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Evaluating the impacts of nitrogen on the growth stages of cucumber | ||
| Iran Agricultural Research | ||
| مقاله 9، دوره 42، شماره 2، بهمن 2023، صفحه 74-83 اصل مقاله (677.69 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22099/iar.2024.50292.1605 | ||
| نویسنده | ||
| behnam sepehr* | ||
| Department of Biosystems Engineering, Bu-ali Sina University, Hamadan, I. R. Iran | ||
| چکیده | ||
| Monitoring physiological parameters of plant and fertilizer requirements are the basic principles in precision farming. Non-destructive and accurate remote sensors make this process feasible and light-handed. The present study evaluates the efficiency of GreenSeeker (GS) and Soil-Plant Analyses Development (SPAD) in Nitrogen (N) fertilizer management. Normalized difference vegetation index (NDVI) was measured with GS and compared to SPAD. Fertigation with 5 different N treatments was applied to 100 pots. The first treatment (N1) had no N concentration, while the other treatments (i.e., N2, N3, N4, and N5) received 2, 9, 22, and 37 mmol.L-1 weekly, respectively. Next, the effect of volumetric fertilizing was investigated by adding supplemental fertilizer to N1 to N3 pots 71 days after planting. Nitrogen concentration in the leaf and first growing fruit was tested using the Kjeldahl method. The results of applied sensors confirmed with visible-near infrared spectroscopy at 200-1100 nm wavelength. NDVI, soil-adjusted vegetation index, and chlorophyll index were calculated from the available spectra and compared to the sensor outputs. Strong correlations were obtained between NDVI and all indices derived from spectra, especially in the vegetative phase. The results showed a strong correlation of NDVI with N rate, especially after supplemental fertilizing. Since the vegetation indices from spectra almost correlated well with NDVI and SPAD in all treatments, spectroscopy monitoring of cucumber could be a precise alternative technique. Linear and nonlinear regressions were applied to model variations of NDVI and SPAD. This study demonstrated the feasibility of using GS for N management according to its sensitivity to cucumber N status. | ||
| کلیدواژهها | ||
| Cucumber؛ GreenSeeker؛ NDVI؛ Nitrogen؛ Spad | ||
| مراجع | ||
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Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19 (6), 716–723. https://doi.org/10.1109/TAC.1974.1100705
Ali, A. H., Thind, S., & Sharma, H. (2014). Prediction of dry direct-seeded rice yields using chlorophyll meter, leaf color chart, and GreenSeeker optical sensor in north-western India. Field Crops Research, 161, 11-15. https://doi.org/10.1016/j.fcr.2014.03.001
Alsina, I., Duma, M., Dubova, L., & Dagis, S. (2016). Comparison of different chlorophylls determination methods for leafy vegetables. Agronomy Research, 14(2), 309-316.
Baresel, J. P., Rischbeck, P., Hu, Y., Kipp, S., Barmeier, G., Mistele, B., & Schmidhalter, U. (2017). Use of a digital camera as an alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat. Computers and Electronics in Agriculture, 140, 25-33. https://doi.org/10.1016/j.compag.2017.05.032
Basyouni, R., Dunn, B., & Goad, C. (2015). Use of non-destructive sensors to assess nitrogen status in potted poinsettia (Euphorbia pulcherrima L. (Wild. ex Klotzsch)) production. Scientia Horticulturae-Amsterdam, 192, 47-53. https://doi.org/10.1016/j.scienta.2015.05.011
Cao, Q., Miao, Y., Feng, G., Gao, X., Li, F., Liu, B., Yue, S., Cheng, S., Ustin, S. L., & Khosla, R. (2015). Active canopy sensing of winter wheat nitrogen status: An evaluation of two sensor systems. Computers and Electronics in Agriculture, 112, 54-67. https://doi.org/10.1016/j.compag.2014.08.012
Gianquinto, G., Orsini, F., Fecondini, M., Mezzetti, M., Sambo, P., & Bona, S. (2011). A methodological approach for defining spectral indices for assessing tomato nitrogen status and yield. European Journal of Agronomy, 35(3), 135-143. https://doi.org/10.1016/j.eja.2011.05.005
Larijani, M., Farokhi-Teymorlou, R. (2012). Evaluation of image processing technique in estimation of nitrogen and plant yield and comparison with conventional methods. Agricultural Machinery, 1(2), 84-91.
