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Cotton yield estimation using several vegetation indices

Year 2024, Volume: 8 Issue: 1, 139 - 151, 19.01.2024
https://doi.org/10.31127/tuje.1346353

Abstract

Accurate yield estimation before harvest is important for farmers and researchers to optimize field management and increase productivity. The purpose of this study is to develop efficient cotton plant productivity using field studies and satellite imagery. Nitrogen (N) fertilizer is an important nutrient in plant development, and when suboptimal amounts are applied, it can cause yield reductions. Different vegetation indices were employed to analyze the dynamics and yield of cotton plants, with a primary focus on the Red, Near-Infrared (NIR), and Red Edge bands derived from satellite imagery. The objective was to assess the nitrogen content in the plants. The present study involved a comparative analysis of various vegetation indicators in relation to cotton plant production. The productivity of the cotton plant was assessed by employing the indices that exhibited the most influence. The analysis revealed that the MCARI index exhibited the worst weaknesses, while the CLRE index demonstrated the main performance. The productivity of each index was computed, and it was observed that the CLRE index exhibited the closest proximity to the average productivity of 34.48 cents per hectare (cent/ha). Similar results have been observed in other indices. The MCARI index exhibits a distinct value of 32.08 in comparison to the others indices. The results of this study illustrate the potential of satellite imaging in monitoring cotton yield, hence offering valuable theoretical and technological assistance for estimating cotton production in agricultural areas.

References

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Year 2024, Volume: 8 Issue: 1, 139 - 151, 19.01.2024
https://doi.org/10.31127/tuje.1346353

Abstract

References

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  • Kim, H. J., & Triplett, B. A. (2001). Cotton fiber growth in planta and in vitro. Models for plant cell elongation and cell wall biogenesis. Plant physiology, 127(4), 1361-1366. https://doi.org/10.1104/pp.010724
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  • Ridley, W., & Devadoss, S. (2023). Competition and trade policy in the world cotton market: Implications for US cotton exports. American Journal of Agricultural Economics, 105, 1365-1387. https://doi.org/10.1111/ajae.12370
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  • He, L., & Mostovoy, G. (2019). Cotton yield estimate using Sentinel-2 data and an ecosystem model over the southern US. Remote Sensing, 11(17), 2000. https://doi.org/10.3390/rs11172000
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  • Ahmad, T., Sud, U. C., Rai, A., & Sahoo, P. M. (2020). An Alternative Sampling Methodology for Estimation of Cotton Yield using Double Sampling Approach. Journal of the Indian Society of Agricultural Statistics, 74(3), 217–226.
  • Shi, G., Du, X., Du, M., Li, Q., Tian, X., Ren, Y., ... & Wang, H. (2022). Cotton Yield Estimation Using the Remotely Sensed Cotton Boll Index from UAV Images. Drones, 6(9), 254. https://doi.org/10.3390/drones6090254
  • Lang, P., Zhang, L., Huang, C., Chen, J., Kang, X., Zhang, Z., & Tong, Q. (2023). Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province. Frontiers in Plant Science, 13, 1048479. https://doi.org/10.3389/fpls.2022.1048479
  • Pantazi, X. E., Moshou, D., Alexandridis, T., Whetton, R. L., & Mouazen, A. M. (2016). Wheat yield prediction using machine learning and advanced sensing techniques. Computers and Electronics in Agriculture, 121, 57-65. https://doi.org/10.1016/j.compag.2015.11.018
  • Zhang, J., Huang, Y., Pu, R., Gonzalez-Moreno, P., Yuan, L., Wu, K., & Huang, W. (2019). Monitoring plant diseases and pests through remote sensing technology: A review. Computers and Electronics in Agriculture, 165, 104943. https://doi.org/10.1016/j.compag.2019.104943
  • Hou, P., Liu, Y., Liu, W., Liu, G., Xie, R., Wang, K., ... & Li, S. (2020). How to increase maize production without extra nitrogen input. Resources, Conservation and Recycling, 160, 104913. https://doi.org/10.1016/j.resconrec.2020.104913
  • Ekinci, M., Atamanalp, M., Turan, M., Alak, G., Kul, R., Kitir, N., & Yildirim, E. (2019). Integrated use of nitrogen fertilizer and fish manure: Effects on the growth and chemical composition of spinach. Communications in Soil Science and Plant Analysis, 50(13), 1580-1590. https://doi.org/10.1080/00103624.2019.1631324
  • Guo, Z., Luo, C., Dong, Y., Dong, K., Zhu, J., & Ma, L. (2021). Effect of nitrogen regulation on the epidemic characteristics of intercropping faba bean rust disease primarily depends on the canopy microclimate and nitrogen nutrition. Field Crops Research, 274, 108339. https://doi.org/10.1016/j.fcr.2021.108339
  • Dhivya, R., Amalabalu, P., Pushpa, R., & Kavithamani, D. (2014). Variability, heritability and genetic advance in upland cotton (Gossypium hirsutum L.). African Journal of Plant Science, 8(1), 1-5. https://doi.org/10.5897/AJPS2013.1099
  • Onoda, Y., Wright, I. J., Evans, J. R., Hikosaka, K., Kitajima, K., Niinemets, Ü., ... & Westoby, M. (2017). Physiological and structural tradeoffs underlying the leaf economics spectrum. New Phytologist, 214(4), 1447-1463. https://doi.org/10.1111/nph.14496
  • Lassaletta, L., Billen, G., Grizzetti, B., Anglade, J., & Garnier, J. (2014). 50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland. Environmental Research Letters, 9(10), 105011. https://doi.org/10.1088/1748-9326/9/10/105011
  • Singh, R. J., & Ahlawat, I. P. S. (2012). Dry matter, nitrogen, phosphorous, and potassium partitioning, accumulation, and use efficiency in transgenic cotton-based cropping systems. Communications in Soil Science and Plant Analysis, 43(20), 2633-2650. https://doi.org/10.1080/00103624.2012.716125
  • Alganci, U., Ozdogan, M., Sertel, E., & Ormeci, C. (2014). Estimating maize and cotton yield in southeastern Turkey with integrated use of satellite images, meteorological data and digital photographs. Field Crops Research, 157, 8-19. https://doi.org/10.1016/j.fcr.2013.12.006
  • Liu, Q. S., Li, X. Y., Liu, G. H., Huang, C., & Guo, Y. S. (2016). Cotton area and yield estimation at Zhanhua County of China using HJ-1 EVI time series. In ITM Web of Conferences, 7, 09001. https://doi.org/10.1051/itmconf/20160709001
  • Bian, C., Shi, H., Wu, S., Zhang, K., Wei, M., Zhao, Y., ... & Chen, S. (2022). Prediction of field-scale wheat yield using machine learning method and multi-spectral UAV data. Remote Sensing, 14(6), 1474. https://doi.org/10.3390/rs14061474
  • Leroux, L., Castets, M., Baron, C., Escorihuela, M. J., Bégué, A., & Seen, D. L. (2019). Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices. European Journal of Agronomy, 108, 11-26. https://doi.org/10.1016/j.eja.2019.04.007
  • Elders, A., Carroll, M. L., Neigh, C. S., D'Agostino, A. L., Ksoll, C., Wooten, M. R., & Brown, M. E. (2022). Estimating crop type and yield of small holder fields in Burkina Faso using multi-day Sentinel-2. Remote Sensing Applications: Society and Environment, 27, 100820. https://doi.org/10.1016/j.rsase.2022.100820
  • Azərbaycan Respublikası Beyləqan Rayon İcra Hakimiyyəti (2023). Coğrafi mövqeyi. http://www.beyleqan-ih.gov.az/az/page/13.html
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There are 62 citations in total.

