital count of corn plants using images taken by unmanned aerial vehicles and cross correlation of templates. Agronomy, 10(4), 469. https://doi.org/10.3390/agronomy10040469
  17. Hassan, M. M., & Pailan, E. (2025). Study on effect of spacing and nutrient management in cabbage cultivation in medium land situation of east Medinipur. Indian Journal of Agricultural Research, 59(2), 285-289. https://doi.org/10.18805/ijare.a-5956
  18. Ibrahim, E., & Gobin, A. (2025). Noncloud contaminants in agricultural soil monitoring: quantifying spectral distortions from plastic covers, pylons, and aircraft overpasses. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 15876-15886. https://doi.org/10.1109/jstars.2025.3581045
  19. Isaq, M., Shivaleela, Prabhuraj, A., Arunkumar, H., & Pampanna, Y. (2023). Seasonal incidence of major insect pests of cabbage (Brassica oleracea var. capitata) at Raichur. The Pharma Innovation Journal, 12(5), 1155-1159. https://www.thepharmajournal.com/archives/2023/vol12issue5/parto/12-5-201-726.pdf
  20. Karmakar, P., Teng, S. W., Murshed, M., Pang, S., Li, Y., & Lin, H. (2024). Crop monitoring by multimodal remote sensing: a review. Remote Sensing Applications: Society and Environment, 33, 101093. https://doi.org/10.1016/j.rsase.2023.101093
  21. Khanal, S., Kushal, K. C., Fulton, J. P., Shearer, S., & Ozkan, E. (2020). Remote sensing in agriculture-accomplishments, limitations, and opportunities. Remote Sensing, 12(22), 1-29. https://doi.org/10.3390/rs12223783
  22. Lee, C. J., Yang, M. D., Tseng, H. H., Hsu, Y. C., Sung, Y., & Chen, W. L. (2023). Single-plant broccoli growth monitoring using deep learning with UAV imagery. Computers and Electronics in Agriculture, 207, 107739. https://doi.org/10.1016/j.compag.2023.107739
  23. Li, J., Li, Y., Qiao, J., Li, L., Wang, X., Yao, J., & Liao, G. (2023). Automatic counting of rapeseed inflorescences using deep learning method and UAV RGB imagery. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1101143
  24. Lu, D., Ye, J., Wang, Y., & Yu, Z. (2023). Plant detection and counting: enhancing precision agriculture in UAV and general scenes. IEEE Access, 11, 116196–116205. https://doi.org/10.1109/access.2023.3325747
  25. Lüling, N., Reiser, D., Straub, J., Stana, A., & Griepentrog, H. W. (2023). Fruit volume and leaf-area determination of cabbage by a neural-network-based instance segmentation for different growth stages. Sensors, 23(1), 129. https://doi.org/10.3390/s23010129
  26. Mabry, M. E., Turner, S. D., Gallagher, E. Y., McAlvay, A. C., An, H., Edger, P. P., Moore, J. D., Pink, D. A. C., Teakle, G. R., Stevens, C. J., Barker, G., Labate, J., Fuller, D. Q., Allaby, R. G., Beissinger, T., Decker, J. E., Gore, M. A., & Pires, J. C. (2021). The evolutionary history of wild, domesticated, and feral Brassica oleracea (Brassicaceae). Molecular Biology and Evolution, 38(10), 4419–4434. https://doi.org/10.1093/molbev/msab183
  27. Ngosong, N. T., Boamah, E. D., Fening, K. O., Kotey, D. A., & Afreh-Nuamah, K. (2021). The efficacy of two bio-rational pesticides on insect pests complex of two varieties of white cabbage (Brassica oleracea var. capitata L.) in the coastal savanna region of Ghana. Phytoparasitica, 49, 397-406. https://doi.org/10.1007/s12600-020-00859-8
  28. Okasha, A. M., Abdelkhaliq, E. T., & Zayton, A. M. (2025). Impact of irrigation water quality, frequency, and technique on cabbage yield, water productivity, and soil properties in clay soil. Egyptian Journal of Soil Science, 65, 961-978. https://doi.org/10.21608/ejss.2025.365220.2037