Lemaire, G., Jeuffroy, M. H., & Gastal, F. (2008). Diagnosis tool for plant and crop N status in vegetative stage. Theory and practices for crop N management. European Journal of Agronomy, 28(4), 614-624. https://doi.org/10.1016/j.eja.2008.01.005
Li, M., Im, J., & Beier, C. (2013). Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over huntington wildlife forest. GIScience and Remote Sensing, 50(4), 361–384. https://doi.org/10.1080/15481603.2013.819161
Liaqat, M. U., Cheema, M. J. M., Huang, W., Mahmood, T., Zaman, M., & Khan, M. M. (2017). Evaluation of MODIS and Landsat multiband vegetation indices used for wheat yield estimation in irrigated Indus Basin. Computers and Electronics in Agriculture, 138, 39-47. https://doi.org/10.1016/j.compag.2017.04.006
Meng, Q., Cooke, W., & Rodgers, J. (2013). Derivation of 16-day time-series NDVI data for environmental studies using a data assimilation approach. GIScience Remote Sensing, 50(5), 500–514. https://doi.org/10.1080/15481603.2013.823733
Muñoz-Huerta, R. F., Guevara-Gonzalez, R. G., Contreras-Medina, L. M., Torres-Pacheco, I., Prado-Olivarez, J., & Ocampo-Velazquez, R.V. (2013). A review of methods for sensing the nitrogen status in plants. advantages, disadvantages and recent advances. Sensors, 13(8), 10823-43. https://doi.org/10.3390/s130810823
Ozdemir, I. (2014). Linear transformation to minimize the effects of variability in understory to estimate percent tree canopy cover using Rapid Eye data. GIScience and Remote Sensing, 51(3), 288–300. https://doi.org/10.1080/15481603.2014.912876
Padilla, F. M., Peña-Fleitas, M. T., Gallardo, M., & Thompson, R. B. (2014). Evaluation of optical sensor measurements of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop nitrogen status of muskmelon. European Journal of Agronomy, 58, 39-52. https://doi.org/10.1016/j.eja.2014.04.006
Padilla, F. M., Pe˜na-Fleitas, M. T., Gallardo, M., & Thompson, R. B. (2016). Proximal optical sensing of cucumber crop N status using chlorophyll fluorescence indices. European Journal of Agronomy, 73, 83–97. https://doi.org/10.3390/s18072083
Padilla, F. M., Pe˜na-Fleitas, M. T., Gallardo, M., & Thompson, R. B. (2017). Determination of sufficiency values of canopy reflectance vegetation indices for maximum growth and yield of cucumber. European Journal of Agronomy, 84, 1-15. https://doi.org/10.1016/J.EJA.2016.12.007
Schmidt, J., Beegle, D., Zhu, Q., & Sripada, R. (2011). Improving in-season nitrogen recommendations for maize using an active sensor. Field Crops Research, 120, 94-101. https://doi.org/10.1016/j.fcr.2010.09.005
Sharma, L. K., Bu, H., & Franzen, D. W. (2014). Comparison of two ground-based active-optical sensors for in-season estimation of corn (Zea mays, L.) yield. Journal of Plant Nutrition, 39(7), 957-966. https://doi.org/10.1080/01904167.2015.1109109
Sharma, L. K., Bu, H., Franzen, D. W., & Denton, A. (2016). Use of corn height measured with an acoustic sensor improves yield estimation with ground-based active optical sensors. Computers and Electronics in Agriculture, 124, 254-262. https://doi.org/10.1016/j.compag.2016.04.016
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
Wang, L., Zhou, X., Zhu, X., & Guo, D.W. (2017). Estimated of leaf nitrogen concentration in wheat using MK-SVR algorithm and satellite remote sensing data. Computers and Electronics in Agriculture, 140, 327-337. https://doi.org/10.1016/j.compag.2017.05.023
Wu, J., Wang, D., Rosen, C. J., & Bauer, M. E. (2007). Comparison of petiole nitrate concentrations, SPAD chlorophyll readings, and QuickBird satellite imagery in detecting nitrogen status of potato canopies. Field Crops Research, 101, 96–103. https://doi.org/10.1016/j.fcr.2006.09.014
Yang, C. M., Liu, C. C., & Wang, Y. W. (2008). Using Formosat-2 satellite data to estimate leaf area index of rice crop. Journal of Photogrammetry and Remote Sensing, 13(4), 253–260. https://doi.org/10.6574/JPRS.2008.13(4).3
Yuan, F., Wang, C., & Mitchell, M. (2014). Spatial patterns of land surface phenology relative to monthly climate variations: US Great Plains. GIScience and Remote Sensing, 51(1), 30–50. https://doi.org/10.1080/15481603.2014.883210 | ||
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