Details

Primary Language English
Subjects Environmental Engineering (Other)
Journal Section Articles
Authors

Bakhtiyar Babashli 0000-0001-7931-1677

Aytaj Badalova 0000-0003-0131-1487

Ramis Shukurov This is me 0009-0003-7582-5082

Agil Ahmadov This is me 0009-0009-9712-4790

Early Pub Date January 7, 2024
Publication Date January 19, 2024
Published in Issue Year 2024 Volume: 8 Issue: 1

Cite

APA Babashli, B., Badalova, A., Shukurov, R., Ahmadov, A. (2024). Cotton yield estimation using several vegetation indices. Turkish Journal of Engineering, 8(1), 139-151. https://doi.org/10.31127/tuje.1346353
AMA Babashli B, Badalova A, Shukurov R, Ahmadov A. Cotton yield estimation using several vegetation indices. TUJE. January 2024;8(1):139-151. doi:10.31127/tuje.1346353
Chicago Babashli, Bakhtiyar, Aytaj Badalova, Ramis Shukurov, and Agil Ahmadov. “Cotton Yield Estimation Using Several Vegetation Indices”. Turkish Journal of Engineering 8, no. 1 (January 2024): 139-51. https://doi.org/10.31127/tuje.1346353.
EndNote Babashli B, Badalova A, Shukurov R, Ahmadov A (January 1, 2024) Cotton yield estimation using several vegetation indices. Turkish Journal of Engineering 8 1 139–151.
IEEE B. Babashli, A. Badalova, R. Shukurov, and A. Ahmadov, “Cotton yield estimation using several vegetation indices”, TUJE, vol. 8, no. 1, pp. 139–151, 2024, doi: 10.31127/tuje.1346353.
ISNAD Babashli, Bakhtiyar et al. “Cotton Yield Estimation Using Several Vegetation Indices”. Turkish Journal of Engineering 8/1 (January 2024), 139-151. https://doi.org/10.31127/tuje.1346353.
JAMA Babashli B, Badalova A, Shukurov R, Ahmadov A. Cotton yield estimation using several vegetation indices. TUJE. 2024;8:139–151.
MLA Babashli, Bakhtiyar et al. “Cotton Yield Estimation Using Several Vegetation Indices”. Turkish Journal of Engineering, vol. 8, no. 1, 2024, pp. 139-51, doi:10.31127/tuje.1346353.
Vancouver Babashli B, Badalova A, Shukurov R, Ahmadov A. Cotton yield estimation using several vegetation indices. TUJE. 2024;8(1):139-51.
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