  29. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO). (2015). Base referencial mundial del recurso suelo 2014, Actualización 2015. Sistema internacional de clasificación de suelos para la nomenclatura de suelos y la creación de leyendas de mapas de suelos. Informes sobre recursos mundiales de suelos 106. https://openknowledge.fao.org/server/api/core/bitstreams/dea292cb-370d-46c7-a44d-59a617953c3b/content
  30. Pathak, H., Igathinathane, C., Zhang, Z., Archer, D., & Hendrickson, J. (2022). A review of unmanned aerial vehicle-based methods for plant stand count evaluation in row crops. Computers and Electronics in Agriculture, 198, 107064. https://doi.org/10.1016/j.compag.2022.107064
  31. Pix4D. (2024). PIX4Dfields. https://www.pix4d.com/es/producto/pix4dfields/
  32. Qgis. (2023). QGIS. https://download.qgis.org/downloads/
  33. Rivera, F., & Morán, J. C. (2024). Estrategia de manejo de Brassica oleraceae L var Capitata, empleando buenas prácticas agrícolas, Jinotega, Nicaragua. Biotecnia, 26, 472-477. https://doi.org/10.18633/biotecnia.v26.2327
  34. SOUTH. (2024). [Galaxy G7]. Guangzhou SOUTH Surveying & Mapping Technology Co., Ltd. https://www.southinstrument.com/product/details/pro_tid/3/id/210.html
  35. Valente, J., Sari, B., Kooistra, L., Kramer, H., & Mücher, S. (2020). Automated crop plant counting from very high-resolution aerial imagery. Precision Agriculture, 21, 1366-1384. https://doi.org/10.1007/s11119-020-09725-3
  36. Vong, C. N., Conway, L. S., Feng, A., Zhou, J., Kitchen, N. R., & Sudduth, K. A. (2022). Corn emergence uniformity estimation and mapping using UAV imagery and deep learning. Computers and Electronics in Agriculture, 198, 107008. https://doi.org/10.1016/j.compag.2022.107008
  37. Wang, H., Huang, G., & Zhang, X. (2025). Analysis and properties of polysaccharides extracted from Brassica oleracea L. var. capitata L. by hot water extraction/ultrasonic-synergistic enzymatic method. Ultrasonics Sonochemistry, 114, 107244. https://doi.org/10.1016/j.ultsonch.2025.107244
  38. Wu, B., Zhang, M., Zeng, H., Tian, F., Potgieter, A. B., Qin, X., Yan, N., Chang, S., Zhao, Y., Dong, Q., Boken, V., Plotnikov, D., Guo, H., Wu, F., Zhao, H., Deronde, B., Tits, L., & Loupian, E. (2023). Challenges and opportunities in remote sensing-based crop monitoring: a review. National Science Review, 10(4), nwac290. https://doi.org/10.1093/nsr/nwac290
  39. Wu, W., Chen, L., Liang, R., Huang, S., Li, X., Huang, B., Luo, H., Zhang, M., Wang, X., & Zhu, H. (2024). The role of light in regulating plant growth, development and sugar metabolism: a review. Frontiers in Plant Science, 15. https://doi.org/10.3389/fpls.2024.1507628
  40. Yuan, J., Li, X., Zhou, M., Zheng, H., Liu, Z., Liu, Y., Wen, M., Cheng, T., Cao, W., Zhu, Y., & Yao, X. (2024). Rapidly count crop seedling emergence based on waveform Method (WM) using drone imagery at the early stage. Computers and Electronics in Agriculture, 220, 108867. https://doi.org/10.1016/j.compag.2024.108867
  41. Zahtila, M., & Knura, M. (2022). Visualizing point density on geometry objects: application in an urban area using social media VGI. Journal of Cartography and Geographic Information, 72, 187-200. https://doi.org/10.1007/s42489-022-00113-7

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